RATIONALE AND OBJECTIVES Cerebral tuberculomas (CTB) and lung cancer brain metastases (BM) frequently show overlapping appearances on conventional magnetic resonance imaging (MRI) but require entirely different treatment strategies. This study aimed to develop and validate a machine learning model integrating clinical and contrast-enhanced MRI features to noninvasively differentiate these two entities. MATERIALS AND METHODS The internal dataset (n = 188, 122 CTB, 66 BM) was split into training/validation (n = 131) and internal test (n = 57) sets. Feature selection (univariate and stepwise logistic regression) was performed only on the training and validation set, yielding four predictors. Machine learning classifiers were trained using these predictors with repeated 5-fold cross-validation. An external test set (n = 51, 26 CTB, 25 BM) from another hospital was used for independent validation. RESULTS Multivariate logistic regression retained four independent predictors: age (OR = 1.046, 95%CI: 1.003-1.091, p = 0.036), carcinoembryonic antigen (CEA) (odds ratios [OR] = 1.711, 95%confidence intervals [CI]: 1.235-2.370, p = 0.001), multiple lesions (OR = 0.080, 95%CI: 0.015-0.438, p = 0.004), and ring with mural nodule (OR = 11.289, 95%CI: 2.551-49.952, p = 0.001). The Gradient Boosting model built on these four features achieved the best performance, with an area under the receiver operating characteristic curve of 0.970 (95%CI: 0.925-0.998) on the internal test set and 0.957 (95%CI: 0.900-0.995) on the external test set. On the external test set, the model showed a sensitivity of 88.0% and a specificity of 92.3%. CONCLUSION A Gradient Boosting model integrating age, CEA, multiple lesions, and ring with mural nodule effectively distinguishes CTB from BM with robust external test performance, providing a noninvasive tool to assist clinical decision-making.
