Interpretable Spiculation Quantification for Lung Cancer Screening
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Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
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![Interpretable Nodule Spiculation Quantification
for Lung Cancer Screening
Wookjin Choi*, Saad Nadeem*, Sadegh Riyahi, Joseph O Deasy, Allen Tannenbaum, and Wei Lu
Department of Medical Physics
* The first two authors contributed equally to this work.
Purpose
•Lung cancer is leading cause of cancer death,
but early detection can reduce the mortality.
•The Lung Imaging Reporting and Data System
(Lung-RADS) developed by the American College
of Radiology
•Lung cancer screening using radiomics has
also been studied and achieved moderate
accuracy (77.5% – 84.6%).
•Nodule Spiculation for Lung Cancer
Screening with CT
–Important suspicious feature: Lung-RADS
–Qualitatively read by radiologists as
absent/spiculated, or spiculation score 1-5
Angle-Preserving Spherical Conformal Mapping
First eigenfunction of the Laplace-
Beltrami operator
Conformal mapping from surface
𝑆 to unit sphere 𝒮2: 𝜙: 𝑆 → 𝒮2
Nadeem, et al. 2017. IEEE Trans Vis Comput Graph 23: 1663-1676.
Area distortion
𝜖𝑖: = log
𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣 𝑘)]
𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣 𝑘]
1. Detect spicule base at 𝜖𝑖=0 (green-yellow)
2. Recursively traverse closed curves toward the negative
distortion values
3. Can break and move towards different apex
4. Detect spicule apex at max. negative 𝜖𝑖 (red x)
Spiculation Scores
𝑠1 =
𝑖 mean 𝜖 𝑝 𝑖 ∗ ℎ 𝑝 𝑖
𝑖 ℎ 𝑝 𝑖
Sensitivity Specificity Accuracy AUC
Cross validation on 72 cases
Shape+Texture 87.2% 81.2% 84.6% 0.89
Shape+Texture+Radiologist’s score (𝑠𝑟) 87.8% 87.1% 87.5% 0.91
Shape+Texture+Our measures (𝑁𝑝 + 𝑠1) 92.7% 83.9% 88.9% 0.92
External validation on 811 cases
Shape+Texture 63.2% 82.1% 79.4% 0.79
Shape+Texture+Radiologist’s score (𝑠𝑟) 73.7% 64.1% 65.5% 0.77
Shape+Texture+Our measures (𝑁𝑝 + 𝑠1) 73.7% 80.9% 79.9% 0.82
Dataset Sensitivity Specificity Accuracy AUC
Hawkins et al.
(2016)
Baseline CT scans of 261 pati
ents in NLST
51.7% 92.9% 80.0% 0.83
Buty et al.
(2016)
LIDC 2054 PNs
Ground-truth by radiologists assessment
82.4%
Kumar et al.
(2015)
LIDC 97 patients
Including metastatic tumors
79.1% 76.1% 77.5%
Liu et al.
(2016)
172 patients
Two independent cohorts 102 and 70 patients
71.4% 83.7% 80.0% 0.81
Choi et al.
(2018)
LIDC 72 patients 87.2% 81.2% 84.6% 0.89
Proposed
Method
LIDC 72 patients 86.6% 84.5% 88.9% 0.92
•Can not distinguish whether a spicule is tumor
invasion or is normal anatomy (vessel)
•Not differentiate spiculation and lobulation
•Not consider size of nodule
•Manual nodule segmentations, some spicules not
segmented completely
•Novel interpretable quantification of pulmonary
nodule spiculation
•Improved malignancy prediction accuracy
•Niehaus et al. shape features
–Roughness of boundary and compactness
–Dependent on size of nodule (AUC 65% to 91%)
•Dhara et al. differential geometry
–Spicule identified as voxels with higher GD/VD2,
GD = geodesic distance, VD = voxel density
–Spiculation score: spicule height and angle
Dhara, et al. 2016. Int J Comput Assit Radiol Surg 11: 337-349.
Niehaus, et al. 2015. J Digit Imaging 28: 704-717
• Public dataset: Lung Image Database
Consortium image collection (LIDC-IDRI)
• Four radiologists performed nodule contouring
and image annotation
• Spiculation score ranged between 1 (non-
spiculated) and 5 (highly spiculated)
Binary mask of
segmented nodule
Triangular mesh
representation
Angle-preserving
spherical
conformal mapping
Compute area
distortion 𝜖
Detect spiculation Quantify spiculation
Conclusion
Limitations
Method
Spiculation Detection
Results
Quantify Spiculation with Spherical
Conformal Mapping and Area Distortion
Metric
Patient Study
Compare against Mean Curvature Method
Compare against Radiologists Spiculation Score
Synthetic Spicules Detection
5-mm Spicules 2-mm Spicules
• All 5-mm spicules were detected, isolated or
merged
• 3 of four 2-mm isolated spicules and 1 of four 2-
mm merged spicules were detected
Malignancy Prediction by SVM-LASSO Radiomics Model
Choi, et al. 2018. Med Phys 45: 1537-1549.
Comparison with other Radiomics Studies
Spiculation Quantification
•Area distortion better than solid angle for non-conic spicules
•Height can be medial axis length or perpendicular distance
•Width can be measured at base or at half max. (FWHM)](https://image.slidesharecdn.com/spiculationposter-180928033644/85/Interpretable-Spiculation-Quantification-for-Lung-Cancer-Screening-1-320.jpg)
Recommended
Interpretable Spiculation Quantification for Lung Cancer Screening
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Nec...
(Sunday, 7/14/2019) 4:00 PM - 5:00 PM
Room: 225BCD
Purpose: To simulate realistic manual delineation (MD) organ-at-risk (OAR) delineation variability (DV) the purpose of quantifying DV’s dosimetric impact.
Methods: Fourteen independent MD head-and-neck OAR structure sets (SS) were obtained from the ESTRO Falcon group. Seven OARs were available (BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid). A consensus MD SS was generated by the simultaneous truth and performance level estimation (STAPLE) method. MD DV was evaluated with respect to the STAPLE SS using the Dice coefficient and Hausdorff distance (HD) geometric similarity metrics. DVs were simulated using auto-delineation (AD)
methods: an average surface of standard deviation (ASSD) method, GrowCut segmentation, and a random walker (RW) segmentation. Each OAR AD was repeated five times with a different seed or variability level. Dice and HD were computed for each OAR AD with respect to the STAPLE SS. Dosimetric analysis was achieved by intercomparing dose-volume histograms (DVH) from a plan developed with a reference MD SS with DVHs for each MD and AD. DVH confidence bands are reported for MD and each AD method.
Results: The MD Dice was 0.7±0.2 (μ±σ). AD Dice values (ASSD, GrowCut, and RW) were 0.5±0.2, 0.7±0.2, and 0.8±0.1, respectively. HDs were 35.4±45.2, 27.3±19.1, 29.3±19.9, and 14.6±10.3. The simulated DV increased with increasing the seed standard deviations or variability level. The dosimetric effect was largest for MD DVs (larger OAR DVH confidence intervals and larger HD), even though the MD Dice was greater than the ASSD and GrowCut Dice values. GrowCut DV resulted in less dosimetric variation than RW, unlike the geometric indices.
Conclusion: We developed a framework to simulate DVs and demonstrated its feasibility. ADs were able to simulate different magnitudes of DVs, but did not replicate the dosimetric consequences of human delineation variability. The correlation between geometric similarity metrics and dosimetric consequences of DV is poor.
Assessing the Dosimetric Links between Organ-At-Risk Delineation Variability ...
Purpose: To determine the relative dosimetric impact of delineation variability (DV) when inter-observer and inter-technique planning variability (PV), and setup variability (SV) with are considered.
Methods: 409 plans for a single head-and-neck patient from the 2017 Radiation Knowledge plan competition were used. Plans were created with Eclipse (N=227), Pinnacle (N=49), RayStation (N=25), Monaco (N=75), and TomoTherapy (N=33) with delivery techniques conventional linac IMRT (N=142), volumetric modulated arc therapy (VMAT, N=234), and helical TomoTherapy (N=33). All plans were optimized using a consistent set of target volumes and a single OAR structure set. Four additional OAR structure sets were contoured by radiation oncologists (N=2) and medical physics residents (N=2) who had completed head-and-neck contouring training. Probabilistic DVHs, dose-volume coverage maps (DVCM), which shows the probability of achieving a dose metric, were computed for each OAR on the following scenarios: SV alone (N=1000), SV+PV (N=1000*409), SV+DV (N=1000*5), SV+PV+DV (total variability [TV], N=1000*409*5). Analysis focused on the probability of exceeding the maximum dose constraint exceeded 5% for each OAR.
