Radiomic phenotyping of tuberculosis histopathology from computed tomography scans of non-human primates

Abstract Tuberculosis (TB) lesions display structural heterogeneity associated with disease status and progression. We explore the potential of radiomic features from computed tomography (CT) scans to classify TB lesions at a microscopic level. Treated marmosets and untreated macaques infected with Mycobacterium tuberculosis underwent PET-CT imaging after a minimum of 12 weeks. Lesions were classified post-necropsy into necrotizing granulomas, non-necrotizing granulomas, inflammation beyond defined granulomas, and fibrotic granulomas. CT scans were segmented, radiomic features were extracted, and the top ten features were used to develop machine learning (ML) models for classification based on a one-versus-all approach. Top performing models were evaluated on a separate validation dataset. 151 lesions from macaques and 149 lesions from marmosets were identified. Top features included GrayLevelNonUnformity and SmallAreaHighGrayLevelEmphasis for both datasets. Linear discriminate analysis for macaques and adaptive boosting for marmosets averaged AUC-ROCs of 0.66-0.70 for discriminating lesion types, ranging from 0.45-0.52 for necrotizing granulomas to 0.91 for fibrotic granulomas. Radiomics distinguished fibrotic from non-fibrotic TB granulomas in untreated macaques, potentially relevant for identifying disease progression risk. Classification of other lesion types was modest. Despite small, heterogeneous datasets across primate species, models performed consistently, supporting CT radiomics as a promising and trainable tool for non-invasive lesion phenotyping.