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Sep 17, 2021 @ Thomas Jefferson Univ
Artificial Intelligence
in Radiation Oncology
Wookjin Choi, PhD
Assistant Professor of Computer Science
Virginia State University
wchoi@vsu.edu
Acknowledgements
Memorial Sloan Kettering Cancer Center
• Wei Lu PhD
• Sadegh Riyahi, PhD
• Jung Hun Oh, PhD
• Saad Nadeem, PhD
• Joseph O. Deasy, PhD
• Andreas Rimner, MD
• Prasad Adusumilli, MD
• Chia-ju Liu, MD
• Wolfgang Weber, MD
Stony Brook University
• Allen Tannenbaum, PhD
University of Virginia School of Medicine
• Jeffrey Siebers, PhD
• Victor Gabriel Leandro Alves, PhD
• Hamidreza Nourzadeh, PhD
• Eric Aliotta, PhD
University of Maryland School of Medicine
• Howard Zhang, PhD
• Wengen Chen, MD, PhD
• Charles White, MD
• Seth Kligerman, MD
• Shan Tan, PhD
• Jiahui Wang, PhD
2
NIH/NCI Grant R01 CA222216, R01 CA172638 and NIH/NCI Cancer Center Support Grant P30 CA008748
The ESTRO Falcon project team, Scott Kaylor of EduCase, Benjamin Nelms of Proknow for the multi-
delineator contour data presented in this work
AI in Radiation Oncology
3
Huynh et al. Nat Rev Clin Oncol 2020
4
Netherton et al. Oncology 2021
Hype cycle for three major innovations
in radiation oncology
Automatable tasks in radiation oncology
for the modern clinic
Outline
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
5
Radiomics
6
 Controllable Feature Analysis
 More Interpretable
Lambin, et al. Eur J Cancer 2012
Aerts et al., Nature Communications, 2014
Radiomics Framework
7
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
• Automated Workflow (Python)
- Integrate all the radiomics components
- 3D Slicer, ITK (C++), Matlab, R, and Python
- Scalable: support multicore & GPU computing
Radiomics Framework
8
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing
Radiomics Framework
9
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing
Radiomics Framework
10
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing
Radiomics Framework
11
Image
Registration
• Multi-level rigid
• Deformable
• Pre/Post-CT
• MSE, MI
Tumor
Segmentation
• Adaptive region growing
• Level set
• Grow cut
• Morphology filter
• Multi-modality
image segmentation
Feature
Extraction
• Intensity distribution
• Spatial variations
(texture)
• Geometric properties
• Jacobian feature
from DVF
• Feature selection
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
Source codes: https://github.com/taznux/radiomics-tools
Deep Learning Model
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing
Radiomics Framework
12
Image
Registration
• Deep Learning Optical fl
ow
• Action like flow
• Differential warp
• Dynamic filtering
• …
Tumor
Segmentation
• U-Net
• Prob. U-Net
• UANet
• …
Feature
Extraction
• AlexNet
• ResNet
• VGG
• LeNet
• ….
Predictive
Model
• ROC analyses
• Prediction models
• Validation
• Tumor response
• Recurrence
• Survival
• Automated Workflow (Python)
• Integrate all the radiomics components
• 3D Slicer, ITK (C++), Matlab, R, and Python
• Scalable: support multicore & GPU computing
Lung Cancer Screening
13
 Early detection of lung cancer by LDCT can reduce mortality
 Known features correlated with PN malignancy
 Size, growth rate (Lung-RADS)
 Calcification, enhancement, solidity → texture features
 Boundary margins (spiculation, lobulation), attachment → shape and
appearance features
Malignant nodules Benign nodules
Size Total Malignancy
< 4mm 2038 0%
4-7 mm 1034 1%
8-20 mm 268 15%
> 20 mm 16 75%
ACR Lung-RADS
Category Baseline Screening Malignancy
1 No PNs; PNs with calcification
Negative
<1% chance of malignancy
2
Solid/part-solid: <6 mm
GGN: <20 mm
Benign appearance
<1% chance of malignancy
3
Solid: ≥6 to <8 mm
Part-solid: ≥6 mm with solid component <6 mm
GGN: ≥20 mm
Probably benign
1-2% chance of malignancy
4A
Solid: ≥8 to <15 mm
Part-solid: ≥8 mm with solid component ≥6 and <8 mm
Suspicious
5-15% chance of malignancy
4B
Solid: ≥15 mm
Part-solid: Solid component ≥8 mm
>15% chance of malignancy
4X
Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph
nodes) or suspicious imaging findings (e.g. spiculation)
>15% chance of malignancy
14
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System
Lung Cancer Screening (Methodology)
• TCIA LIDC-IDRI public data set (n=1,010)
• Multi-institutional data
• 72 cases evaluated (31 benign and 41 malignant cases)
• Consensus contour
15
GLCM GLRM
Texture features Intensity features
2D
Shape features
3D
Lung Cancer Screening (SVM-LASSO Model )
16
SVM classification
Distinctive feature identification
Malignant?
Predicted malignancy
Feature extraction
Yes
10x10-fold
CV
10-fold
CV
LASSO feature selection
• Size (BB_AP) : Highly correlated with the axial longest diameter
and its perpendicular diameter (r = 0.96, larger – more
malignant)
• Texture (SD_IDM) : Tumor heterogeneity (smaller – more
malignant)
Lung Screening (Results: Comparison)
Sensitivity Specificity Accuracy AUC
Lung-RADS
Clinical guideline
73.3% 70.4% 72.2% 0.74
Hawkins et al. (2016)
Radiomics – 23 features
51.7 % 92.9% 80.0% 0.83
Ma et al. (2016)
Radiomics – 583 features
80.0% 85.5% 82.7%
Buty et al. (2016)
DL – 400 SH and 4096 AlexNet features
82.4%
Kumar et al. (2015)
DL: 5000 features
79.1% 76.1% 77.5%
Proposed
Radiomics: two features (Size and Texture)
87.2% 81.2% 84.6% 0.89
17
DL: Deep Learning, SH: Spherical Harmonics
Choi et al., Medical Physics, 2018.
