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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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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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Preliminary results

• A subset of National Lung Screening Trial (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

66%

68%

70%

72%

74%

76%

78%

1 2 3 4 5 6 7 8 9 10

Accu

racy

Feature numbers

Intensity and shapeRadiomics

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

Aver

age

AU

C

Feature numbers

Intensity and shapeRadiomics

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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

Surv

ival

≥ 9

mo

Surv

ival

< 9

mo

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Preliminary resultsOutcome 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 featuresSurvival 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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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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(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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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