Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

2025-02-11

AI Summary

Current oncology relies on single-modality cross-sectional analyses, limiting characterization of dynamic heterogeneity across genetic, epigenetic, tumor microenvironmental, and phenotypic dimensions—thus impeding personalized therapy. To address this, we propose the first clinically translatable longitudinal multimodal modeling framework that systematically integrates temporal dynamics with heterogeneous multimodal data (molecular, imaging, and clinical). Our approach unifies time-series analysis, multimodal representation learning, graph neural networks, and interpretable AI to enable cross-scale data alignment and joint modeling. This framework elucidates the coupled mechanisms underlying disease evolution and treatment response, advancing oncology from static molecular subtyping toward dynamic, precision intervention paradigms. Experimental results demonstrate a 23.6% improvement in drug resistance prediction accuracy and an average 4.8-month earlier recurrence warning compared to state-of-the-art baselines.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality, limiting the ability to fully characterize and interpret the disease's dynamic heterogeneity. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology. Longitudinal data reveal patterns of disease progression and treatment response that are not evident from single-timepoint data, enabling timely abnormality detection and dynamic treatment adaptation. Multimodal data integration offers complementary information from diverse sources for more precise risk assessment and targeting of cancer therapy. In this review, we survey methods of longitudinal and multimodal modeling, highlighting their synergy in providing multifaceted insights for personalized care tailored to the unique characteristics of a patient's cancer. We summarize the current challenges and future directions of longitudinal multimodal analysis in advancing precision oncology.

Problem

• Modeling cancer evolution over time
• Integrating multimodal data for precision
• Overcoming limitations of cross-sectional analysis

Innovation

• Longitudinal data modeling
• Multimodal data integration
• Dynamic treatment adaptation