From data silos to integrated diagnosis: multimodal machine learning in glioblastoma
TL;DR
Recent advances and emerging translational evidence for clinically oriented MML in glioblastoma is summarized, with particular emphasis on MRI-centered systems that integrate imaging with pathology, selected molecular measurements, and longitudinal clinical context.
Abstract
Effective glioblastoma care requires integrating multiparametric longitudinal MRI with histopathology, molecular profiling, and clinical records documenting surgery, radiotherapy, chemotherapy, and supportive treatments. In routine practice, however, these data streams are often evaluated separately rather than jointly, which can delay molecularly informed stratification, limit reproducibility across centers, and complicate interpretation of post-treatment imaging changes. Multimodal machine learning (MML) provides a framework for clinical decision support by integrating diverse patient data across the course of care, from symptom presentation through diagnosis to treatment decisions. By combining MRI, whole-slide pathology, molecular and methylation profiling, and treatment timelines derived from electronic health records, MML models can capture disease characteristics over time across biological scales through representation learning and multimodal fusion. Importantly, these approaches can incorporate uncertainty through model calibration and confidence-aware predictions. When rigorously developed and validated, MML models may generate clinically relevant outputs, including integrated diagnosis, molecular classification, individualized survival estimates, probabilistic discrimination between tumor progression and pseudo-progression, and stratification for clinical trial eligibility. In this Mini Review, we summarize recent advances and emerging translational evidence for clinically oriented MML in glioblastoma, with particular emphasis on MRI-centered systems that integrate imaging with pathology, selected molecular measurements, and longitudinal clinical context. We also outline key methodological and practical considerations, including dataset curation, leakage control, external validation, calibration, and post-deployment monitoring—required to support safe, robust, and generalizable implementation of MML approaches in neuro-oncology practice.