The document presents Wookjin Choi's research on medical image analysis, focusing on the automatic detection of pulmonary nodules using advanced algorithms and radiomics. It highlights the predictive capabilities of radiomic features in assessing malignancy and clinical outcomes in lung cancer and hepatocellular carcinoma, supported by extensive validation and feature selection techniques. Future work is outlined, emphasizing the development of new radiomic features and their integration with molecular biomarkers for improved lung screening.

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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