Development and validation of a preoperative magnetic resonance imaging-based (MRI)and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective analysis

Yuxiang Sun ^1,2 Junpeng Xu ² Dongsheng Kong ² Yu Zhang ³ Qijia Wu ² Liqin Wei ² Zihao Zhu ¹ Shiyu Feng ^2* chunhui Li ^1* chunhui Li ^1*

• ¹ Affiliated Hospital of Hebei University, Baoding, Hebei Province, China
• ² The First Medical Center of Chinese PLA General Hospital, Beijing, China
• ³ Department of Neurosurgery, Xuanwu Hospital, Xiongan, China

BACKGROUND:Accurate preoperative identification of intracranial glioblastoma (GB), primary central nervous system lymphoma (PCNSL), and brain metastases (BM) is crucial for determining the appropriate treatment strategy.We aimed to develop and validate the utility of preoperative magnetic resonance imaging-based radiomics and machine learning models for the noninvasive identification them.STUDY TYPE:Retrospective.POPULATION:We included 202 patients, including 71 GB, 59 PCNSL, and 72 BM,randomly divided into a training cohort (n ＝141) and a validation cohort (n = 61).FIELD STRENGTH/SEQUENCE:Axial T2-weighted fast spin-echo sequence (T2WI) and contrast-enhanced T1-weighted spin-echo sequence (CE-T1WI) using 1.5-T and 3.0-T scanners.ASSESSMENT:We extracted radiomics features from the T2 sequence and CE-T1 sequence separately. Then, we applied the F-test and recursive feature elimination (RFE) to reduce the dimensionality for both individual sequences and the combined sequence CE-T1 combined with T2.The support vector machine (SVM), k-nearest neighbor (KNN), and naive Bayes classifier (NBC) were used in model development. STATISTICAL TESTS:Chi-square test, one-way analysis of variance, and Kruskal-Wallis test were performed. The P values <0.05 were considered statistically significant.erformance was evaluated using AUC, sensitivity, specificity, and accuracy metrics.Result:The SVM model exhibited superior diagnostic performance with macro-average AUC values of 0.91 for CE-T1 alone, 0.86 for T2 alone, and 0.93 for combined CE-T1 and T2 sequences. And the combined sequence model demonstrated the best overall accuracy, sensitivity, and F1 score, with an accuracy of 0.77, outperforming both KNN and NBC models.The SVM-based MRI radiomics model effectively distinguishes between GB, PCNSL, and BM. Combining CE-T1 and T2 sequences significantly enhances classification performance, providing a robust, noninvasive diagnostic tool that could assist in treatment planning and improve patient outcomes.