Whole-Lung Ct Radiomics-Based Machine Learning Classification of Nontuberculous Mycobacteria Lung Disease Across Geographically Distinct Cohort

BACKGROUND Nontuberculous mycobacterial lung disease (NTM-LD) is highly heterogenous, geographically and etiologically, hindering effective timely identification. Prior CT radiomics studies require manual segmentation of pathology. We developed a whole-lung CT radiomics-based machine learning approach and identified common features across two geographically distinct NTM-LD cohorts. STUDY DESIGN AND METHODS 1,300 chest CT scans from China (871 TB; 429 NTM, Dataset 1) and 173 independent NTM cohort from UAB, US. Whole-lung regions were automatically segmented on each scan, and 85 quantitative radiomic features were extracted using a standardized image-processing pipeline. We evaluated two frameworks to assess model performance and generalizability: (1) training on Dataset 1 with external validation on Dataset 2, and (2) training on the combined cohort. Linear discriminant analysis (LDA) was used as the primary classification method. Cross-cohort concordance analysis was performed to evaluate the reproducibility of radiomic features across datasets. RESULTS In Scenario 1, the LDA classifier trained on Dataset 1 achieved an AUC of 0.79 (95% CI, 0.73-0.84) with high specificity (0.91). On the external UAB cohort, the model achieved an AUC of 0.94 (95% CI, 0.90-0.97). In Scenario 2, the combined cohort model achieved an AUC of 0.81 (95% CI, 0.76-0.85) with improved sensitivity (0.61) and precision (0.82). Feature importance analysis identified 16 features consistently ranked among the top 20 in both scenarios, predominantly texture-based descriptors reflecting distinct parenchymal patterns between mycobacterial species. CONCLUSION Whole-lung CT radiomics enables interpretable NTM-LD classification across geographically distinct populations without manual annotation. Suggesting population-independent parenchymal signatures of NTM-LD.