ACR Lung-RADS
Category Baseline Screening Malignancy
1 No PNs; PNs with calcification
Negative
<1% chance of malignancy
2
Solid/part-solid: <6 mm
GGN: <20 mm
Benign appearance
<1% chance of malignancy
3
Solid: ≥6 to <8 mm
Part-solid: ≥6 mm with solid component <6 mm
GGN: ≥20 mm
Probably benign
1-2% chance of malignancy
4A
Solid: ≥8 to <15 mm
Part-solid: ≥8 mm with solid component ≥6 and <8 mm
Suspicious
5-15% chance of malignancy
4B
Solid: ≥15 mm
Part-solid: Solid component ≥8 mm
>15% chance of malignancy
4X
Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph
nodes) or suspicious imaging findings (e.g. spiculation)
>15% chance of malignancy
18
ACR: American College of Radiology
Lung-RADS: Lung CT Screening Reporting and Data System
Spiculation Quantification (Motivation)
• Blind Radiomics
• Semantic Features
• Semi-automatic Segmentation
- GrowCut and LevelSet
19
Radiologists spiculation score (RS) for different pulmonary nodules
1 2 3 4 5
Choi et al. in CMPB 2021
Spiculation Quantification (Methodology)
20
𝜖𝑖: = log
𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣𝑘)])
𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣𝑘])
Area Distortion Map
Spherical Mapping
Eigenfunction
Spiculation Quantification (Results)
21
RS 1 2 3 4 5
s1 -0.2 -0.7 -0.6 -1.1 -1.8
s2 0.1 0.2 0.4 0.6 1.6
Ns 0 1 4 8 14
Nl 2 0 3 1 1
Na 1 14 4 0 1
Spiculation quantification and attachment detection via area distortion metric. The results of spiculation
detection (red line: baseline of peak, black line: medial axis of peak, red X: spiculation, black X:
lobulation, yellow X: attached peaks). Radiologist’s spiculation score (RS), and the proposed interpretable
spiculation features ( s1 : spiculation score, s2 : spiculation score, Ns : no. of spiculation, Nl : no. of
lobulation and Na : no. of attached peaks) are shown below.
Spiculation Quantification (Results: Comparison)
22
Choi et al. in CMPB 2021
Progression-free survival Prediction
after SBRT for early-stage NSCLC
23
Thor, Choi et al. ASTRO 2020
• 412 patients treated between 2006 and 2017
• PETs and CTs within three months prior to SBRT start.
• The median prescription dose was 50Gy in 5 fractions.
Progression-free survival Prediction (Results)
• PET entropy, CT number of peaks,
CT major axis, and gender.
• The most frequently selected model
included PET entropy and CT
number of peaks
- The c-index in the validation subset
was 0.77
- The prediction-stratified survival
indicated a clear separation between
the observed HR and LR
- e.g. a PFS of 60% was observed at 12
months in HR vs. 22 months in LR.
24
Thor, Choi et al. ASTRO 2020
Local tumor morphological changes
25
Jacobian Map
- Jacobian matrix: calculates rate of displacement change in each direction.
- Determinant indicates volumetric ratio of shrinkage/expansion.
𝐷𝑒𝑡 𝐽 =
𝐷𝑒𝑡 𝐽 > 1 volume expansion
𝐷𝑒𝑡 𝐽 = 1 no volume change
𝐷𝑒𝑡 𝐽 < 1 volume shrinkage
𝐷𝑒𝑡 𝐽 = 1.2 = 20% expansion
𝐷𝑒𝑡 𝐽 = 0.8 = 20% shrinkage (-20%)
Riyahi, Choi et al., PMB 2018
Local tumor morphological changes (Results)
26
Local tumor morphological changes (Results)
Features P-value AUC Correlation to responders
Minimum Jacobian 0.009 0.98 -0.79
Median Jacobian 0.046 0.95 -0.72
The P-value, AUC and correlation to responders for all significant features in univariate analysis
27
Riyahi, Choi et al., PMB 2018 SVM-LASSO: AUC 0.91
Aggressive Lung ADC Subtype Prediction (Motivation)
28
CT
MIP
PET/CT
Soild
CT PET/CT
Five classifications of lung ADC Travis et
al. JTO 2011
 Solid and MIP components: poor surgery/SBRT prognosis factor
 Benefit from lobectomy rather than limited resection
 Core biopsy (Leeman et al. IJROBP 2017)
 Minimally invasive, not routinely performed, sampling error (about 60%
agreement with pathology)
 Preoperative diagnostic CT and FDG PET/CT radiomics
 Non-invasive and routinely performed
Aggressive Lung ADC Subtype Prediction (Method & Results)
• Retrospectively enrolled 120 patients
- Stage I lung ADC, ≤2cm
- Preoperative diagnostic CT and FDG PET/CT
• Histopathologic endpoint
- Aggressiveness (Solid : 18 cases, MIP : 5 cases)
• 206 radiomic features & 14 clinical parameters
• SVM-LASSO model
29
Performance of the SVM-LASSO model to predict aggressive lung ADC
Choi et al. Manuscript under review
Box plots of SUVmax (FDR q=0.004) and PET Mean of
Cluster Shade (q=0.002)
Feature Sensitivity Specificity PPV NPV Accuracy AUC
Conventional SUVmax 57.8±4.6% 78.5±1.4% 39.2±2.3% 88.6±1.1% 74.5±1.4% 0.64±0.01
SVM-LASSO
PET Mean of Cluster
Shade
67.4±3.1% 86.0±1.1% 53.7±2.1% 91.7±1.0% 82.4±1.0% 0.78±0.01
p-value SUVmax vs. SVM-LASSO 0.002 1e-5 7e-8 3e-5 5e-8 0.03
Unsupervised Learning of Deep Learned Features
from Breast Cancer Images
30
Lee, Choi et al. IEEE BIBE 2020
Unsupervised Learning of Deep Learned Features
31
Slide name
Silhouette
optimal
number
Cluster set Accuracy F1-score
TCGA-A7-A0DA 29
[25, 22, 6,
2, 24, 14,
0, 20, 10]
0.8829 0.8929
TCGA-A2-A0YM 20 [7, 6, 1, 5] 0.8360 0.8863
TCGA-A2-A3XT 19
[13, 10, 5,
1, 2]
0.9316 0.9514
TCGA-BH-A0BG 8 [1, 5] 0.7828 0.6857
TCGA-E2-A1LS 26 [18, 5] 0.8495 0.8680
TCGA-OL-A66I 25
[7, 18, 12,
20, 1, 0]
0.7761 0.7091
TCGA-C8-A26Y 21
[9, 18, 12,
16]
0.8594 0.8122
Lee, Choi et al. IEEE BIBE 2020
PathCNN: interpretable convolutional neural networks
for survival prediction and pathway analysis applied to glioblastoma
32
Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN.
• CNNs have achieved great success
• A lack of interpretability remains
a key barrier
• Moreover, because biological array
data are generally represented in a
non-grid structured format
• PathCNN
An interpretable CNN model on
integrated multi-omics data using
a newly defined pathway image.
PathCNN: interpretable CNNs (results)
33
Cancer PathCNN Logistic
regression SVM with RBF Neural network MiNet
GBM 0.755 ± 0.009 0.668 ± 0.039 0.685 ± 0.037 0.692 ± 0.030 0.690 ± 0.032
LGG 0.877 ± 0.007 0.816 ± 0.036 0.884 ± 0.017 0.791 ± 0.031 0.854 ± 0.027
LUAD 0.637 ± 0.014 0.581 ± 0.028 0.624 ± 0.034 0.573 ± 0.031 0.597 ± 0.042
KIRC 0.709 ± 0.009 0.654 ± 0.034 0.684 ± 0.027 0.702 ± 0.028 0.659 ± 0.030
Comparison of predictive performance with benchmark methods in terms of the area
under the curve (AUC: mean ± standard deviation) over 30 iterations of the 5-fold
cross validation
Note: AUCs for PathCNN were obtained with three principal components. Bold = Highest AUC for each dataset.
