Utilizing Radiological Images for Predicting Drug Resistance of Lung Tuberculosis Vassili Kovalev, Vitali Liauchuk, Alexander Kalinovsky Institute of Informatics, Belarus Alena Skrahina, Andrei Astrauko, Aleh Tarasau Center for Pulmonology and Tuberculosis, Belarus Alex Rosenthal, Andrei Gabrielian National Inst. of Allergy and Infectious Diseases, NIH, USA

Purpose In a number of countries Lung Tuberculosis (TB) remains a serious problem due to the high incidence of drug resistant cases. Results of our previous study (Kovalev et al., CARS-2013) suggest that there are statistically significant links between the textural (structural) image features and drug resistance status of lung tuberculosis patients. The purpose of this study is to examine the potential utility of CT and X-ray image features for predicting the drug resistance. ?

Context: The Project and Tuberculosis Portal NIH-funded Project (4th year) on creation of free TB resources. Originally established as Belarus TB Portal. Recently: Belarus, Georgia, Moldova, Romania + ... Patient & Lab data, CT, X-ray, full genome of Mycobacterium, etc Currently: 400 pat., > 9000 CT, > 1 000 000 XRays (expected) To be hosted by Amazon Emphasis: on Drug Resistance (DR) phenomenon and DR cases http://tuberculosis.by/

The Context: Online Services (experimental) http://imlab.grid.by/

Materials: Study Groups TB Patients Study Group (107) Drug Sensitive (41) Drug Resistant (66) Blind Validation Group (62) Males (38) Females (24)

Materials: Typical Image Examples patient 1 patient 2 patient 3 patient 4 KODAK Point of Care 260 2248x2248 pix GE LightSpeed Pro 16 drug Sensitive drug Resistant

Methods: Segmentation of Lungs (~NRM anatomy) http://imlab.grid.by

Methods: Segmentation of Lungs (Pathol. anatomy) 1 2 3 Coarse segmentation based on Ribcage

Methods: General Procedure Preliminary (on Study Group): Calculating IMG descriptors, PCA, testing by one-leave-out Step 0 Training Classifiers on Study Group data: SVM, Logistic Regression, Bayes, KNN Step 1 Validation Group: Calculating IMG descriptors, rotation raw FTR, obtaining PCs Step 2 Predicting Drug Resistance status of blind Validation Group using trained classifiers Step 3 Finalizing: Obtaining Patient IDs, their true resistance status, calculating the accuracy scores Step 4

Results: Software

Results: Study Group

Results: Blind Validation Group

Conclusion The Drug Resistance of Lung Tuberculosis patients can be predicted based on radiological images with the accuracy of up to 70 %. Acknowledgements. This work was funded by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), U.S. Department of Health and Human Services, USA through the CRDF project BOB 1-31055-MK-11.

For More Details: Visit our Online Services (experimental) http://imlab.grid.by/

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