Lesion-level prediction of local recurrence after stereotactic radiotherapy for brain metastases using machine learning and mixed-effects modeling

Abstract Background The aim of this study was to identify and validate clinically meaningful predictors of local treatment failure (LTF) after stereotactic radiotherapy (SRT) for brain metastases by integrating machine learning (ML)-based feature selection with generalized linear mixed-effects modeling (GLMM). Method The retrospective study included 211 brain metastases from 63 patients treated with SRT. Each lesion was investigated independently, considering the within-patient clustering. Random forest classifiers were trained using a 3-fold cross-validation repeated 100 times to assess predictive performance and feature importance. The predictors included age, lesion length, biological effective dose (BED), sex, post-SRT systemic therapy, Karnofsky Performance Status (KPS), type of primary tumor, and lesion location. Features with high importance were further evaluated using GLMM to determine statistical significance. Receiver operating characteristic (ROC) analysis was performed to determine optimal thresholds and diagnostic accuracy. Results The median duration of follow-up was 232 days, and 31 lesions (14.7%) developed LTF following SRT. The ML model achieved a mean area under the ROC curve (AUC) of 0.88 and an accuracy of 0.84. Variable importance rankings identified age, primary tumor, BED, length, and KPS. The GLMM confirmed associations of lower BED (odds ratio [OR] = 0.89, 95% confidence interval [CI] = 0.80-0.98; P = .023) and greater lesion length (OR = 1.16, 95% CI = 1.08-1.24; P < .001) with LTF. The ROC analysis identified BED ≤60.0 Gy (AUC = 0.72; sensitivity = 0.81; specificity = 0.71) and lesion length ≥19.3 mm (AUC = 0.78; sensitivity = 0.58; specificity = 0.88) as thresholds. Conclusion Lesion length was the most robust predictor of LTF after SRT, with a length ≥19.3 mm indicating increased risk.