SVM, support vector machine; RBF, radial basis function; MiNet, Multi-omics Integrative Net; GBM, glioblastoma
multiforme; LGG, low-grade glioma; LUAD, lung adenocarcinoma; KIRC, kidney cancer.
Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN.
Outline
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
34
Delineation Variability Quantification and Simulation
A framework for radiation therapy variability analysis
35
RT plan
Structure Set
CT Image
Dose Distribution
Structure Sets
DV simulation
ASSD
GrowCut
RW
Other delineators
SV analysis
DV analysis
Geometric
Dosimetric
Variability analysis
Human DV
Simulated
DV
Consensus SS
OARNet
Choi et al., AAPM, 2019.
Delineation Variability Quantification and
Simulation
• ESTRO Falcon contour workshop (EduCase)
- A HNC case, Larynx, 70 Gy and 35 fractions
- 14 independent manually delineated (MD) OAR structure sets (SS)
- BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid
• Consensus MD SS
- The simultaneous truth and performance level estimation (STAPLE)
36
Choi, Nourzadeh et al., AAPM, 2019.
Delineation Variability Quantification and
Simulation (Methods)
•Geometric analysis
- Similarity: Dice coefficient (Volumetric, Surface)
- Distance: Hausdorff distance (HD), Actual Average Surface
Distance (AASD)
- Reference: STAPLE SS
•Dosimetric analysis
- Single dose distribution planned from a human SS
- DVH confidence bands (90%tile)
- 𝐷mean, 𝐷max, 𝐷min, 𝐷50
37
Choi, Nourzadeh et al., AAPM, 2019.
Delineation Variability Quantification and Simulation (Results)
• DVH variability not predicted by geometric measures
• Large human variability
38
100%
50%
0%
100%
50%
0%
Human ASSD GrowCut RW
Right
Parotid
Left
Parotid
Choi, Nourzadeh et al., AAPM, 2019.
Human ASSD GrowCut RW
100%
50%
0%
Education
100%
50%
0%
Clinic
39
Dose Volume Coverage Map (DVCM)
40
Human ASSD
GrowCut RW
• Plan Competition Data Set: A HNC case, Nasopharynx , 70 Gy
• PV: 409 plans for IMRT, VMAT, and Tomotherapy on various TPS
Eclipse, Monaco , Pinnacle, RayStation, Tomotherapy
• SV: Setup error simulation using Radiation Therapy Robustness
Analyzer (RTRA)
• 1000 simulations: 3mm translation and 5-degree rotation
• DV: 5 manually delineated (MD) SSs
• BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, and SpinalCord
• 3x5 Simulated DVs using Radiation Therapy Variability Analyzer (RTVA)
Variability Analysis
Plan Variability, Setup Variability, Delineation Variability
41
Choi, Nourzadeh et al., AAPM, 2020.
Dose Volume Coverage Map (DVCM)
42
Plan Variability Setup Variability Delineation Variability
Choi, Nourzadeh et al., AAPM, 2020.
Plan Competition DVCM Analysis
43
Fractional
Volume
Dose
OAR Dose Constraint
Prob Threshold
5%
Plan Competition DVCM Analysis
44
TV SV×PV SV×DV PV×DV SV PV DV Average
BrainStem 0.15 0.11 0.10 0.07 0.03 0.01 0.02 0.07 >0.95
Chiasm 0.68 0.00 0.65 0.56 0.00 0.00 0.49 0.34
Eye_L 0.07 0.05 0.04 0.01 0.02 0.01 0.01 0.03
Eye_R 0.02 0.01 0.01 0.01 0.00 0.00 0.01 0.01 >0.5
Lens_L 1.00 1.00 1.00 1.00 0.99 1.00 0.01 0.86
Lens_R 1.00 1.00 1.00 1.00 1.00 1.00 0.01 0.86 >0.3
Mandible 0.04 0.04 0.00 0.07 0.00 0.08 0.01 0.03 >0.2
OpticNerve_L 0.51 0.42 0.43 0.36 0.17 0.00 0.34 0.32 >0.05
OpticNerve_R 0.56 0.38 0.61 0.38 0.20 0.00 0.39 0.36 ≤0.05
SpinalCord 0.13 0.12 0.05 0.05 0.04 0.01 0.02 0.06 =0.00
Average 0.42 0.31 0.39 0.35 0.25 0.21 0.13 0.29
Fractional volume affected by different variations when constraint failing probability > 5% (worst-case scenario)
Choi, Nourzadeh et al., AAPM, 2020.
OARNet: auto-delineate organs-at-risk (OARs) in
head and neck (H&N) CT image
45
Soomro, Nourzadeh, Choi et al., AAPM, 2020.
OARNet results
46
A Probabilistic U-Net for Segmentation of Ambiguous Images
Kohl et al. NeurIPS 2018 47
Segmentations on different Variability levels (middle 5) and their occupancy map
• Implemented the model using PyTorch
• Model trained using TCIA LIDC dataset (about 2000 nodules with up to 4 radiologists delineations)
• Titan Xp, 12GB (1 week for training)
• Unstable to train, 2D segmentation, tumor
A Probabilistic U-Net (Results)
48
Variability
Low High
A Probabilistic U-Net for Segmentation of Ambiguous Images
Wasserstein distance?
2D → 2.5D → 3D 49
Summary
 Radiomics - Decision Support Tools
• Lung Cancer Screening
• Tumor Response Prediction and Evaluation
• Aggressive Lung ADC subtype prediction
• Multimodal data: Pathology, Multiomics, etc.
 Auto Delineation and Variability Analysis
• Delineation Variability Quantification
• Dosimetric Consequences of Variabilities
• OARNet, Probabilistic U-Net
50
Short-term Future Works
• Human-Variability aware auto-delineation
• Variability quantification and simulation using generative models
• OARNet + Probabilistic U-Net → Probabilistic OARNet
• Develop interpretable radiomic features
• Improve spiculation quantification
• Multi-institution validation
• Integrate the radiomics framework into TPS
• Eclipse (C#), MIM (Python), RayStation (Python)
51
Long-term Future Works
• Comprehensive Framework for Cancer Imaging
• Multi-modal imaging
• Response prediction and evaluation (Pre, Mid, and Post)
• Longitudinal analysis of tumor change
• Shape analysis (e.g. Spiculation)
• Deep learning models
• Automation of Clinical Workflow
• Big Data Analytics: EMR, PACS, ROIS, Genomics, etc.
