Multimodal AI fusion: integrating MRI with PET/CT, histopathology, and liquid biopsy for bone tumor diagnosis.

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Abstract

The diagnosis of bone tumors, though a low-incidence occurrence, presents a formidable challenge due to the morphological heterogeneity and variable biological behavior of these lesions. No single diagnostic modality whether imaging or biopsy is sufficient to provide the comprehensive information necessary for definitive diagnosis, accurate staging, and effective treatment planning. This report argues that a fragmented, sequential approach to diagnosis is inherently limited and that the future of orthopedic oncology lies in the integration of diverse data streams. Multimodal artificial intelligence (AI) offers a promising supportive approach by creating an AI-enabled integrative decision-support environment where computational systems can synthesize and contextualize information from magnetic resonance imaging (MRI), positron emission tomography computed tomography (PET/CT), histopathology, and liquid biopsy. This report details the specific capabilities and limitations of each of these foundational modalities, outlines the technical architectures required for their fusion, and illustrates how an integrated AI system can augment clinical workflows. It also addresses the critical challenges that must be overcome for clinical adoption, including data standardization, algorithmic interpretability, and economic viability, ultimately positioning this approach as an important emerging direction for future precision oncology.

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