Results: The primary source of variability was PV, which was expected due to inter-observer planning abilities and preferences during the optimization planning process, even when all participants utilized the same constraints. The parotid had the most significant interquartile range (IQR) on the PV scenario. Conversely, adding SV, DV, and TV each reduced the IQR, showing a washing out effect on the DVCM.
Conclusion: Assessment of OAR sensitivity to DV will be highly sensitive to the specific planning technique and planner, likely requiring plan-specific assessment of in-tolerance delineation variations. Incorporation SV and DV variabilities in plan assessments washes out their relative impacts on maximum dose.
Artificial Intelligence in Radiation Oncology
- The document discusses the use of artificial intelligence and radiomics in radiation oncology. It presents frameworks for radiomics analysis involving image registration, tumor segmentation, feature extraction, and predictive modeling.
- Specific applications discussed include using radiomics for lung cancer screening and prediction of tumor response. Radiomics features combined with machine learning models show improved performance over clinical guidelines for assessing lung nodule malignancy.
- Methods are also presented for quantifying tumor characteristics like spiculation through image analysis and extracting interpretable radiomics features. This can provide semantic information to radiologists for assessment.
Quantitative image analysis for cancer diagnosis and radiation therapy
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Quantitative Cancer Image Analysis
Delineation Variability
- Human Delineation Variability Quantification
- Realistic Variability Simulation
- Probabilistic U-Net
Radiomics
- Lung Cancer Screening
- Spiculation Quantification
- Aggressive Lung ADC subtype prediction
Radiomics in Lung Cancer
Radiomics
1. Lung Cancer Screening
2. Spiculation Quantification
3. Aggressive Lung ADC Subtype
Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Recommended
Interpretable Spiculation Quantification for Lung Cancer Screening
Spiculations are spikes on the surface of pulmonary nodule and are important predictors of malignancy in lung cancer. In this work, we introduced an interpretable, parameter-free technique for quantifying this critical feature using the area distortion metric from the spherical conformal (angle-preserving) parameterization. The conformal factor in the spherical mapping formulation provides a direct measure of spiculation which can be used to detect spikes and compute spike heights for geometrically-complex spiculations. The use of the area distortion metric from conformal mapping has never been exploited before in this context. Based on the area distortion metric and the spiculation height, we introduced a novel spiculation score. A combination of our spiculation measures was found to be highly correlated (Spearman's rank correlation coefficient ρ = 0.48) with the radiologist's spiculation score. These measures were also used in the radiomics framework to achieve state-of-the-art malignancy prediction accuracy of 88.9% on a publicly available dataset.
Simulation of Realistic Organ-At-Risk Delineation Variability in Head and Nec...
(Sunday, 7/14/2019) 4:00 PM - 5:00 PM
Room: 225BCD
Purpose: To simulate realistic manual delineation (MD) organ-at-risk (OAR) delineation variability (DV) the purpose of quantifying DV’s dosimetric impact.
Methods: Fourteen independent MD head-and-neck OAR structure sets (SS) were obtained from the ESTRO Falcon group. Seven OARs were available (BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid). A consensus MD SS was generated by the simultaneous truth and performance level estimation (STAPLE) method. MD DV was evaluated with respect to the STAPLE SS using the Dice coefficient and Hausdorff distance (HD) geometric similarity metrics. DVs were simulated using auto-delineation (AD)
methods: an average surface of standard deviation (ASSD) method, GrowCut segmentation, and a random walker (RW) segmentation. Each OAR AD was repeated five times with a different seed or variability level. Dice and HD were computed for each OAR AD with respect to the STAPLE SS. Dosimetric analysis was achieved by intercomparing dose-volume histograms (DVH) from a plan developed with a reference MD SS with DVHs for each MD and AD. DVH confidence bands are reported for MD and each AD method.
Results: The MD Dice was 0.7±0.2 (μ±σ). AD Dice values (ASSD, GrowCut, and RW) were 0.5±0.2, 0.7±0.2, and 0.8±0.1, respectively. HDs were 35.4±45.2, 27.3±19.1, 29.3±19.9, and 14.6±10.3. The simulated DV increased with increasing the seed standard deviations or variability level. The dosimetric effect was largest for MD DVs (larger OAR DVH confidence intervals and larger HD), even though the MD Dice was greater than the ASSD and GrowCut Dice values. GrowCut DV resulted in less dosimetric variation than RW, unlike the geometric indices.
Conclusion: We developed a framework to simulate DVs and demonstrated its feasibility. ADs were able to simulate different magnitudes of DVs, but did not replicate the dosimetric consequences of human delineation variability. The correlation between geometric similarity metrics and dosimetric consequences of DV is poor.
Assessing the Dosimetric Links between Organ-At-Risk Delineation Variability ...
Purpose: To determine the relative dosimetric impact of delineation variability (DV) when inter-observer and inter-technique planning variability (PV), and setup variability (SV) with are considered.
Methods: 409 plans for a single head-and-neck patient from the 2017 Radiation Knowledge plan competition were used. Plans were created with Eclipse (N=227), Pinnacle (N=49), RayStation (N=25), Monaco (N=75), and TomoTherapy (N=33) with delivery techniques conventional linac IMRT (N=142), volumetric modulated arc therapy (VMAT, N=234), and helical TomoTherapy (N=33). All plans were optimized using a consistent set of target volumes and a single OAR structure set. Four additional OAR structure sets were contoured by radiation oncologists (N=2) and medical physics residents (N=2) who had completed head-and-neck contouring training. Probabilistic DVHs, dose-volume coverage maps (DVCM), which shows the probability of achieving a dose metric, were computed for each OAR on the following scenarios: SV alone (N=1000), SV+PV (N=1000*409), SV+DV (N=1000*5), SV+PV+DV (total variability [TV], N=1000*409*5). Analysis focused on the probability of exceeding the maximum dose constraint exceeded 5% for each OAR.
Results: The primary source of variability was PV, which was expected due to inter-observer planning abilities and preferences during the optimization planning process, even when all participants utilized the same constraints. The parotid had the most significant interquartile range (IQR) on the PV scenario. Conversely, adding SV, DV, and TV each reduced the IQR, showing a washing out effect on the DVCM.
Conclusion: Assessment of OAR sensitivity to DV will be highly sensitive to the specific planning technique and planner, likely requiring plan-specific assessment of in-tolerance delineation variations. Incorporation SV and DV variabilities in plan assessments washes out their relative impacts on maximum dose.
Artificial Intelligence in Radiation Oncology
- The document discusses the use of artificial intelligence and radiomics in radiation oncology. It presents frameworks for radiomics analysis involving image registration, tumor segmentation, feature extraction, and predictive modeling.
- Specific applications discussed include using radiomics for lung cancer screening and prediction of tumor response. Radiomics features combined with machine learning models show improved performance over clinical guidelines for assessing lung nodule malignancy.
- Methods are also presented for quantifying tumor characteristics like spiculation through image analysis and extracting interpretable radiomics features. This can provide semantic information to radiologists for assessment.
Quantitative image analysis for cancer diagnosis and radiation therapy
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
Quantitative Cancer Image Analysis
Delineation Variability
- Human Delineation Variability Quantification
- Realistic Variability Simulation
- Probabilistic U-Net
Radiomics
- Lung Cancer Screening
- Spiculation Quantification
- Aggressive Lung ADC subtype prediction
Radiomics in Lung Cancer
Radiomics
1. Lung Cancer Screening
2. Spiculation Quantification
3. Aggressive Lung ADC Subtype
Quantitative Image Analysis for Cancer Diagnosis and Radiation Therapy
1.Lung Cancer Screening
1.1.Deep learning (feasible but not interpretable)
1.2.Radiomics (concise model)
1.3.Spiculation quantification (interpretable feature)
2.PET/CT Tumor Response
2.1.Aggressive Lung ADC subtype prediction (helpful for surgeons)
2.2.Pathologic response prediction (accurate but not concise)
2.3.Local tumor morphological changes (accurate and interpretable)
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Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Abstract
Purpose/Objective(s)
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
Materials/Methods
The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres (diameters 10 to 60 mm) placed in the lung contralateral to the tumor. Twenty-seven texture features (9 from intensity histogram, 8 from the gray-level co-occurrence matrix [GLCM], and 10 from the gray-level run-length matrix [GLRM]) were extracted from [lung – GTV]. The Bland-Altman method was applied to measure the normalized range of agreement (nRoA) of each texture feature when GTV size varied. A feature was considered as robust when its nRoA was less than that of [lung – GTV] volume (8.8%) and regarded as not correlated when their absolute correlation coefficient was lower than 0.70.