• Provide an informatics platform for comprehensive cancer therapy
52
Selected Publications
1. Jung Hun Oh*, Wookjin Choi* et al., “PathCNN: interpretable convolutional neural networks for survival prediction and
pathway analysis applied to glioblastoma”, Bioinformatics, 2021, *joint first author
2. Wookjin Choi et al., “ Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening”, Computer
Methods and Programs in Biomedicine”, 2021
3. Noemi Garau, Wookjin Choi, et al., “ External validation of radiomics‐based predictive models in low‐dose CT screening
for early lung cancer diagnosis”, Medical Physics, 2020
4. Jiahui Wang, Wookjin Choi et al., “Prediction of anal cancer recurrence after chemoradiotherapy using quantitative
image features extracted from serial 18F-FDG PET/CT”, Frontiers in oncology, 2019
5. Wookjin Choi et al., “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”,
Medical Physics, 2018
6. Sadegh Riyahi, Wookjin Choi, et al., “Quantifying local tumor morphological changes with Jacobian map for prediction of
pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer”, Physics in Medicine and
Biology, 2018
7. Shan Tan, Laquan Li, Wookjin Choi, et al., “Adaptive region-growing with maximum curvature strategy for tumor
segmentation in 18F-FDG PET”, Physics in Medicine and Biology, 2017
8. Wookjin Choi et al., “Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Ductal
Adenocarcinoma”, Medical Physics, 2016
9. Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based
Feature Descriptor”, Computer Methods and Programs in Biomedicine, 2014
53
Complete list of publications: https://scholar.google.com/citations?user=iHgsGLUAAAAJ
54
Thank You!
Q & A
https://qradiomics.wordpress.com
E-mail: wchoi@vsu.edu
Spiculation Quantification: Height, Area Distortion
• Area distortion better than solid angle for non-conic spicules
• Height is medial axis length, can be perpendicular distance
• Width is measured at base, can be FWHM
𝑠1 =
𝑖 mean 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖
𝑖 ℎ𝑝 𝑖
𝑠2 =
𝑖 min 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖
𝑖 ℎ𝑝 𝑖
55
Spiculation Quantification (Model Validation)
56
Local tumor morphological changes
(Motivation)
57
Delineation Variability Simulation (Methods)
• DV Simulation using auto-delineation (AD) methods (σ=2, 5, 10 mm)
• Average surface of standard deviation (ASSD): random perturbation
• GrowCut: cellular automata region growing
• Random walker (RW): probabilistic segmentation
58
Background 0
Foreground 1
Initial Binary Mask
Gaussian-Smoothed Mask
Gaussian Noises-Added Mask
Intensity
Spatial location
Inside
Outside
σ = 2mm σ = 5mm σ = 8mm σ = 10mm
Choi et al., AAPM, 2019.
OARNet comparisons
59
(a) Dice similarity coefficient and (b) Hausdorff comparison for the alternative delineation
methods. The points in the graphs are mean values and bars show the 95% confidence intervals.
60
A matrix of adjusted P-values.The row represents the 146 KEGG pathways ordered on pathway images.
The columns represent the first two principal components of each omics type.The red color indicates key
pathways with adjusted P-values < 0.001

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Artificial Intelligence in Radiation Oncology

  • 1. Sep 17, 2021 @ Thomas Jefferson Univ Artificial Intelligence in Radiation Oncology Wookjin Choi, PhD Assistant Professor of Computer Science Virginia State University wchoi@vsu.edu
  • 2. Acknowledgements Memorial Sloan Kettering Cancer Center • Wei Lu PhD • Sadegh Riyahi, PhD • Jung Hun Oh, PhD • Saad Nadeem, PhD • Joseph O. Deasy, PhD • Andreas Rimner, MD • Prasad Adusumilli, MD • Chia-ju Liu, MD • Wolfgang Weber, MD Stony Brook University • Allen Tannenbaum, PhD University of Virginia School of Medicine • Jeffrey Siebers, PhD • Victor Gabriel Leandro Alves, PhD • Hamidreza Nourzadeh, PhD • Eric Aliotta, PhD University of Maryland School of Medicine • Howard Zhang, PhD • Wengen Chen, MD, PhD • Charles White, MD • Seth Kligerman, MD • Shan Tan, PhD • Jiahui Wang, PhD 2 NIH/NCI Grant R01 CA222216, R01 CA172638 and NIH/NCI Cancer Center Support Grant P30 CA008748 The ESTRO Falcon project team, Scott Kaylor of EduCase, Benjamin Nelms of Proknow for the multi- delineator contour data presented in this work
  • 3. AI in Radiation Oncology 3 Huynh et al. Nat Rev Clin Oncol 2020
  • 4. 4 Netherton et al. Oncology 2021 Hype cycle for three major innovations in radiation oncology Automatable tasks in radiation oncology for the modern clinic
  • 5. Outline  Radiomics - Decision Support Tools • Lung Cancer Screening • Tumor Response Prediction and Evaluation • Aggressive Lung ADC subtype prediction • Multimodal data: Pathology, Multiomics, etc.  Auto Delineation and Variability Analysis • Delineation Variability Quantification • Dosimetric Consequences of Variabilities • OARNet, Probabilistic U-Net 5
  • 6. Radiomics 6  Controllable Feature Analysis  More Interpretable Lambin, et al. Eur J Cancer 2012 Aerts et al., Nature Communications, 2014
  • 7. Radiomics Framework 7 Image Registration • Multi-level rigid • Deformable • Pre/Post-CT • MSE, MI Tumor Segmentation • Adaptive region growing • Level set • Grow cut • Morphology filter • Multi-modality image segmentation Feature Extraction • Intensity distribution • Spatial variations (texture) • Geometric properties • Jacobian feature from DVF • Feature selection Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival Source codes: https://github.com/taznux/radiomics-tools • Automated Workflow (Python) - Integrate all the radiomics components - 3D Slicer, ITK (C++), Matlab, R, and Python - Scalable: support multicore & GPU computing
  • 8. Radiomics Framework 8 Image Registration • Multi-level rigid • Deformable • Pre/Post-CT • MSE, MI Tumor Segmentation • Adaptive region growing • Level set • Grow cut • Morphology filter • Multi-modality image segmentation Feature Extraction • Intensity distribution • Spatial variations (texture) • Geometric properties • Jacobian feature from DVF • Feature selection Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival Source codes: https://github.com/taznux/radiomics-tools Deep Learning Model • Automated Workflow (Python) • Integrate all the radiomics components • 3D Slicer, ITK (C++), Matlab, R, and Python • Scalable: support multicore & GPU computing
