Analysis of Cancer Risk Models Driven by Multimodal Data Fusion

Abstract

Cancer is a leading global cause of mortality, with its pathogenesis involving complex interactions across genetic, molecular, and clinical dimensions. Early and accurate risk prediction is thus pivotal for timely intervention and improving patient survival rates. However, traditional single-modal cancer risk models, which depend on a single data type such as genomics or imaging, cannot capture cancer’s inherent multi-dimensional and heterogeneous characteristics. This shortcoming not only limits their predictive accuracy but also restricts their practical utility in clinical settings. Multimodal data fusion effectively addresses this limitation by integrating complementary genomic, proteomic, imaging, and clinical data to build more comprehensive and reliable risk models. This paper systematically analyzes the current landscape of multimodal data-driven cancer risk models. It elaborates on the four core data types and their unique roles in reflecting disease attributes, classifies fusion methods into three levels based on different data processing stages, explores key clinical applications including early screening and prognosis assessment, and discusses major challenges such as data heterogeneity and privacy concerns along with corresponding solutions. The study emphasizes the significant value of multimodal fusion in enhancing model performance and offers a theoretical and technical reference to advance the development and clinical translation of precision oncology.