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

Despite advances in neurosurgical techniques, radiation therapy, and chemotherapeutic regimens, the prognosis for patients remains poor, with recurrence rates exceeding 70% within two years after surgical resection [70]. The high probability of early recurrence underscores the critical need for robust, patient-specific prognostic tools that can support personalized clinical decision-making. Accurate prediction of tumor recurrence is crucial for stratifying patients into appropriate risk categories, guiding adjuvant therapy choices, and optimizing postoperative surveillance [71]. Traditionally, recurrence risk assessments are based on clinical judgment informed by factors such as tumor size, histological grade, and extent of resection. However, these assessments often lack the granularity and precision needed for an individualized prognosis, particularly given the heterogeneity of tumor biology and interpatient variability [72]. Recent advances in multimodal machine learning (ML) have demonstrated the potential to address these challenges by integrating diverse data sources, as evidenced by emerging frameworks that leverage collaborative model optimization [73-75] and reward-driven paradigms for multimodal fusion [76,77]. Structural magnetic resonance imaging (MRI), especially contrast-enhanced T1-weighted imaging, is routinely employed in brain tumor diagnosis and follow-up, providing insights into tumor morphology and contrast uptake behavior [78].