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Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Huajun Zhou, Fengtao Zhou, Chenyu Zhao, Yingxue Xu, Luyang Luo, Hao Chen

TL;DR

This survey canvasses a decade of multimodal data integration for precision oncology, detailing data modalities, fusion architectures (early, intermediate, late, and multi-level), and strategies for incomplete data (imputation-based and imputation-free). It connects methodological advances to concrete clinical applications—ranging from risk stratification and screening to segmentation, staging, and biomarker discovery—while highlighting persistent challenges in robustness, heterogeneity, explainability, efficiency, and cross-center generalization. The work emphasizes that intermediate and multi-level fusion approaches currently show the strongest promise, though standardized benchmarks are needed to draw definitive conclusions. By cataloging datasets and reviewing roughly 300 papers, the paper provides a practical roadmap for researchers and clinicians to advance robust, interpretable, and transferable multimodal AI tools in cancer care.

Abstract

The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The inherent heterogeneity of tumors necessitates gathering information from diverse data sources to provide valuable insights from various perspectives, fostering a holistic comprehension of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides, showcasing remarkable progress in understanding the intricate details within heterogeneous data modalities. These strides have exhibited tremendous potential for improving clinical decision-making and model interpretation, contributing to the advancement of cancer care and treatment. Given the rapid progress that has been achieved, we provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology. In addition, we conclude the primary clinical applications that have reaped significant benefits, including early assessment, diagnosis, prognosis, and biomarker discovery. Finally, derived from the findings of this survey, we present an in-depth analysis that explores the pivotal challenges and reveals essential pathways for future research in the field of multimodal data integration for precision oncology.

Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

TL;DR

This survey canvasses a decade of multimodal data integration for precision oncology, detailing data modalities, fusion architectures (early, intermediate, late, and multi-level), and strategies for incomplete data (imputation-based and imputation-free). It connects methodological advances to concrete clinical applications—ranging from risk stratification and screening to segmentation, staging, and biomarker discovery—while highlighting persistent challenges in robustness, heterogeneity, explainability, efficiency, and cross-center generalization. The work emphasizes that intermediate and multi-level fusion approaches currently show the strongest promise, though standardized benchmarks are needed to draw definitive conclusions. By cataloging datasets and reviewing roughly 300 papers, the paper provides a practical roadmap for researchers and clinicians to advance robust, interpretable, and transferable multimodal AI tools in cancer care.

Abstract

The essence of precision oncology lies in its commitment to tailor targeted treatments and care measures to each patient based on the individual characteristics of the tumor. The inherent heterogeneity of tumors necessitates gathering information from diverse data sources to provide valuable insights from various perspectives, fostering a holistic comprehension of the tumor. Over the past decade, multimodal data integration technology for precision oncology has made significant strides, showcasing remarkable progress in understanding the intricate details within heterogeneous data modalities. These strides have exhibited tremendous potential for improving clinical decision-making and model interpretation, contributing to the advancement of cancer care and treatment. Given the rapid progress that has been achieved, we provide a comprehensive overview of about 300 papers detailing cutting-edge multimodal data integration techniques in precision oncology. In addition, we conclude the primary clinical applications that have reaped significant benefits, including early assessment, diagnosis, prognosis, and biomarker discovery. Finally, derived from the findings of this survey, we present an in-depth analysis that explores the pivotal challenges and reveals essential pathways for future research in the field of multimodal data integration for precision oncology.
Paper Structure (41 sections, 4 figures, 4 tables)

This paper contains 41 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data Modality
  3. Methods of Multimodal Data Integration
  4. Integration of Complete Data
  5. Early Fusion
  6. Intermediate Fusion
  7. Late Fusion
  8. Multi-level Fusion
  9. Comparative Analysis.
  10. Integration of Incomplete Data
  11. Imputation-based Methods
  12. Imputation-free Methods
  13. Comparative Analysis
  14. Applications of Multimodal Data Integration
  15. Early Assessment
  16. ...and 26 more sections

Figures (4)

  • Figure 1: Overview of multimodal data integration for advancing precision oncology.
  • Figure 2: Histogram of the reviewed papers on multimodal data integration for precision oncology in the past decade.
  • Figure 3: Fusion strategies for complete data, including (a) early fusion, (b) intermediate fusion, (c) late fusion, and (d-e) multi-level fusion.
  • Figure 4: Fusion strategies for incomplete date, including imputation-based methods and imputation-free methods.