Ensemble learning-based radiomics model for discriminating brain metastasis from glioblastoma

作者：Qi Zeng, Fangxu Jia, Shengming Tang, Haoling He, Yan Fu, Xueying Wang, Jinfan Zhang, Zeming Tan, Haiyun Tang, Jing Wang, Xiaoping Yi, Bihong T. Chen · 发表于：European Journal of Radiology · 年份：2024 · DOI：10.1016/j.ejrad.2024.111900 · 被引用次数：10 · 研究领域：Glioma Diagnosis and Treatment、Radiomics and Machine Learning in Medical Imaging、Brain Metastases and Treatment

OBJECTIVE: Differentiating between brain metastasis (BM) and glioblastoma (GBM) preoperatively is challenging due to their similar imaging features on conventional brain MRI. This study aimed to enhance diagnostic accuracy through a machine learning model based on MRI radiomics data. METHODS: This retrospective study included 235 patients with confirmed solitary BM and 273 patients with GBM. Patients were randomly assigned to the training (n = 356) or the validation (n = 152) cohort. Conventional brain MRI sequences including T1-weighted imaging (T1WI), contrast-enhanced_T1WI, and T2-weighted imaging (T2WI) were acquired. Brain tumors were delineated on all three sequences and segmented. Features were selected from demographic, clinical, and radiomic data. An integrated ensemble machine learning model, i.e., the elastic regression-SVM-SVM model (ERSS) and a multivariable logistic regression (LR) model combining demographic, clinical, and radiomic data were built for predictive modeling. Model efficiency was evaluated using discrimination, calibration, and decision curve analyses. Additionally, external validation was performed using an independent cohort consisting of 47 patients with GBM and 43 patients with isolated BM to assess the ERSS model generalizability. RESULTS: The ERSS model demonstrated more optimal classification performance (AUC: 0.9548, 95% CI: 0.9337-0.9734 in training cohort; AUC: 0.9716, 95% CI: 0.9485-0.9895 in validation cohort) as compared to the LR mode...