Machine Learning Model Using Clinical and Contrast-Enhanced MRI Features Differentiates Cerebral Tuberculoma from Lung Cancer Brain Metastasis.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Hangzhou Red Cross Hospital, Hangzhou, China (A.W., X.Q., Y.H., W.L., Y.W. ). Electronic address: [email protected].
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University(Army Medical University) and Key Laboratory of Tumor Immunopathology, Ministry of Education of China, Chongqing, China (W.D.).
- Department of Radiology,Wenzhou Central Hospital, Affiliated to Wenzhou Medical University, Wenzhou, China (Q.C.).
- Department of Radiology, Hangzhou Red Cross Hospital, Hangzhou, China (A.W., X.Q., Y.H., W.L., Y.W. ).
- Cancer Center,Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, China (J.W.).
- Department of Radiology, Hangzhou Red Cross Hospital, Hangzhou, China (A.W., X.Q., Y.H., W.L., Y.W. ). Electronic address: [email protected].
Abstract
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. 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. 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%. 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.