Results
Eighteen texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except energy and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were nonrobust. Particularly, all three low gray-level emphasis features had extremely large nRoAs (∼30%), indicating huge variations when GTV size changed. None of the robust features was correlated with the normal lung [lung – GTV] volume, suggesting that they can provide additional information. Three nonrobust features (sum and two nonuniformity features) were highly correlated with the normal lung volume. None feature showed statistically significant differences (P < 0.05) with respect to GTV location (upper vs. lower lobe).
Conclusion
We identified 18 robust lung CT texture features which were invariant to varying tumor volumes. Particularly the three GLRM high gray-level emphasis features can characterize the radiologic manifestations of pulmonary abnormalities. Hence these features can be further examined for the prediction of the RILD.
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Presentation clinical applications of Artificial Intelligence for radiation o...
Presentation clinical applications of Artificial Intelligence for radiation o...Jean-Emmanuel Bibault Bibault, MD, PhD
Presentation for the European Society for Therapeutic Radiation Oncology annual meeting of 2019 - ESTRO38Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA).
http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4958261
Current Projects - Wookjin Choi
Radiomics for lung cancer screening
Radiomics for Hepatocellular Carcinoma (HCC) Radiation Therapy
Robust lung texture features
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Abstract
Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they should be robust (relatively invariant or unbiased) to tumor size variations and not correlated (non-redundant) with the normal lung volume of interest, i.e., volume of the peri-tumoral region.
Methods: CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated with spheres (diameters 10 to 60 mm) and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the peri-tumoral region (uniform 30 mm expansion around the GTV in the lung). The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%), which was chosen at the nRoA of the volume of the peri-tumoral region with modification based on the cumulative graph of features vs. nRoA. A feature was regarded as not correlated with the volume of the peri-tumoral region when their correlation was lower than 0.70.
Results: 16 of the 27 texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except cluster shade, cluster prominence, and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were unrobost. None of the robust features was correlated with the volume of the peri-tumoral region. No feature showed statistically significant differences (P<0.05) on GTV location (upper vs. lower lobe).
Conclusion: We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD. Particularly, GLRM high gray-level emphasis features were robust and characterized the radiologic manifestations of pulmonary abnormalities.
4DCT driving improved lung radiotherapy outcomes at NCCI
4D CT scanning provides improved target delineation and treatment alignment for lung radiotherapy by accounting for tumor motion throughout the respiratory cycle. Analysis of 4D CT scans from 20 patients at NCCI found that accounting for tumor motion increased planning target volumes by an average of 9.1-42.5% depending on tumor size. Studies have also shown 4D CT can reduce needed planning margins from 2mm down when using bony anatomy for alignment by better visualizing tumor position. NCCI now uses 4D CT along with IMRT and daily CBCT to precisely target tumors and minimize dose to healthy tissues, with the goal of reducing radiation pneumonitis complications.
computer aided detection of pulmonary nodules in ct scans
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Atlas drenaje ganglionar de Martínez - Monge
This document describes a cross-sectional nodal atlas intended to assist radiation oncologists in defining clinical target volumes in 3D radiation therapy planning. It outlines the lymphatic drainage patterns and locations of nodal stations on CT images of the head and neck, thorax, abdomen, pelvis. Key nodal stations are labeled on consecutive equidistant CT images with the goal of correlating these stations to their known spatial references to help delineate target volumes in 3D planning programs.
Radiation protectionandqualityassessmentinxri fin
The document discusses patient safety and image quality in x-ray imaging. It notes that ionizing radiation carries risks like carcinogenesis and outlines radiation doses from common medical imaging procedures. Maintaining adequate image quality while avoiding unnecessary radiation exposure requires justification of exams, optimization of protocols, and limiting patient doses. Key principles of radiation protection aim to balance image quality needs with radiation risks.
Nephrometry
This document discusses several tumor nephrometry scoring systems used to assess renal cell carcinoma complexity and predict surgical outcomes of partial nephrectomy. It describes the RENAL, PADUA, Centrality Index (C-Index), and DAP scoring systems, including their components, development, validation studies, limitations, and comparisons. The goal of these systems is to standardize reporting on tumor characteristics, surgical complexity, and allow for better patient counseling and comparisons between studies. Later systems like DAP aimed to improve on earlier ones by integrating and optimizing their individual strengths.
SBRT versus Surgery in Early lung cancer : Debate
This document discusses stereotactic body radiation therapy (SBRT) versus surgery for early stage non-small cell lung cancer (NSCLC). SBRT delivers a high dose of precision radiation to the tumor target in 1-5 fractions. Several studies have shown comparable survival and recurrence rates between lobectomy and sublobar resection for stage I lung cancer. SBRT has comparable or better local tumor control and survival rates than conventional radiation therapy for early stage NSCLC, with fewer side effects. While surgery may remain the standard of care, SBRT has emerged as a viable alternative to surgery for medically inoperable early stage NSCLC patients, with some studies investigating its use in operable patients as well.
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Purpose/Objectives: To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic adenocarcinoma (PDA).
Materials/Methods: Ten PDA patients were enrolled and underwent three CT scans: a 4D-CT immediately following a CE 3D-CT, and an individually optimized CE 4D-CT using a test injection to estimate the peak contrast enhancement time and to optimize the delay time. Three physicians contoured the tumor and pancreatic tissues. We compared image quality scores, tumor volume, motion, image noise, tumor-to-pancreas contrast, and contrast-to- noise ratio (CNR) in the three CTs. We also evaluated inter-observer variations in contouring the tumor using simultaneous truth and performance level estimation (STAPLE).
Results: The average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, p=0.47), and both were significantly better than that for 4D-CT (2.6, p<0.001). The tumor-to- pancreas contrast in CE 3D-CT and CE 4D-CT were comparable (15.5 and 16.7 HU, p=0.71), and the later was significantly higher than that in 4D-CT (9.2 HU, p=0.03). Image noise in CE 3D-CT (12.5 HU) was significantly lower than that in CE 4D-CT (22.1 HU, p<0.001) and 4D-CT (19.4 HU, p=0.005). The CNR in CE 3D-CT and CE 4D-CT were comparable (1.4 and 0.8, p=0.23), and the former was significantly better than that in 4D-CT (0.6, p=0.04). The average tumor volume was smaller in CE 3D-CT (29.8 cm 3 ) and CE 4D-CT (22.8 cm 3 ) than in 4D-CT (42.0 cm 3 ), though the differences were not statistically significant. The tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, p=0.23). The inter-observer variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%), and the former was significantly smaller than that of 4D-CT (55.6%, p=0.047).
Conclusions: The CE 4D-CT demonstrated largely comparable characteristics to the CE 3D-CT. It has high potential for simultaneously delineating the tumor and quantifying the tumor motion with a single scan.
Optimal fuzzy rule based pulmonary nodule detection
The document describes a lung cancer detection system that uses CT scans. It discusses (1) segmenting the lungs from CT images using adaptive thresholding and connected component analysis, (2) detecting nodule candidate regions using multi-thresholding and rule-based pruning, and (3) optimizing the rule-based pruning using a genetic algorithm trained fuzzy inference system to reduce false positives while maintaining high sensitivity. Experimental results on a publicly available lung image database show the optimized fuzzy system achieved better performance than a conventional rule-based approach.
Computer aided detection of pulmonary nodules using genetic programming
This document describes a method for detecting pulmonary nodules in CT scans using genetic programming. It first segments the lung regions from CT images and extracts nodule candidates. Features are then extracted from the candidates. Genetic programming is used to classify candidates as nodules or non-nodules by optimizing combinations of features. The method was tested on a publicly available lung image database, achieving a true positive rate of over 90% and low false positive rate.
PROSTATE CANCER IN NUTSHELL
This document discusses the radiotherapeutic management of prostate cancer. It begins by introducing the multidisciplinary team involved in diagnosis and treatment. It then covers diagnosis, including various screening tests and pathology findings. Treatment options discussed include observation, surgery, radiation therapy and hormonal treatment. The document focuses on the evolution of radiation techniques from conventional to IMRT and brachytherapy. It compares outcomes and side effects of different local treatment modalities and discusses the role of neoadjuvant hormonal therapy.
