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

Abstract

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 model according to the receiver operating characteristic (ROC) curve and decision curve for the internal cohort. The external validation cohort had less optimal but still robust performance (AUC: 0.7174, 95% CI: 0.6172–0.8024). The ERSS model with integration of multiple classifiers, including elastic net, random forest and support vector machine, produced robust predictive performance and outperformed the LR method.

Conclusion

The results suggested that the integrated machine learning model, i.e., the ERSS model, had the potential for efficient and accurate preoperative differentiation of BM from GBM, which may improve clinical decision-making and outcomes of patients with brain tumors.