  • 9. Radiomics Framework 9 Image Registration • Multi-level rigid • Deformable • Pre/Post-CT • MSE, MI Tumor Segmentation • Adaptive region growing • Level set • Grow cut • Morphology filter • Multi-modality image segmentation Feature Extraction • Intensity distribution • Spatial variations (texture) • Geometric properties • Jacobian feature from DVF • Feature selection Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival Source codes: https://github.com/taznux/radiomics-tools Deep Learning Model • Automated Workflow (Python) • Integrate all the radiomics components • 3D Slicer, ITK (C++), Matlab, R, and Python • Scalable: support multicore & GPU computing
  • 10. Radiomics Framework 10 Image Registration • Multi-level rigid • Deformable • Pre/Post-CT • MSE, MI Tumor Segmentation • Adaptive region growing • Level set • Grow cut • Morphology filter • Multi-modality image segmentation Feature Extraction • Intensity distribution • Spatial variations (texture) • Geometric properties • Jacobian feature from DVF • Feature selection Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival Source codes: https://github.com/taznux/radiomics-tools Deep Learning Model • Automated Workflow (Python) • Integrate all the radiomics components • 3D Slicer, ITK (C++), Matlab, R, and Python • Scalable: support multicore & GPU computing
  • 11. Radiomics Framework 11 Image Registration • Multi-level rigid • Deformable • Pre/Post-CT • MSE, MI Tumor Segmentation • Adaptive region growing • Level set • Grow cut • Morphology filter • Multi-modality image segmentation Feature Extraction • Intensity distribution • Spatial variations (texture) • Geometric properties • Jacobian feature from DVF • Feature selection Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival Source codes: https://github.com/taznux/radiomics-tools Deep Learning Model • Automated Workflow (Python) • Integrate all the radiomics components • 3D Slicer, ITK (C++), Matlab, R, and Python • Scalable: support multicore & GPU computing
  • 12. Radiomics Framework 12 Image Registration • Deep Learning Optical fl ow • Action like flow • Differential warp • Dynamic filtering • … Tumor Segmentation • U-Net • Prob. U-Net • UANet • … Feature Extraction • AlexNet • ResNet • VGG • LeNet • …. Predictive Model • ROC analyses • Prediction models • Validation • Tumor response • Recurrence • Survival • Automated Workflow (Python) • Integrate all the radiomics components • 3D Slicer, ITK (C++), Matlab, R, and Python • Scalable: support multicore & GPU computing
  • 13. Lung Cancer Screening 13  Early detection of lung cancer by LDCT can reduce mortality  Known features correlated with PN malignancy  Size, growth rate (Lung-RADS)  Calcification, enhancement, solidity → texture features  Boundary margins (spiculation, lobulation), attachment → shape and appearance features Malignant nodules Benign nodules Size Total Malignancy < 4mm 2038 0% 4-7 mm 1034 1% 8-20 mm 268 15% > 20 mm 16 75%
  • 14. ACR Lung-RADS Category Baseline Screening Malignancy 1 No PNs; PNs with calcification Negative <1% chance of malignancy 2 Solid/part-solid: <6 mm GGN: <20 mm Benign appearance <1% chance of malignancy 3 Solid: ≥6 to <8 mm Part-solid: ≥6 mm with solid component <6 mm GGN: ≥20 mm Probably benign 1-2% chance of malignancy 4A Solid: ≥8 to <15 mm Part-solid: ≥8 mm with solid component ≥6 and <8 mm Suspicious 5-15% chance of malignancy 4B Solid: ≥15 mm Part-solid: Solid component ≥8 mm >15% chance of malignancy 4X Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph nodes) or suspicious imaging findings (e.g. spiculation) >15% chance of malignancy 14 ACR: American College of Radiology Lung-RADS: Lung CT Screening Reporting and Data System
  • 15. Lung Cancer Screening (Methodology) • TCIA LIDC-IDRI public data set (n=1,010) • Multi-institutional data • 72 cases evaluated (31 benign and 41 malignant cases) • Consensus contour 15 GLCM GLRM Texture features Intensity features 2D Shape features 3D
  • 16. Lung Cancer Screening (SVM-LASSO Model ) 16 SVM classification Distinctive feature identification Malignant? Predicted malignancy Feature extraction Yes 10x10-fold CV 10-fold CV LASSO feature selection • Size (BB_AP) : Highly correlated with the axial longest diameter and its perpendicular diameter (r = 0.96, larger – more malignant) • Texture (SD_IDM) : Tumor heterogeneity (smaller – more malignant)
  • 17. Lung Screening (Results: Comparison) Sensitivity Specificity Accuracy AUC Lung-RADS Clinical guideline 73.3% 70.4% 72.2% 0.74 Hawkins et al. (2016) Radiomics – 23 features 51.7 % 92.9% 80.0% 0.83 Ma et al. (2016) Radiomics – 583 features 80.0% 85.5% 82.7% Buty et al. (2016) DL – 400 SH and 4096 AlexNet features 82.4% Kumar et al. (2015) DL: 5000 features 79.1% 76.1% 77.5% Proposed Radiomics: two features (Size and Texture) 87.2% 81.2% 84.6% 0.89 17 DL: Deep Learning, SH: Spherical Harmonics Choi et al., Medical Physics, 2018.
  • 18. ACR Lung-RADS Category Baseline Screening Malignancy 1 No PNs; PNs with calcification Negative <1% chance of malignancy 2 Solid/part-solid: <6 mm GGN: <20 mm Benign appearance <1% chance of malignancy 3 Solid: ≥6 to <8 mm Part-solid: ≥6 mm with solid component <6 mm GGN: ≥20 mm Probably benign 1-2% chance of malignancy 4A Solid: ≥8 to <15 mm Part-solid: ≥8 mm with solid component ≥6 and <8 mm Suspicious 5-15% chance of malignancy 4B Solid: ≥15 mm Part-solid: Solid component ≥8 mm >15% chance of malignancy 4X Category 3 or 4 PNs with suspicious features (e.g. enlarged lymph nodes) or suspicious imaging findings (e.g. spiculation) >15% chance of malignancy 18 ACR: American College of Radiology Lung-RADS: Lung CT Screening Reporting and Data System
  • 19. Spiculation Quantification (Motivation) • Blind Radiomics • Semantic Features • Semi-automatic Segmentation - GrowCut and LevelSet 19 Radiologists spiculation score (RS) for different pulmonary nodules 1 2 3 4 5 Choi et al. in CMPB 2021
  • 20. Spiculation Quantification (Methodology) 20 𝜖𝑖: = log 𝑗,𝑘 𝐴 ([𝜙(𝑣𝑖), 𝜙(𝑣𝑗), 𝜙(𝑣𝑘)]) 𝑗,𝑘 𝐴 ([𝑣𝑖, 𝑣𝑗, 𝑣𝑘]) Area Distortion Map Spherical Mapping Eigenfunction
  • 21. Spiculation Quantification (Results) 21 RS 1 2 3 4 5 s1 -0.2 -0.7 -0.6 -1.1 -1.8 s2 0.1 0.2 0.4 0.6 1.6 Ns 0 1 4 8 14 Nl 2 0 3 1 1 Na 1 14 4 0 1 Spiculation quantification and attachment detection via area distortion metric. The results of spiculation detection (red line: baseline of peak, black line: medial axis of peak, red X: spiculation, black X: lobulation, yellow X: attached peaks). Radiologist’s spiculation score (RS), and the proposed interpretable spiculation features ( s1 : spiculation score, s2 : spiculation score, Ns : no. of spiculation, Nl : no. of lobulation and Na : no. of attached peaks) are shown below.
