mlsanityData to 7 Oct 2026

Multi-Modal Machine Learning Framework for Predicting Early Recurrence of Brain Tumors Using MRI and Clinical Biomarkers

arXiv 2509.01161 ↗First posted 1 Sep 2025cs.LGcited by 0

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Abstract (arXiv)

Accurately predicting early recurrence in brain tumor patients following surgical resection remains a clinical challenge. This study proposes a multi-modal machine learning framework that integrates structural MRI features with clinical biomarkers to improve postoperative recurrence prediction. We employ four machine learning algorithms -- Gradient Boosting Machine (GBM), Random Survival Forest (RSF), CoxBoost, and XGBoost -- and validate model performance using concordance index (C-index), time-dependent AUC, calibration curves, and decision curve analysis. Our model demonstrates promising performance, offering a potential tool for risk stratification and personalized follow-up planning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 60 of its 110 references that are in the map. First posted on arXiv 1 Sep 2025.

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60 of 110 references.

References · 60

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38 more
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