Sciahbasi A - AIMRADIAL 2013 - Radiation exposure
Sciahbasi A - AIMRADIAL 2013 - Radiation exposureInternational Chair on Interventional Cardiology and Transradial Approach
Vascular access route (radial vs femoral) and radiation exposure in percutaneous coronary interventions and diagnostic angiographyMore Related Content
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Artificial Intelligence in Radiation Oncology
The document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines topics including radiomics decision support tools, automatic delineation and variability analysis, and applications like lung cancer screening, tumor response prediction, and aggressive lung adenocarcinoma subtype prediction. Radiomics frameworks and deep learning models are presented. Results show potential for AI to provide quantitative imaging biomarkers and improve outcomes in areas like screening, treatment planning, and response assessment.
Artificial Intelligence in Radiation Oncology
This document discusses artificial intelligence applications in radiation oncology. It begins with acknowledgements and then outlines several AI applications including radiomics tools for lung cancer screening, tumor response prediction, and predicting aggressive lung adenocarcinoma subtypes. It also discusses using AI for automatic tumor delineation and quantification of delineation variability as well as local tumor morphological changes prediction and metabolic tumor volume changes. The document provides details on methods and results for several of these AI applications in radiation oncology.
Future Rt Cco (0sullivan)
1) The document discusses optimal practice in radiation treatment for head and neck cancer in the 21st century, focusing on balancing treatment targets and sparing normal tissues using available technology and expertise.
2) It reviews treatment options and approaches for different stages of head and neck cancer, highlighting evidence that altered fractionation and chemoradiation can improve outcomes over standard radiation alone.
3) Challenges of implementing intensity-modulated radiation therapy (IMRT) for head and neck cancer are discussed, as well as examples of how IMRT can improve target coverage and tissue sparing compared to conventional techniques.
Radiomics and Deep Learning for Lung Cancer Screening
The document summarizes research on using radiomics and deep learning approaches for lung cancer screening. It describes:
1) Using radiomic features like shape, texture, and intensity from lung nodules on CT scans and an SVM-LASSO model to classify nodules with 87.9% sensitivity and 78.2% specificity, outperforming the Lung-RADS system.
2) A deep learning model developed for a Kaggle competition that achieved 67.4% accuracy on nodule classification but only ranked 99th due to overfitting issues without enough data.
3) Future work could integrate quantification of nodule characteristics like spiculation with plasma biomarkers to improve diagnostic accuracy.
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Abstract
Purpose/Objective(s)
Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
Materials/Methods
The free-breathing CTs of 14 lung SBRT patients were studied. Different sizes of GTVs were simulated with spheres (diameters 10 to 60 mm) placed in the lung contralateral to the tumor. Twenty-seven texture features (9 from intensity histogram, 8 from the gray-level co-occurrence matrix [GLCM], and 10 from the gray-level run-length matrix [GLRM]) were extracted from [lung – GTV]. The Bland-Altman method was applied to measure the normalized range of agreement (nRoA) of each texture feature when GTV size varied. A feature was considered as robust when its nRoA was less than that of [lung – GTV] volume (8.8%) and regarded as not correlated when their absolute correlation coefficient was lower than 0.70.
Results
Eighteen texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except energy and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were nonrobust. Particularly, all three low gray-level emphasis features had extremely large nRoAs (∼30%), indicating huge variations when GTV size changed. None of the robust features was correlated with the normal lung [lung – GTV] volume, suggesting that they can provide additional information. Three nonrobust features (sum and two nonuniformity features) were highly correlated with the normal lung volume. None feature showed statistically significant differences (P < 0.05) with respect to GTV location (upper vs. lower lobe).
Conclusion
We identified 18 robust lung CT texture features which were invariant to varying tumor volumes. Particularly the three GLRM high gray-level emphasis features can characterize the radiologic manifestations of pulmonary abnormalities. Hence these features can be further examined for the prediction of the RILD.
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Presentation clinical applications of Artificial Intelligence for radiation o...
Presentation clinical applications of Artificial Intelligence for radiation o...Jean-Emmanuel Bibault Bibault, MD, PhD
Presentation for the European Society for Therapeutic Radiation Oncology annual meeting of 2019 - ESTRO38Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Purpose: To predict the histopathologic subtypes with poor surgery prognosis in early stage lung adenocarcinomas using CT and PET radiomics.
Methods: We retrospectively enrolled 53 patients with stage I lung adenocarcinoma who underwent both diagnostic CT and 18F-fluorodeoxyglucose (FDG) PET/CT before complete surgical resection of the tumors. Tumor segmentation was manually contoured by a physician on both the diagnostic CT and the attenuation CT of PET/CT.A total of 170 radiomics features were extracted on both PET and CT images to design predictive models for two histopathologic endpoints: (1) tumors with solid or micropapillary predominant subtype (aggressiveness), and (2) tumors with micropapillary component more than 5% (MIP5). We used least absolute shrinkage and selection operator (LASSO) as a model building method coupled with a class separability feature selection (CSFS) method. For an unbiased model estimate, a 10-fold cross validation approach was used. The area under the curve (AUC) and prediction accuracy were employed to evaluate the performance of the model. P-values were computed using Wilcoxon rank-sum test.
Results: Of the 53 patients, 9 and 15 had tumors with aggressiveness and MIP5, respectively. For both endpoints, LASSO models with two PET radiomics features achieved the best performance. For aggressiveness, the LASSO model with PET Cluster Shade and PET 2D Variance resulted in 77.6±2.3% accuracy and 0.71±0.02 AUC (P = 0.011). For MIP5, the LASSO model with PET Eccentricity and PET Cluster Shade resulted in 69.6±3.1% accuracy and 0.68±0.04 AUC (P=0.014). The PET Cluster Shade was commonly selected in both models. Cluster shade is a texture feature that measures the skewness of the co-occurrence matrix. Higher PET cluster shade predicted that the tumor was more aggressive and more likely MIP5.
Conclusion: We showed that PET/CT radiomics features can predict tumor aggressiveness.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by the National Cancer Institute Grants R01CA172638.
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic ductal adenocarcinomas (PDA).
http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4958261
Current Projects - Wookjin Choi
Radiomics for lung cancer screening
Radiomics for Hepatocellular Carcinoma (HCC) Radiation Therapy
Robust lung texture features
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Abstract
Purpose: Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (RILD) - pneumonitis and fibrosis. For these features to be clinically useful, they should be robust (relatively invariant or unbiased) to tumor size variations and not correlated (non-redundant) with the normal lung volume of interest, i.e., volume of the peri-tumoral region.
Methods: CT images of 14 lung cancer patients were studied. Different sizes of gross tumor volumes (GTVs) were simulated with spheres (diameters 10 to 60 mm) and placed in the lung contralateral to the tumor. 27 texture features [nine from intensity histogram, eight from the gray-level co-occurrence matrix (GLCM) and ten from the gray-level run-length matrix (GLRM)] were extracted from the peri-tumoral region (uniform 30 mm expansion around the GTV in the lung). The Bland-Altman analysis was applied to measure the normalized range of agreement (nRoA) for each feature when GTV size varied. A feature was considered as robust when its nRoA was less than the threshold (100%), which was chosen at the nRoA of the volume of the peri-tumoral region with modification based on the cumulative graph of features vs. nRoA. A feature was regarded as not correlated with the volume of the peri-tumoral region when their correlation was lower than 0.70.
Results: 16 of the 27 texture features were identified as robust. All intensity histogram features were robust except sum and kurtosis. All GLCM features were robust except cluster shade, cluster prominence, and Haralick's Correlation. Five GLRM features (two run emphasis and three high gray-level emphasis) were robust while the other five (two nonuniformity and three low gray-level emphasis) were unrobost. None of the robust features was correlated with the volume of the peri-tumoral region. No feature showed statistically significant differences (P<0.05) on GTV location (upper vs. lower lobe).
Conclusion: We identified 16 robust normal lung CT texture features that can be further examined for the prediction of RILD. Particularly, GLRM high gray-level emphasis features were robust and characterized the radiologic manifestations of pulmonary abnormalities.
4DCT driving improved lung radiotherapy outcomes at NCCI
4D CT scanning provides improved target delineation and treatment alignment for lung radiotherapy by accounting for tumor motion throughout the respiratory cycle. Analysis of 4D CT scans from 20 patients at NCCI found that accounting for tumor motion increased planning target volumes by an average of 9.1-42.5% depending on tumor size. Studies have also shown 4D CT can reduce needed planning margins from 2mm down when using bony anatomy for alignment by better visualizing tumor position. NCCI now uses 4D CT along with IMRT and daily CBCT to precisely target tumors and minimize dose to healthy tissues, with the goal of reducing radiation pneumonitis complications.
computer aided detection of pulmonary nodules in ct scans
The document discusses computer aided detection of pulmonary nodules in CT scans. It introduces lung cancer as a major health problem and describes how detecting nodules early can improve survival rates. It then provides an overview of pulmonary nodule detection CAD systems, describing their general structure and evaluating various approaches in the literature. Key contributions are genetic programming and shape-based classifiers and a hierarchical block analysis method that achieved high performance on a publicly available lung image database.