  • 22. Spiculation Quantification (Results: Comparison) 22 Choi et al. in CMPB 2021
  • 23. Progression-free survival Prediction after SBRT for early-stage NSCLC 23 Thor, Choi et al. ASTRO 2020 • 412 patients treated between 2006 and 2017 • PETs and CTs within three months prior to SBRT start. • The median prescription dose was 50Gy in 5 fractions.
  • 24. Progression-free survival Prediction (Results) • PET entropy, CT number of peaks, CT major axis, and gender. • The most frequently selected model included PET entropy and CT number of peaks - The c-index in the validation subset was 0.77 - The prediction-stratified survival indicated a clear separation between the observed HR and LR - e.g. a PFS of 60% was observed at 12 months in HR vs. 22 months in LR. 24 Thor, Choi et al. ASTRO 2020
  • 25. Local tumor morphological changes 25 Jacobian Map - Jacobian matrix: calculates rate of displacement change in each direction. - Determinant indicates volumetric ratio of shrinkage/expansion. 𝐷𝑒𝑡 𝐽 = 𝐷𝑒𝑡 𝐽 > 1 volume expansion 𝐷𝑒𝑡 𝐽 = 1 no volume change 𝐷𝑒𝑡 𝐽 < 1 volume shrinkage 𝐷𝑒𝑡 𝐽 = 1.2 = 20% expansion 𝐷𝑒𝑡 𝐽 = 0.8 = 20% shrinkage (-20%) Riyahi, Choi et al., PMB 2018
  • 26. Local tumor morphological changes (Results) 26
  • 27. Local tumor morphological changes (Results) Features P-value AUC Correlation to responders Minimum Jacobian 0.009 0.98 -0.79 Median Jacobian 0.046 0.95 -0.72 The P-value, AUC and correlation to responders for all significant features in univariate analysis 27 Riyahi, Choi et al., PMB 2018 SVM-LASSO: AUC 0.91
  • 28. Aggressive Lung ADC Subtype Prediction (Motivation) 28 CT MIP PET/CT Soild CT PET/CT Five classifications of lung ADC Travis et al. JTO 2011  Solid and MIP components: poor surgery/SBRT prognosis factor  Benefit from lobectomy rather than limited resection  Core biopsy (Leeman et al. IJROBP 2017)  Minimally invasive, not routinely performed, sampling error (about 60% agreement with pathology)  Preoperative diagnostic CT and FDG PET/CT radiomics  Non-invasive and routinely performed
  • 29. Aggressive Lung ADC Subtype Prediction (Method & Results) • Retrospectively enrolled 120 patients - Stage I lung ADC, ≤2cm - Preoperative diagnostic CT and FDG PET/CT • Histopathologic endpoint - Aggressiveness (Solid : 18 cases, MIP : 5 cases) • 206 radiomic features & 14 clinical parameters • SVM-LASSO model 29 Performance of the SVM-LASSO model to predict aggressive lung ADC Choi et al. Manuscript under review Box plots of SUVmax (FDR q=0.004) and PET Mean of Cluster Shade (q=0.002) Feature Sensitivity Specificity PPV NPV Accuracy AUC Conventional SUVmax 57.8±4.6% 78.5±1.4% 39.2±2.3% 88.6±1.1% 74.5±1.4% 0.64±0.01 SVM-LASSO PET Mean of Cluster Shade 67.4±3.1% 86.0±1.1% 53.7±2.1% 91.7±1.0% 82.4±1.0% 0.78±0.01 p-value SUVmax vs. SVM-LASSO 0.002 1e-5 7e-8 3e-5 5e-8 0.03
  • 30. Unsupervised Learning of Deep Learned Features from Breast Cancer Images 30 Lee, Choi et al. IEEE BIBE 2020
  • 31. Unsupervised Learning of Deep Learned Features 31 Slide name Silhouette optimal number Cluster set Accuracy F1-score TCGA-A7-A0DA 29 [25, 22, 6, 2, 24, 14, 0, 20, 10] 0.8829 0.8929 TCGA-A2-A0YM 20 [7, 6, 1, 5] 0.8360 0.8863 TCGA-A2-A3XT 19 [13, 10, 5, 1, 2] 0.9316 0.9514 TCGA-BH-A0BG 8 [1, 5] 0.7828 0.6857 TCGA-E2-A1LS 26 [18, 5] 0.8495 0.8680 TCGA-OL-A66I 25 [7, 18, 12, 20, 1, 0] 0.7761 0.7091 TCGA-C8-A26Y 21 [9, 18, 12, 16] 0.8594 0.8122 Lee, Choi et al. IEEE BIBE 2020
  • 32. PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma 32 Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN. • CNNs have achieved great success • A lack of interpretability remains a key barrier • Moreover, because biological array data are generally represented in a non-grid structured format • PathCNN An interpretable CNN model on integrated multi-omics data using a newly defined pathway image.
  • 33. PathCNN: interpretable CNNs (results) 33 Cancer PathCNN Logistic regression SVM with RBF Neural network MiNet GBM 0.755 ± 0.009 0.668 ± 0.039 0.685 ± 0.037 0.692 ± 0.030 0.690 ± 0.032 LGG 0.877 ± 0.007 0.816 ± 0.036 0.884 ± 0.017 0.791 ± 0.031 0.854 ± 0.027 LUAD 0.637 ± 0.014 0.581 ± 0.028 0.624 ± 0.034 0.573 ± 0.031 0.597 ± 0.042 KIRC 0.709 ± 0.009 0.654 ± 0.034 0.684 ± 0.027 0.702 ± 0.028 0.659 ± 0.030 Comparison of predictive performance with benchmark methods in terms of the area under the curve (AUC: mean ± standard deviation) over 30 iterations of the 5-fold cross validation Note: AUCs for PathCNN were obtained with three principal components. Bold = Highest AUC for each dataset. SVM, support vector machine; RBF, radial basis function; MiNet, Multi-omics Integrative Net; GBM, glioblastoma multiforme; LGG, low-grade glioma; LUAD, lung adenocarcinoma; KIRC, kidney cancer. Oh, Choi et al. Bioinformatics 2021, joint first author Source code: https://github.com/mskspi/PathCNN.
