1. 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

2. 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. ?

3. 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, > XRays (expected)
To be hosted by Amazon
Emphasis: on Drug Resistance (DR) phenomenon and DR cases

4. The Context: Online Services (experimental)

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

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

7. Methods: Segmentation of Lungs (~NRM anatomy)

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

9. 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

10. Results: Software

11. Results: Study Group

12. Results: Blind Validation Group

13. 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 MK-11.

14. For More Details: Visit our Online Services (experimental)

15. Thank you !