Atlas drenaje ganglionar de Martínez - Monge
This document describes a cross-sectional nodal atlas intended to assist radiation oncologists in defining clinical target volumes in 3D radiation therapy planning. It outlines the lymphatic drainage patterns and locations of nodal stations on CT images of the head and neck, thorax, abdomen, pelvis. Key nodal stations are labeled on consecutive equidistant CT images with the goal of correlating these stations to their known spatial references to help delineate target volumes in 3D planning programs.
Radiation protectionandqualityassessmentinxri fin
The document discusses patient safety and image quality in x-ray imaging. It notes that ionizing radiation carries risks like carcinogenesis and outlines radiation doses from common medical imaging procedures. Maintaining adequate image quality while avoiding unnecessary radiation exposure requires justification of exams, optimization of protocols, and limiting patient doses. Key principles of radiation protection aim to balance image quality needs with radiation risks.
Nephrometry
This document discusses several tumor nephrometry scoring systems used to assess renal cell carcinoma complexity and predict surgical outcomes of partial nephrectomy. It describes the RENAL, PADUA, Centrality Index (C-Index), and DAP scoring systems, including their components, development, validation studies, limitations, and comparisons. The goal of these systems is to standardize reporting on tumor characteristics, surgical complexity, and allow for better patient counseling and comparisons between studies. Later systems like DAP aimed to improve on earlier ones by integrating and optimizing their individual strengths.
SBRT versus Surgery in Early lung cancer : Debate
This document discusses stereotactic body radiation therapy (SBRT) versus surgery for early stage non-small cell lung cancer (NSCLC). SBRT delivers a high dose of precision radiation to the tumor target in 1-5 fractions. Several studies have shown comparable survival and recurrence rates between lobectomy and sublobar resection for stage I lung cancer. SBRT has comparable or better local tumor control and survival rates than conventional radiation therapy for early stage NSCLC, with fewer side effects. While surgery may remain the standard of care, SBRT has emerged as a viable alternative to surgery for medically inoperable early stage NSCLC patients, with some studies investigating its use in operable patients as well.
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Purpose/Objectives: To develop an individually optimized contrast-enhanced (CE) 4D-CT for radiotherapy simulation in pancreatic adenocarcinoma (PDA).
Materials/Methods: Ten PDA patients were enrolled and underwent three CT scans: a 4D-CT immediately following a CE 3D-CT, and an individually optimized CE 4D-CT using a test injection to estimate the peak contrast enhancement time and to optimize the delay time. Three physicians contoured the tumor and pancreatic tissues. We compared image quality scores, tumor volume, motion, image noise, tumor-to-pancreas contrast, and contrast-to- noise ratio (CNR) in the three CTs. We also evaluated inter-observer variations in contouring the tumor using simultaneous truth and performance level estimation (STAPLE).
Results: The average image quality scores for CE 3D-CT and CE 4D-CT were comparable (4.0 and 3.8, p=0.47), and both were significantly better than that for 4D-CT (2.6, p<0.001). The tumor-to- pancreas contrast in CE 3D-CT and CE 4D-CT were comparable (15.5 and 16.7 HU, p=0.71), and the later was significantly higher than that in 4D-CT (9.2 HU, p=0.03). Image noise in CE 3D-CT (12.5 HU) was significantly lower than that in CE 4D-CT (22.1 HU, p<0.001) and 4D-CT (19.4 HU, p=0.005). The CNR in CE 3D-CT and CE 4D-CT were comparable (1.4 and 0.8, p=0.23), and the former was significantly better than that in 4D-CT (0.6, p=0.04). The average tumor volume was smaller in CE 3D-CT (29.8 cm 3 ) and CE 4D-CT (22.8 cm 3 ) than in 4D-CT (42.0 cm 3 ), though the differences were not statistically significant. The tumor motion was comparable in 4D-CT and CE 4D-CT (7.2 and 6.2 mm, p=0.23). The inter-observer variations were comparable in CE 3D-CT and CE 4D-CT (Jaccard index 66.0% and 61.9%), and the former was significantly smaller than that of 4D-CT (55.6%, p=0.047).
Conclusions: The CE 4D-CT demonstrated largely comparable characteristics to the CE 3D-CT. It has high potential for simultaneously delineating the tumor and quantifying the tumor motion with a single scan.
Optimal fuzzy rule based pulmonary nodule detection
The document describes a lung cancer detection system that uses CT scans. It discusses (1) segmenting the lungs from CT images using adaptive thresholding and connected component analysis, (2) detecting nodule candidate regions using multi-thresholding and rule-based pruning, and (3) optimizing the rule-based pruning using a genetic algorithm trained fuzzy inference system to reduce false positives while maintaining high sensitivity. Experimental results on a publicly available lung image database show the optimized fuzzy system achieved better performance than a conventional rule-based approach.
Computer aided detection of pulmonary nodules using genetic programming
This document describes a method for detecting pulmonary nodules in CT scans using genetic programming. It first segments the lung regions from CT images and extracts nodule candidates. Features are then extracted from the candidates. Genetic programming is used to classify candidates as nodules or non-nodules by optimizing combinations of features. The method was tested on a publicly available lung image database, achieving a true positive rate of over 90% and low false positive rate.
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Radiomics and Deep Learning for Lung Cancer Screening
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Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
Radiomics Analysis of Pulmonary Nodules in Low Dose CT for Early Detection of...
Presentation clinical applications of Artificial Intelligence for radiation o...
Presentation clinical applications of Artificial Intelligence for radiation o...
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Aggressive Lung Adenocarcinoma Subtype Prediction Using FDG-PET/CT Radiomics
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
Robust Normal Lung CT Texture Features for the Prediction of Radiation-Induce...
4DCT driving improved lung radiotherapy outcomes at NCCI
4DCT driving improved lung radiotherapy outcomes at NCCI
computer aided detection of pulmonary nodules in ct scans
computer aided detection of pulmonary nodules in ct scans
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in...
Optimal fuzzy rule based pulmonary nodule detection
Optimal fuzzy rule based pulmonary nodule detection
Computer aided detection of pulmonary nodules using genetic programming
Computer aided detection of pulmonary nodules using genetic programming
Similar to Interpretable Spiculation Quantification for Lung Cancer Screening
PROSTATE CANCER IN NUTSHELL
This document discusses the radiotherapeutic management of prostate cancer. It begins by introducing the multidisciplinary team involved in diagnosis and treatment. It then covers diagnosis, including various screening tests and pathology findings. Treatment options discussed include observation, surgery, radiation therapy and hormonal treatment. The document focuses on the evolution of radiation techniques from conventional to IMRT and brachytherapy. It compares outcomes and side effects of different local treatment modalities and discusses the role of neoadjuvant hormonal therapy.
Sciahbasi A - AIMRADIAL 2013 - Radiation exposure
Sciahbasi A - AIMRADIAL 2013 - Radiation exposureInternational Chair on Interventional Cardiology and Transradial Approach
Vascular access route (radial vs femoral) and radiation exposure in percutaneous coronary interventions and diagnostic angiography1 Hassan
The document summarizes findings from a study on the appropriateness of upper endoscopy referrals in Italy. Key findings include:
1) 22% of over 13,000 endoscopies were deemed inappropriate based on guidelines. Relevant findings were found in 51% of appropriate referrals and 32% of inappropriate ones.
2) Common relevant findings included esophagitis, gastric erosions, esophageal varices, and ulcers. New cancers were found in 1.6% of patients.
3) Guidelines had high sensitivity but low specificity for relevant findings and cancer. A simple rule using age and alarm symptoms had similar accuracy to guidelines.
4) Over 20% of endoscopy
Chondrosarcoma
This study retrospectively analyzed outcomes of 27 patients who underwent surgery for chondrosarcomas originating in the sacrum between 1992-2014. It found that en bloc resection was associated with longer hospital stays than intralesional surgery. En bloc resection also resulted in greater loss of motor, bowel and bladder function compared to intralesional surgery, though the results were not statistically significant. The 5-year and 10-year overall survival rates for the entire group were 53% and 37.8% respectively, with a higher rate seen for unilateral nerve root resection versus bilateral resection. The study aims to help assess functional outcomes and quality of life for patients with this rare type of cancer.