  • 34. Outline  Radiomics - Decision Support Tools • Lung Cancer Screening • Tumor Response Prediction and Evaluation • Aggressive Lung ADC subtype prediction • Multimodal data: Pathology, Multiomics, etc.  Auto Delineation and Variability Analysis • Delineation Variability Quantification • Dosimetric Consequences of Variabilities • OARNet, Probabilistic U-Net 34
  • 35. Delineation Variability Quantification and Simulation A framework for radiation therapy variability analysis 35 RT plan Structure Set CT Image Dose Distribution Structure Sets DV simulation ASSD GrowCut RW Other delineators SV analysis DV analysis Geometric Dosimetric Variability analysis Human DV Simulated DV Consensus SS OARNet Choi et al., AAPM, 2019.
  • 36. Delineation Variability Quantification and Simulation • ESTRO Falcon contour workshop (EduCase) - A HNC case, Larynx, 70 Gy and 35 fractions - 14 independent manually delineated (MD) OAR structure sets (SS) - BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, SpinalCord, and Thyroid • Consensus MD SS - The simultaneous truth and performance level estimation (STAPLE) 36 Choi, Nourzadeh et al., AAPM, 2019.
  • 37. Delineation Variability Quantification and Simulation (Methods) •Geometric analysis - Similarity: Dice coefficient (Volumetric, Surface) - Distance: Hausdorff distance (HD), Actual Average Surface Distance (AASD) - Reference: STAPLE SS •Dosimetric analysis - Single dose distribution planned from a human SS - DVH confidence bands (90%tile) - 𝐷mean, 𝐷max, 𝐷min, 𝐷50 37 Choi, Nourzadeh et al., AAPM, 2019.
  • 38. Delineation Variability Quantification and Simulation (Results) • DVH variability not predicted by geometric measures • Large human variability 38 100% 50% 0% 100% 50% 0% Human ASSD GrowCut RW Right Parotid Left Parotid Choi, Nourzadeh et al., AAPM, 2019.
  • 39. Human ASSD GrowCut RW 100% 50% 0% Education 100% 50% 0% Clinic 39
  • 40. Dose Volume Coverage Map (DVCM) 40 Human ASSD GrowCut RW
  • 41. • Plan Competition Data Set: A HNC case, Nasopharynx , 70 Gy • PV: 409 plans for IMRT, VMAT, and Tomotherapy on various TPS Eclipse, Monaco , Pinnacle, RayStation, Tomotherapy • SV: Setup error simulation using Radiation Therapy Robustness Analyzer (RTRA) • 1000 simulations: 3mm translation and 5-degree rotation • DV: 5 manually delineated (MD) SSs • BrainStem, Esophagus, OralCavity, Parotid_L, Parotid_R, and SpinalCord • 3x5 Simulated DVs using Radiation Therapy Variability Analyzer (RTVA) Variability Analysis Plan Variability, Setup Variability, Delineation Variability 41 Choi, Nourzadeh et al., AAPM, 2020.
  • 42. Dose Volume Coverage Map (DVCM) 42 Plan Variability Setup Variability Delineation Variability Choi, Nourzadeh et al., AAPM, 2020.
  • 43. Plan Competition DVCM Analysis 43 Fractional Volume Dose OAR Dose Constraint Prob Threshold 5%
  • 44. Plan Competition DVCM Analysis 44 TV SV×PV SV×DV PV×DV SV PV DV Average BrainStem 0.15 0.11 0.10 0.07 0.03 0.01 0.02 0.07 >0.95 Chiasm 0.68 0.00 0.65 0.56 0.00 0.00 0.49 0.34 Eye_L 0.07 0.05 0.04 0.01 0.02 0.01 0.01 0.03 Eye_R 0.02 0.01 0.01 0.01 0.00 0.00 0.01 0.01 >0.5 Lens_L 1.00 1.00 1.00 1.00 0.99 1.00 0.01 0.86 Lens_R 1.00 1.00 1.00 1.00 1.00 1.00 0.01 0.86 >0.3 Mandible 0.04 0.04 0.00 0.07 0.00 0.08 0.01 0.03 >0.2 OpticNerve_L 0.51 0.42 0.43 0.36 0.17 0.00 0.34 0.32 >0.05 OpticNerve_R 0.56 0.38 0.61 0.38 0.20 0.00 0.39 0.36 ≤0.05 SpinalCord 0.13 0.12 0.05 0.05 0.04 0.01 0.02 0.06 =0.00 Average 0.42 0.31 0.39 0.35 0.25 0.21 0.13 0.29 Fractional volume affected by different variations when constraint failing probability > 5% (worst-case scenario) Choi, Nourzadeh et al., AAPM, 2020.
  • 45. OARNet: auto-delineate organs-at-risk (OARs) in head and neck (H&N) CT image 45 Soomro, Nourzadeh, Choi et al., AAPM, 2020.
  • 47. A Probabilistic U-Net for Segmentation of Ambiguous Images Kohl et al. NeurIPS 2018 47
  • 48. Segmentations on different Variability levels (middle 5) and their occupancy map • Implemented the model using PyTorch • Model trained using TCIA LIDC dataset (about 2000 nodules with up to 4 radiologists delineations) • Titan Xp, 12GB (1 week for training) • Unstable to train, 2D segmentation, tumor A Probabilistic U-Net (Results) 48 Variability Low High
  • 49. A Probabilistic U-Net for Segmentation of Ambiguous Images Wasserstein distance? 2D → 2.5D → 3D 49
  • 50. Summary  Radiomics - Decision Support Tools • Lung Cancer Screening • Tumor Response Prediction and Evaluation • Aggressive Lung ADC subtype prediction • Multimodal data: Pathology, Multiomics, etc.  Auto Delineation and Variability Analysis • Delineation Variability Quantification • Dosimetric Consequences of Variabilities • OARNet, Probabilistic U-Net 50
  • 51. Short-term Future Works • Human-Variability aware auto-delineation • Variability quantification and simulation using generative models • OARNet + Probabilistic U-Net → Probabilistic OARNet • Develop interpretable radiomic features • Improve spiculation quantification • Multi-institution validation • Integrate the radiomics framework into TPS • Eclipse (C#), MIM (Python), RayStation (Python) 51
  • 52. Long-term Future Works • Comprehensive Framework for Cancer Imaging • Multi-modal imaging • Response prediction and evaluation (Pre, Mid, and Post) • Longitudinal analysis of tumor change • Shape analysis (e.g. Spiculation) • Deep learning models • Automation of Clinical Workflow • Big Data Analytics: EMR, PACS, ROIS, Genomics, etc. • Provide an informatics platform for comprehensive cancer therapy 52
  • 53. Selected Publications 1. Jung Hun Oh*, Wookjin Choi* et al., “PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma”, Bioinformatics, 2021, *joint first author 2. Wookjin Choi et al., “ Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening”, Computer Methods and Programs in Biomedicine”, 2021 3. Noemi Garau, Wookjin Choi, et al., “ External validation of radiomics‐based predictive models in low‐dose CT screening for early lung cancer diagnosis”, Medical Physics, 2020 4. Jiahui Wang, Wookjin Choi et al., “Prediction of anal cancer recurrence after chemoradiotherapy using quantitative image features extracted from serial 18F-FDG PET/CT”, Frontiers in oncology, 2019 5. Wookjin Choi et al., “Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer”, Medical Physics, 2018 6. Sadegh Riyahi, Wookjin Choi, et al., “Quantifying local tumor morphological changes with Jacobian map for prediction of pathologic tumor response to chemo-radiotherapy in locally advanced esophageal cancer”, Physics in Medicine and Biology, 2018 7. Shan Tan, Laquan Li, Wookjin Choi, et al., “Adaptive region-growing with maximum curvature strategy for tumor segmentation in 18F-FDG PET”, Physics in Medicine and Biology, 2017 8. Wookjin Choi et al., “Individually Optimized Contrast-Enhanced 4D-CT for Radiotherapy Simulation in Pancreatic Ductal Adenocarcinoma”, Medical Physics, 2016 9. Wookjin Choi, Tae-Sun Choi, “Automated Pulmonary Nodule Detection based on Three-dimensional Shape-based Feature Descriptor”, Computer Methods and Programs in Biomedicine, 2014 53 Complete list of publications: https://scholar.google.com/citations?user=iHgsGLUAAAAJ
