Lung CT‐based multi‐lesion radiomic model to differentiate between nontuberculous mycobacteria and Mycobacterium tuberculosis

Abstract Background Nontuberculous mycobacterial lung disease (NTM‐LD) and Mycobacterium tuberculosis lung disease (MTB‐LD) are difficult to distinguish based on conventional imaging examinations. In recent years, radiomics has been used to discriminate them. However, existing radiomic methods mainly focus on specific lesion types, and have limitations in handling the presence of multiple lesion types that vary among different patients. Purpose We aimed to establish a radiomic model based on multiple lesion types in the patient's CT scans, and analyzed the importance of different lesion types in distinguishing the two diseases. Methods 120 NTM‐LD and 120 MTB‐LD patients were retrospectively enrolled in this study and randomly split into the training (168) and testing (72) sets. A total of 1037 radiomic features were extracted separately for each lesion type. The univariate analysis, least absolute shrinkage, and selection operator were used to select the significant radiomic features. The radiomic signature score (Radscore) from each lesion type was estimated and aggregated to construct the multi‐lesion feature vector for each patient. A multi‐lesion radiomic (MLR) model was then established using the random forest classifier, which can estimate importance coefficients for different lesion types. The performances of the MLR model and single radomic models were investigated by the receiver operating characteristic curve (ROC). The impact of the predicted lesion importance was also evaluated in subjective imaging diagnosis. Results The MLR model achieved an area under the curve (AUC) of 90.2% (95% CI: 86.2% 94.1%) in differentiating NTM‐LD and MTB‐LD, outperforming the models using specific lesion types following existing radiomic models by 1% to 13%. Among different lesion types, tree‐in‐bud pattern demonstrated the highest distinguishing value, followed by consolidation, nodules, and lymph node enlargement. Given the estimated lesion importance, two senior radiologists exhibited improved accuracy in diagnosis, with an increased accuracy of 8.33% and 8.34%, respectively. Conclusions This is the first radiomic study to use multiple lesion types to distinguish NTM‐LD and MTB‐LD. The developed MLR model performed well in differentiating the two diseases, and the lesion types with high importance exhibited the potential to assist experienced radiologists in clinical decision‐making.