Saturday 1615 – louvard – cto age
This study assessed the outcomes of percutaneous coronary intervention (PCI) for chronic total occlusions (CTO) in elderly patients aged 75 years and older compared to younger patients. The study found that CTO PCI success rates were similar between those aged 75+ and younger. While major adverse cardiac event rates were higher in the elderly, successful revascularization was associated with reduced MACE and cardiac mortality at 5-year follow-up in both age groups. The conclusion is that age alone should not determine PCI eligibility and that CTO PCI can provide benefits in elderly patients similar to younger patients when clinically appropriate.
MCC 2011 - Slide 14
Intersphincteric resection is a technique that allows for sphincter-preserving surgery for rectal cancers located 1-2 cm from the anal verge. The procedure involves partial resection of the internal anal sphincter while completely preserving the external anal sphincter. A study of 90 patients who underwent this surgery found that 82% had 5-year overall survival and 75% had 5-year disease-free survival. While 41% had perfect post-operative continence, 76% reported overall subjective satisfaction with functional results. Preoperative radiotherapy was associated with worse functional outcomes.
2018 preceptorship-lung-stereotactic-ablative-rt-early-stages-nsclc-suresh-senan
This document discusses stereotactic ablative radiotherapy (SABR) for early-stage lung cancer. It provides an overview of SABR as a technique and guidelines for its use. It also summarizes outcomes data from studies comparing SABR to surgery or conventional radiotherapy for early-stage lung cancer. Key findings include comparable local control with SABR compared to other options but superior quality of life. Toxicities are generally mild but increase for more central tumors or patients with comorbidities. Completely randomized trials are still needed to definitively guide treatment of early-stage lung cancer.
HEALTH ECONOMICS
This study compared the cost-effectiveness of carotid artery stent placement (CAS) versus carotid endarterectomy (CEA) in high-risk patients based on data from the SAPPHIRE trial. The mean cost per quality-adjusted life year (QALY) gained was higher for CAS ($16,223) than for CEA ($12,745). The incremental cost-effectiveness ratio estimated CAS to be $67,891 more expensive per QALY gained than CEA, with a wide range. Although CAS was not inferior to CEA for outcomes, its higher cost offsets the marginal benefit for high-risk patients.
Sino Nasal malignancy & Anterior skull base surgery, Endoscopy is the best ???
1) The document compares endoscopic versus open surgery for sinonasal and anterior skull base tumors. It finds that endoscopic surgery provides comparable oncologic outcomes to open surgery, with lower morbidity, faster recovery, and shorter hospital stays.
2) A review of studies found no difference in margins or survival between the two approaches. Endoscopic surgery was associated with significantly shorter hospital stays.
3) Complications were also lower with endoscopic surgery. While open surgery remains necessary in some complex cases, endoscopic surgery is presented as a valid alternative for most sinonasal and anterior skull base malignancies.
Carlo Di Mario - Recent Publications & Research in CTO: 2015-16
This document summarizes recent publications and research on chronic total occlusions (CTOs) from 2015-2016. It finds that the number of published manuscripts on CTOs has significantly increased in recent years. Several studies examined outcomes of patients undergoing percutaneous coronary intervention (PCI) on CTOs versus medical therapy or bypass surgery, finding lower mortality and adverse event rates with PCI. Other publications identified predictors of successful CTO recanalization and evaluated new techniques and scoring systems. Additional research investigated the physiological and anatomical changes in CTO arteries pre- and post-PCI using imaging modalities like intravascular ultrasound (IVUS) and computed tomography angiography (CTA).
Evaluating Lung Nodules in an Endemic Region for Coccidioidomycosis
This document summarizes a talk on evaluating lung nodules in a region where coccidioidomycosis is endemic. It discusses challenges in applying national guidelines locally. Radiological characteristics and clinical risk factors are used to evaluate nodules. A calculator was developed using these factors that better differentiates between coccidioidomycosis and lung cancer in nodules. Ongoing work includes refining the calculator, evaluating new tests like PCR for coccidioidomycosis, creating a tissue database, and assessing the impact of a lung nodule clinic.
03 suh lung sbrt hyderabad feb 2013 (cancer ci 2013) john h. suh
Stereotactic body radiation therapy (SBRT) is a form of high-precision radiotherapy that delivers large, precise radiation doses to tumors in just a few treatment sessions. Studies have shown SBRT provides excellent local tumor control of early stage non-small cell lung cancer comparable to surgery, with less invasive treatment. Ongoing and completed prospective studies continue to evaluate SBRT's long-term outcomes and toxicities compared to other standard treatments like surgery or conventional radiation therapy. SBRT is becoming an important treatment option for medically inoperable early stage lung cancer patients.
spine lecture cervical radiculopathy
A 55-year-old woman presented with 6 weeks of right upper extremity pain radiating to her right shoulder and arm. She has cervical radiculopathy likely due to foraminal stenosis. Options for treatment include anterior cervical discectomy and fusion (ACDF) or artificial disc replacement (ADR) to relieve pressure on the nerve root and prevent adjacent segment degeneration. Long-term studies show that both procedures provide relief of symptoms, but ADR is associated with less dysphagia and slightly better preservation of motion compared to ACDF.
Image processing in lung cancer screening and treatment
The document discusses image processing techniques for lung cancer screening and treatment. It covers topics like lung segmentation, nodule detection, computer-aided diagnosis, image-guided radiotherapy, and quantitative assessment of tumor response. Lung segmentation is used to isolate the lungs from other organs in CT images. Nodule detection algorithms then aim to find potential cancerous nodules. Computer-aided diagnosis systems analyze extracted features of nodules to determine if they are malignant or benign. Image-guided radiotherapy utilizes 4D CT and gating to account for tumor motion during treatment. Quantitative metrics like standardized uptake value are used to assess tumor response in PET imaging.
Intraoperative Ultrasound and MRI Scan in Glioma surgery
1. Intraoperative imaging such as intraoperative ultrasound (IOUS), intraoperative magnetic resonance imaging (IOMRI), and intraoperative computed tomography (IOCT) can help achieve maximal safe resection of brain tumors by identifying residual tumor for further resection.
2. Studies have shown IOUS and IOMRI are the most advantageous intraoperative imaging modalities and can improve extent of resection, particularly for non-enhancing gliomas. IOMRI has been shown to increase gross total resection rates compared to conventional surgery alone.
3. The extent of resection is a strong prognostic factor for survival, with increasing resection associated with improved progression-free and overall survival, especially for high grade gliomas. Intraoperative imaging allows for identification and resection
2011.05.11, CK Soc, SCIM Talk, Final
This document presents 10 cases of patients treated with CyberKnife radiosurgery for intramedullary spinal cord metastases. Key details include that patients had various primary cancers, most commonly breast cancer, and multiple metastases elsewhere. Lesions ranged in size from 0.12 to 1.98 cc. Patients received radiosurgery doses from 1400 to 2700 cGy in 2 to 5 fractions. Five of five evaluable patients had stabilization or improvement of symptoms. No patients experienced radiation toxicity. CyberKnife radiosurgery appears to be a good treatment option for intramedullary spinal cord metastases that can preserve neurologic function with low morbidity.
Non small cell ca
This document discusses the management of non-small cell lung carcinoma. It begins by outlining the lymph node staging system for NSCLC. It then discusses the main treatment modalities of surgery, radiation, and chemotherapy. For early stage disease, surgery is the primary treatment discussed. The document outlines criteria for determining operability and details on surgical procedures. It also discusses the role of radiation and chemoradiation for various stages. Post-operative radiation is discussed for high risk patients. The document provides guidance on chemotherapy regimens and timing for different stages.
Random and systematic errors 25.10.12
This document provides definitions and examples of random and systematic errors that can occur during the radiotherapy treatment process. It discusses various sources of errors including patient setup, organ motion, and target deformation. Methods for managing errors such as offline and online correction techniques, immobilization devices, and image-guidance are presented. The importance of distinguishing between random and systematic errors when establishing appropriate planning target volume margins is also emphasized.
H.F.R.S. for Meningiomas--Final Paper
This study evaluated outcomes of 28 patients with intracranial meningiomas treated with hypofractionated radiosurgery. Most tumors were treated with 22.5-30 Gy delivered in 5 fractions. With a mean follow up of 32.6 months, the local tumor control rate was 100% with only one instance of marginal progression. Symptoms improved or resolved in over 66% of patients who originally presented with symptoms. Side effects occurred in 4 patients but the permanent morbidity rate was low at 3.5%. Hypofractionated radiosurgery provided high tumor control with a low risk of side effects, even for large tumors greater than 9 cm3.