  • 54. 54 Thank You! Q & A https://qradiomics.wordpress.com E-mail: wchoi@vsu.edu
  • 55. Spiculation Quantification: Height, Area Distortion • Area distortion better than solid angle for non-conic spicules • Height is medial axis length, can be perpendicular distance • Width is measured at base, can be FWHM 𝑠1 = 𝑖 mean 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖 𝑖 ℎ𝑝 𝑖 𝑠2 = 𝑖 min 𝜖𝑝 𝑖 ∗ ℎ𝑝 𝑖 𝑖 ℎ𝑝 𝑖 55
  • 57. Local tumor morphological changes (Motivation) 57
  • 58. Delineation Variability Simulation (Methods) • DV Simulation using auto-delineation (AD) methods (σ=2, 5, 10 mm) • Average surface of standard deviation (ASSD): random perturbation • GrowCut: cellular automata region growing • Random walker (RW): probabilistic segmentation 58 Background 0 Foreground 1 Initial Binary Mask Gaussian-Smoothed Mask Gaussian Noises-Added Mask Intensity Spatial location Inside Outside σ = 2mm σ = 5mm σ = 8mm σ = 10mm Choi et al., AAPM, 2019.
  • 59. OARNet comparisons 59 (a) Dice similarity coefficient and (b) Hausdorff comparison for the alternative delineation methods. The points in the graphs are mean values and bars show the 95% confidence intervals.
  • 60. 60 A matrix of adjusted P-values.The row represents the 146 KEGG pathways ordered on pathway images. The columns represent the first two principal components of each omics type.The red color indicates key pathways with adjusted P-values < 0.001

Editor's Notes

  1. Thank you for coming today! It’s an honor to have the opportunity to share my research here TJU. I’m going to talk about -
  2. I would like to thank everyone who has helped me in the projects
  3. a general overview of the radiation therapy workflow with brief descriptions of expected applications of artificial intelligence (AI) at each step. The workflow begins with the decision to treat the patient with radiation therapy, followed by a simulation appointment during which medical images are acquired for treatment planning. Subsequently, the patient-specific treatment plan is created, and then the plan is subjected to approval, review and quality assurance (QA) measures prior to delivery of radiation to the patient. The patient then receives follow-up care. AI has the potential to improve radiation therapy for patients with cancer by increasing efficiency for the staff involved, improving the quality of treatments, and providing additional clinical information and predictions of treatment response to assist and improve clinical decision-making.
  4. (triangle: Monte Carlo; square: Inverse optimization/IMRT; circle: deep learning-based contouring). The curve depicts expectations by the target audience (those in radiation oncology and medical physics) as a function of time. Yellow, magenta, cyan, green, and blue portions of the curve denote “innovation trigger,” “peak of inflated expectations,” “trough of disillusionment,” “slope of enlightenment,” and “productivity plateau” regions, respectively. Automatable tasks in radiation oncology for the modern clinic. The extent to which each skill set is used or task is performed in this figure is not indicated and may be dependent on each clinical practice. In order to group essential tasks performed during the treatment planning process, “Physical,” “Knowledge,” and “Social” skill domains were created and are indicated by green, magenta, and blue ellipses, respectively. Skills or tasks are indicated by circles within each colored domain and may be shared between domains. Based on works cited in this review, tasks which may be automated are within the “Automatable” domain.
  5. How to generalize it
  6. A large number of image features from medical images additional information that has prognostic value
  7. I open sample automated workflow and essential components to public
  8. I open sample automated workflow and essential components to public
  9. I open sample automated workflow and essential components to public
  10. I open sample automated workflow and essential components to public
  11. I open sample automated workflow and essential components to public
  12. I open sample automated workflow and essential components to public
  13. Frequent use of LDCT increased number of indeterminate PNs Prediction of PN malignancy is important
  14. The Lung Imaging Reporting and Data System (Lung-RADS) was developed by the American College of Radiology (ACR) to standardize the screening of lung cancer on CT images. As shown in the Table, the Lung-RADS categorization is mainly based on PN size (the average of the longest and shortest diameters on axial slice) with some consideration to calcification, appearance type (solid, part-solid, and non-solid or ground glass nodule/GGN), and additional suspicious features. We also performed Lung-RADS categorization based on the PN contour and the physician’s annotations. To match the original LIDC-IDRI diagnosis, categories 3 and lower are labeled as benign and category 4 (4A, 4B, and 4X) as malignant.
  15. 79 LDCT scans: 36 benign and 43 malignant cases, 7 missing contours We performed Lung-RADS categorization based on the PN contour and the physician’s annotations. Having diagnosis data 157 Primary cancer 43 -> 41 biopsy-proven, progression Benign 36 -> 31 biopsy-proven, 2yrs of stable PN, progression Metastatic cancer or unknown 78
  16. To increase interpretability, need concise model with minimum number of features
  17. The proposed method showed comparable or better accuracy than others, Better than deep learning with two features
  18. The Lung Imaging Reporting and Data System (Lung-RADS) was developed by the American College of Radiology (ACR) to standardize the screening of lung cancer on CT images. As shown in the Table, the Lung-RADS categorization is mainly based on PN size (the average of the longest and shortest diameters on axial slice) with some consideration to calcification, appearance type (solid, part-solid, and non-solid or ground glass nodule/GGN), and additional suspicious features. We also performed Lung-RADS categorization based on the PN contour and the physician’s annotations. To match the original LIDC-IDRI diagnosis, categories 3 and lower are labeled as benign and category 4 (4A, 4B, and 4X) as malignant.
  19. Spiculations are spikes on the surface of PN and are important predictors of malignancy in lung cancer