Radiomic‐Based MRI for Classification of Solitary Brain Metastases Subtypes From Primary Lymphoma of the Central Nervous System

Background Differential diagnosis of brain metastases subtype and primary central nervous system lymphoma (PCNSL) is necessary for treatment decisions. The application of machine learning facilitates the classification of brain tumors, but prior investigations into primary lymphoma and brain metastases subtype classification have been limited. Purpose To develop a machine‐learning model to classify PCNSL, brain metastases with primary lung and non‐lung origin. Study Type Retrospective. Population A total of 211 subjects with pathologically confirmed PCNSL or brain metastases (training cohort 168 and testing cohort 43). Field Strength/Sequence A 3.0 T axial contrast‐enhanced T1 ‐weighted spin‐echo inversion recovery sequence ( T1WI‐CE ), axial T2 ‐weighted fluid‐attenuation inversion recovery sequence ( T2FLAIR ) Assessment Several machine‐learning models (support vector machine, random forest, and K‐nearest neighbors) were built with least absolute shrinkage and selection operator (LASSO) using features from T1WI‐CE, T2FLAIR, and clinical. The model with the highest performance in the training cohort was selected to differentiate lesions in the testing cohort. Then, three radiologists conducted a two‐round classification (with and without model reference) using images and clinical information from testing cohorts. Statistical Tests Five‐fold cross‐validation was used for model evaluation and calibration. Model performance was assessed based on sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC). Results Twenty‐five image features were selected by LASSO analysis. Random forest classifier was selected for its highest performance on the training set with an AUC of 0.73. After calibration, this model achieved an accuracy of 0.70 on the testing set. Accuracies of all three radiologists improved under model reference (0.49 vs. 0.70, 0.60 vs. 0.77, 0.58 vs. 0.72, respectively). Data Conclusion The random forest model based on conventional MRI and clinical data can diagnose PCNSL and brain metastases subtypes (lung and non‐lung origin). Model classification can help foster the diagnostic accuracy of specialists and streamline prognostication workflow. Evidence Level 4 Technical Efficacy Stage 2