Dice corso_27.02
• Creation of 1 ATLAS
based on 15 pts
• 10 pts evaluated
- Autocontours breast CTV
- Autocontours breast PTV
- Autocontours OARs
- Autocontours pelvic
lymph nodes CTV
- Autocontours OARs
Evaluation:
Evaluation:
- Dice Similarity Coefficient
- Time savings
- Dice Similarity Coefficient
- Time savings
Results
Breast Cancer
Anorectal Cancer
Mean Dice Coefficient:
Mean Dice Coefficient:
CTV = 0.85
PTV = 0.80
OARs = 0.75
CTV
Similar to Interpretable Spiculation Quantification for Lung Cancer Screening (20)
2018 preceptorship-lung-stereotactic-ablative-rt-early-stages-nsclc-suresh-senan
2018 preceptorship-lung-stereotactic-ablative-rt-early-stages-nsclc-suresh-senan
Sino Nasal malignancy & Anterior skull base surgery, Endoscopy is the best ???
Sino Nasal malignancy & Anterior skull base surgery, Endoscopy is the best ???
Carlo Di Mario - Recent Publications & Research in CTO: 2015-16
Carlo Di Mario - Recent Publications & Research in CTO: 2015-16
Evaluating Lung Nodules in an Endemic Region for Coccidioidomycosis
Evaluating Lung Nodules in an Endemic Region for Coccidioidomycosis
03 suh lung sbrt hyderabad feb 2013 (cancer ci 2013) john h. suh
03 suh lung sbrt hyderabad feb 2013 (cancer ci 2013) john h. suh
Image processing in lung cancer screening and treatment
Image processing in lung cancer screening and treatment
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Unsupervised segmentation (unlabeled regions of interest, ROIs) and autoencoder (AE)-based classification were used to classify differences in cavitation patterns in knees and digits using the stained images (n=20-30 images/group).
Each image was divided into 256 x 256 pixel patches, and a convolutional neural network (CNN)-based unsupervised segmentation was used to identify ROIs. These patches were subsequently fed into a CNN-based AE whose latent space layer was connected to a classifier for input patch classification.
The AE was trained using the ROIs identified by the unsupervised segmentation, and the image classes were used to train the classifier. Whole image classifications were determined by maximum voting of the patch results and evaluated by accuracy.
Artificial Intelligence To Reduce Radiation-induced Cardiotoxicity In Lung Ca...
Traditionally, radiation-induced cardiotoxicity has been studied using cardiac radiation doses rather than functional imaging. We developed artificial intelligence (AI) models based on novel cardiac delta radiomics using pre- and post-treatment FDG-PET/CT scans to predict overall survival in lung cancer patients undergoing radiotherapy. We identified four clinically relevant delta radiomics features with the AI prediction models. The best model achieved an AUC of 0.91 on the training set and 0.87 on the test set. We are a pioneering group in AI for functional cardiac imaging. If validated, this approach will enable to use standard PET/CT scans as functional cardiac imaging with good predictive AUC for OS, as well as provide automated methods to provide functional cardiac information for clinical outcome prediction AI in lung cancer patients.
Novel Functional Radiomics for Prediction of Cardiac PET Avidity in Lung Canc...
Purpose/Objective(s)
Traditional methods of evaluating cardiotoxicity focus solely on radiation doses to the heart and do not incorporate functional imaging information. Functional imaging has great potential to improve the ability to provide early prediction for cardiotoxicity for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained as part of standard staging work up for lung cancer patients. Although FDG PET/CT scans are typically used to evaluate the tumor, imaging guidelines note that FDG PET/CT scans are an FDA-approved method to image for cardiac inflammation, and studies have noted that the PET cardiac signal can be predictive of clinical outcomes. The purpose of this work was to develop a radiomics model to predict clinical cardiac assessment of standard of care FDG PET/CT scans.
Materials/Methods
The study included 100 consecutive lung cancer patients treated with radiotherapy who underwent standard pre-treatment FDG-PET/CT staging scans. A clinician reviewed the PET/CT scans per clinical cardiac assessment guidelines and classified the cardiac uptake as: 0 = uniform diffuse, 1 = absent, 2 = heterogeneous, with event rates of 20%, 44%, and 35%, respectively. The heart was delineated and 200 novel functional radiomics features were selected to classify cardiac FDG uptake patterns. We divided the data into an 80% training set and a 20% test set to train and evaluate the classification models. Feature reduction was carried out using the Wilcoxon test (with Bonferroni adjusted p<0.05), hierarchical clustering, and Recursive Feature Elimination. Two automatic machine learning (AutoML) frameworks were used to determine classification models: a Random Forest Classifier (Tree-based Pipeline Optimization Tool, TPOT) and Linear Discriminant Analysis (AutoSklearn). 10-fold cross validation was carried out for training and the accuracy of the ability of the models to predict for clinical cardiac assessment is reported.
Results
Fifty-one independent radiomics features were reduced to 3 clinically pertinent features (PET 2D Skewness, PET Grey Level Co-occurrence Matrix Correlation, and PET Median) using feature reduction techniques. The model selected by TPOT showed 89.8% predictive accuracy in the cross validation of the training set and 85% predictive accuracy on the test set. The model selected by AutoSklearn showed 89.7% predictive accuracy in the cross validation of the training set and 80% predictive accuracy on the test set.
Conclusion
The novelty of this work is that it is the first study to develop and evaluate functional cardiac radiomic features from standard of care FDG PET/CT scans with the data showing good predictive accuracy with clinical imaging evaluation. If validated, the current work provides automated methods to provide functional cardiac information using standard of care imaging that can be used as an imaging biomarker for early clinical toxicity prediction for lung cancer patients.
Novel Deep Learning Segmentation Models for Accurate GTV and OAR Segmentation...
Purpose/Objective(s)
MR-guided adaptive radiotherapy (MRgART) improves target coverage and organ-at-risk (OAR) sparing in pancreatic cancer radiation therapy (RT). Inter-fractional changes in patients undergoing RT require time intensive re-delineation of gross tumor volume (GTV) and OARs prior to adaptive optimization. Accurate automatic segmentation has the potential to significantly improve efficiency of the adaptive workflow. We hypothesized that state-of-the-art deep learning (DL) segmentation models could adequately segment GTV and OARs in both planning and daily fractional MR scans.
Materials/Methods
The study included 21 patients with pancreatic cancer treated with MRgART (10 Gy x 5 fractions). The planning MR as well as all daily MR images and registrations were collected (6 image sets per patient and a total of 126 image sets). The planning MR and fraction 1-4 image sets were used as the training set (N = 105), while the test set (N = 21) comprised images for fraction 5, to simulate the last step of incremental learning from planning to final fraction. Evaluated contours included the GTV, Small Bowel, Large Bowel, Duodenum, Left and Right Kidney, Liver, Spinal Cord, and Stomach. To mimic clinical conditions, contour accuracy was evaluated within the ring structure surrounding the PTV, inside of which daily adaptive re-contouring is applied (2 cm expansion in the cradio-caudal direction, 3 cm expansion otherwise). We evaluated three DL model architectures: SegResNet, SegResNet 2D, and SwinUNETR to autosegment GTV and OARs. The segmentation models were trained on the training set using 5-fold cross-validation (CV) and quantitatively analyzed by comparing against clinically used contours with DICE scores. Qualitative analysis was performed by a radiation oncologist using a scoring scale: 1 = perfect, 2 = minor discrepancy, 3 = moderate discrepancy, and 4 = rejected.
Results
Overall, the DL segmentations were in acceptable agreement with clinical contours. The best performing model was the SwinUNETR model with overall training DICE = 0.88±0.06, test DICE = 0.78±0.11, and qualitative score of 1.6±0.8. The agreement between the DL model and clinical segmentation for the GTV was 0.79±0.08, with a qualitative score of 2.2±0.9
Conclusion
We report here the most comprehensive work on DL segmentation for pancreatic cancer MRgART, including quantitative and clinically-pertinent qualitative evaluations of 126 image sets and 3 DL architectures. Our data show good quantitative agreement between DL and clinical contours, and acceptable clinician evaluations for the majority of GTVs and OARs. The current work has great potential to significantly reduce a major bottleneck in the MRgART workflow for pancreatic cancer patients.
Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Canc...
AAPM2023_SU-300-IePD-F6-4
Purpose: Traditional methods of evaluating cardiotoxicity rely on cardiac radiation doses and do not incorporate functional imaging. Cardiac functional imaging can improve the ability to provide early prediction for clinical outcomes for lung cancer patients undergoing radiotherapy. FDG-based PET/CT imaging is routinely obtained for staging and disease assessment after treatment. Although FDG PET/CT scans are typically used to evaluate the tumor, studies have shown that the PET cardiac signal is predictive of clinical outcomes. Our study aimed to develop novel functional cardiac delta radiomics using pre and post-treatment FDG PET/CT scans to predict for overall survival (OS).
