Radiomics for lung cancer screening Radiomics for Hepatocellular Carcinoma (HCC) Radiation Therapy Robust lung texture features

1. Wookjin Choi, PhD
• PhD in Mechatronics: Medical Image Analysis
Automatic detection of pulmonary nodules in lung CT images
(Choi et al. CMPB 2014, Entropy 2013, Information Sciences 2012)
– Lung and nodule auto-segmentation algorithms
– A novel shape feature descriptor for pulmonary nodule
– A genetic programming model for the feature selection and
classification
• Individually optimized contrast-enhanced 4D-CT for radiotherapy
simulation in pancreatic adenocarcinoma (Accepted in Medical
Physics)
• Optimized Contrast Injection for Pulmonary Thromboembolic
Disease
• Inter-Fractional Tumor Motion Analysis Using 4D-CT and CBCT
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2. Radiomics for lung cancer screening
• To determine whether the detected nodules are
malignant or benign
• Malignancy of lung nodules correlates highly with
– Geometrical size, growth rate, margin –> shape and
appearance features
– Calcification, enhancement –> texture features
• Non-invasive, cost effective and able to describe entire
tumor volume
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3. Preliminary results
• A subset of National Lung ScreeningTrial (NLST)
– 285 solitary pulmonary nodule (SPN): 158 malignant and
127 benign nodules
• 10 times 3-fold cross validation of LASSO feature
selection and SVM classification
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66%
68%
70%
72%
74%
76%
78%
1 2 3 4 5 6 7 8 9 10
Accuracy
Feature numbers
Intensity and shape
Radiomics
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
1 2 3 4 5 6 7 8 9 10
AverageAUC
Feature numbers
Intensity and shape
Radiomics

4. Radiomics for Hepatocellular
Carcinoma (HCC) Radiation Therapy
• To predict clinical outcomes after radiation therapy
– Survival, response, local recurrence (LR), liver metastasis (LM), and
distant metastasis (DM)
– 21 HCC pts treated by radiation therapy
• Clinical features and radiomic features from pre treatment CT
images and enhancement map (A-N, P-N,V-N)
A-N P-N V-N
Survival≥9moSurvival<9mo
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5. Preliminary results
Outcome Sensitivity Specificity Accuracy Selected features
Survival 91% 72% 83% Age, PVTatRT, Tstage, ECOG, multiplicity, Nstage
Response 0% 89% 73% ECOG, T_Dose, sex, multiplicity, PVTatRT
LR 36% 85% 66% ECOG, AFP_pre
DM 0 95% 82% AFP_pre
LM 96% 69% 84% Tstage, ECOG, sex, Nstage, AFP_pre, PVTatRT
Clinical features only
Outcome Sensitivity Specificity Accuracy Selected features
Survival 92% 99% 96% Age, PVTatRT, Tstage, sex, ECOG, V_Median, AN_Skewness
Response 100% 95% 96%
Perpendicular Diameter, Longest Length of Bounding Box,
V_Kurtosis, Orientation, Tstage
LR 90% 90% 90%
VN_Median, N_Minimum, Longest Length of Bounding Box,
AFP_pre, ECOG, Elongation, AN_Skewness, N_Skewness,
Orientation, P_Variance
DM 36% 100% 91% AN_Skewness, PN_Maximum
LM 100% 89% 95% Eccentricity, ECOG, Tstage, A_Minimum
Radiomics and clinical features
LASSO feature selection and SVM classification model with 10x10-fold cross validation
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6. Robust lung texture features
• To identify robust features for predicting radiation
induced lung disease with total lung texture analysis
• Clinically useful features for the prediction
– Relatively invariant (robust) to tumor size as well as not
correlated with normal lung volume
– Tumor volumes varied from patient to patient, and even
varied in same patient after or during the treatment
Feature variations with respect to tumor sizeSimulation of different sizes of tumors
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7. (a) Distributions of feature variations for each feature, the red line (5%) is the robustness threshold; (b)
Correlations between each texture feature and the volume of the simulated normal lung without GTV
• Only 11 features were robust.
– All first-order intensity-histogram features (min, max, mean, and median), two
of the GLCM and four of the GLRM features were robust.
• Correlation with normal lung volume
– All robust features were not correlated, but three unrobust features
showed high correlation
• The robust features can be further examined for the prediction of
RILD.
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8. Future works
• New disease specific radiomic features
– Tumor morphological shape changes for the nodule
growth
– Tumor texture changes
– Developed features
• Nodule shape descriptor (Choi et al. CMPB 2014)
• Esophagus wall thickness and asymmetry (Wang et al. SPIE MI
2015)
• Integration of molecular biomarkers and imaging
radiomic features, and find associations between them
for lung screening
• Find robust radiomic features and its standardization
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