Methods: We conducted a study of 109 lung cancer patients who underwent standard FDG-PET/CT scans pre- and post-radiotherapy. Data from ACRIN 6668 (N=70) and an investigator-initiated lung cancer trial (N=39) for functional avoidance radiotherapy were used. The heart was delineated, and 200 cardiac CT and PET functional radiomics features were selected. Delta radiomics was calculated as the change between pre- and post-PET/CT. The data were divided into 80%/20% training/test set, and feature reduction was performed using Wilcoxon test, hierarchical clustering, and recursive feature elimination. A Gradient Boosting Classifier machine learning model evaluated the ability of the delta PET/CT cardiac radiomics to predict for OS using 10-fold cross-validation for training and area-under-the-curve (AUC) for model assessment.
Results: Median survival was 431 days (range 144 to 1640 days). 4 clinically relevant delta features were identified: pre-CT_Maximum, post-CT_Minimum, delta-CT_GLRM_Run_Variance, delta-PET_GLRM_Run_Entropy. The model showed an AUC of 0.91 on the training set and an AUC of 0.87 on the test set.
Conclusion: This is the first study to evaluate functional cardiac delta radiomic features from standard PET/CT scans with data showing good predictive AUC for OS. If validated, this work provides automated methods to provide functional cardiac information for clinical outcome prediction in lung cancer patients.
CIRDataset: A large-scale Dataset for Clinically-Interpretable lung nodule Ra...
The CIRDataset provides a large-scale dataset of 956 annotated lung nodules with segmentations and classifications of spiculations and lobulations, which are important radiomic features for assessing malignancy. It aims to address the lack of publicly available datasets capturing these subtle radiological features typically assessed by radiologists but often smoothed over by deep learning segmentation models. The dataset is accompanied by code, models, and a pipeline to enable the development of AI systems for joint nodule segmentation, classification of spiculations/lobulations, and malignancy prediction using an end-to-end deep learning approach.
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Automatic motion tracking system for analysis of insect behavior
Undergraduate research.
We present a multi-object tracking system to track small insects such as ants and bees. Motion-based object tracking recognizes the movements of objects in videos using information extracted from the given video frames. We applied several computer vision techniques, such as blob detection and appearance matching, to track ants. Moreover, we discussed different object detection methodologies and investigated the various challenges of object detection, such as illumination variations and blob merge/split. The proposed system effectively tracked multiple objects in various environments.
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Normal lung CT texture features have been used for the prediction of radiation-induced lung disease (radiation pneumonitis and radiation fibrosis). For these features to be clinically useful, they need to be relatively invariant (robust) to tumor size and not correlated with normal lung volume.
http://scitation.aip.org/content/aapm/journal/medphys/43/6/10.1118/1.4955803
Robust breathing signal extraction from cone beam CT projections based on ada...
This document summarizes a research paper that proposes a novel method for extracting breathing signals from cone beam CT projections without using external markers. The method uses an adaptive filtering technique to enhance weak oscillating structures in the Amsterdam Shroud image generated from the projections. A two-step optimization approach is then used to reveal the large-scale regularity of the breathing signals. Evaluation on 5 patient data sets found the new algorithm outperformed existing methods by extracting less noisy signals with errors of only -0.07±1.58 breaths per minute compared to reference signals. While results are promising, the study had a small data set and image quality remains limited.
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Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar lead (limb II)
4. Differentiate between intervals and segments
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1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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Tests for analysis of different pharmaceutical.pptx
Interpretable Spiculation Quantification for Lung Cancer Screening
- 1. Interpretable Nodule Spiculation Quantification for Lung Cancer Screening Wookjin Choi*, Saad Nadeem*, Sadegh Riyahi, Joseph O Deasy, Allen Tannenbaum, and Wei Lu Department of Medical Physics * The first two authors contributed equally to this work. Purpose •Lung cancer is leading cause of cancer death, but early detection can reduce the mortality. •The Lung Imaging Reporting and Data System (Lung-RADS) developed by the American College of Radiology •Lung cancer screening using radiomics has also been studied and achieved moderate accuracy (77.5% – 84.6%). •Nodule Spiculation for Lung Cancer Screening with CT –Important suspicious feature: Lung-RADS –Qualitatively read by radiologists as absent/spiculated, or spiculation score 1-5 Angle-Preserving Spherical Conformal Mapping First eigenfunction of the Laplace- Beltrami operator Conformal mapping from surface 𝑆 to unit sphere 𝒮2: 𝜙: 𝑆 → 𝒮2 Nadeem, et al. 2017. IEEE Trans Vis Comput Graph 23: 1663-1676. Area distortion 𝜖𝑖: = log 𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣 𝑘)] 𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣 𝑘] 1. Detect spicule base at 𝜖𝑖=0 (green-yellow) 2. Recursively traverse closed curves toward the negative distortion values 3. Can break and move towards different apex 4. Detect spicule apex at max. negative 𝜖𝑖 (red x) Spiculation Scores 𝑠1 = 𝑖 mean 𝜖 𝑝 𝑖 ∗ ℎ 𝑝 𝑖 𝑖 ℎ 𝑝 𝑖 Sensitivity Specificity Accuracy AUC Cross validation on 72 cases Shape+Texture 87.2% 81.2% 84.6% 0.89 Shape+Texture+Radiologist’s score (𝑠𝑟) 87.8% 87.1% 87.5% 0.91 Shape+Texture+Our measures (𝑁𝑝 + 𝑠1) 92.7% 83.9% 88.9% 0.92 External validation on 811 cases Shape+Texture 63.2% 82.1% 79.4% 0.79 Shape+Texture+Radiologist’s score (𝑠𝑟) 73.7% 64.1% 65.5% 0.77 Shape+Texture+Our measures (𝑁𝑝 + 𝑠1) 73.7% 80.9% 79.9% 0.82 Dataset Sensitivity Specificity Accuracy AUC Hawkins et al. (2016) Baseline CT scans of 261 pati ents in NLST 51.7% 92.9% 80.0% 0.83 Buty et al. (2016) LIDC 2054 PNs Ground-truth by radiologists assessment 82.4% Kumar et al. (2015) LIDC 97 patients Including metastatic tumors 79.1% 76.1% 77.5% Liu et al. (2016) 172 patients Two independent cohorts 102 and 70 patients 71.4% 83.7% 80.0% 0.81 Choi et al. (2018) LIDC 72 patients 87.2% 81.2% 84.6% 0.89 Proposed Method LIDC 72 patients 86.6% 84.5% 88.9% 0.92 •Can not distinguish whether a spicule is tumor invasion or is normal anatomy (vessel) •Not differentiate spiculation and lobulation •Not consider size of nodule •Manual nodule segmentations, some spicules not segmented completely •Novel interpretable quantification of pulmonary nodule spiculation •Improved malignancy prediction accuracy •Niehaus et al. shape features –Roughness of boundary and compactness –Dependent on size of nodule (AUC 65% to 91%) •Dhara et al. differential geometry –Spicule identified as voxels with higher GD/VD2, GD = geodesic distance, VD = voxel density –Spiculation score: spicule height and angle Dhara, et al. 2016. Int J Comput Assit Radiol Surg 11: 337-349. Niehaus, et al. 2015. J Digit Imaging 28: 704-717 • Public dataset: Lung Image Database Consortium image collection (LIDC-IDRI) • Four radiologists performed nodule contouring and image annotation • Spiculation score ranged between 1 (non- spiculated) and 5 (highly spiculated) Binary mask of segmented nodule Triangular mesh representation Angle-preserving spherical conformal mapping Compute area distortion 𝜖 Detect spiculation Quantify spiculation Conclusion Limitations Method Spiculation Detection Results Quantify Spiculation with Spherical Conformal Mapping and Area Distortion Metric Patient Study Compare against Mean Curvature Method Compare against Radiologists Spiculation Score Synthetic Spicules Detection 5-mm Spicules 2-mm Spicules • All 5-mm spicules were detected, isolated or merged • 3 of four 2-mm isolated spicules and 1 of four 2- mm merged spicules were detected Malignancy Prediction by SVM-LASSO Radiomics Model Choi, et al. 2018. Med Phys 45: 1537-1549. Comparison with other Radiomics Studies Spiculation Quantification •Area distortion better than solid angle for non-conic spicules •Height can be medial axis length or perpendicular distance •Width can be measured at base or at half max. (FWHM)