[Paper Review] Multimodal Data Integration for Precision Oncology: Challenges and Future Directions

Hua-Jun Zhou, Fengtao Zhou+4 more|arXiv (Cornell University)|Jun 28, 2024

Genetics, Bioinformatics, and Biomedical Research4 citations

TL;DR

This paper presents a comprehensive survey of 300+ studies on multimodal data integration in precision oncology, analyzing techniques that combine medical imaging, clinical records, and omics data to improve cancer diagnosis, prognosis, and biomarker discovery. It identifies key challenges in data heterogeneity, missing modalities, and model interpretability, and outlines future research directions for robust, generalizable AI-driven oncology solutions.

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.

Motivation & Objective

• To provide a systematic review of 300+ recent studies on multimodal data integration in precision oncology from 2014 to 2024.
• To identify and analyze the most effective fusion strategies for integrating heterogeneous data modalities such as medical imaging, clinical records, and omics data.
• To evaluate the clinical impact of multimodal integration in key oncology applications including early diagnosis, prognosis prediction, and biomarker discovery.
• To highlight persistent challenges such as missing modalities, data heterogeneity, and model interpretability in real-world clinical deployment.
• To outline actionable future research directions for advancing robust, generalizable, and clinically interpretable AI models in oncology.

Proposed method

• Systematic literature review of 300+ peer-reviewed papers in multimodal data integration for precision oncology (2014–2024).
• Categorization of methods based on fusion strategy: early, late, and hybrid fusion, with analysis of their strengths and limitations.
• Evaluation of imputation-based and imputation-free approaches for handling incomplete multimodal data.
• Analysis of model architectures including attention mechanisms, graph neural networks, and transformers in multimodal oncology applications.
• Synthesis of findings across clinical tasks: diagnosis, prognosis, treatment response prediction, and biomarker discovery.
• Identification of recurring methodological gaps and technical challenges through comparative analysis of state-of-the-art approaches.

Experimental results

Research questions

• RQ1 What are the dominant multimodal fusion strategies used in precision oncology, and how do they compare in performance and robustness?
• RQ2 How do existing methods handle missing modalities, and what trade-offs exist between imputation-based and imputation-free approaches?
• RQ3 Which data modalities (imaging, clinical, omics) contribute most significantly to improved diagnostic and prognostic accuracy in cancer?
• RQ4 What are the key limitations in current multimodal integration models that hinder clinical translation?
• RQ5 What future research directions are most critical for advancing reliable, interpretable, and generalizable AI in precision oncology?

Key findings

• Multimodal integration significantly improves performance in cancer diagnosis, prognosis prediction, and treatment response modeling compared to single-modality approaches.
• Attention-based and graph neural network models show strong performance in capturing complex inter-modality relationships across imaging, clinical, and omics data.
• Imputation-free methods are increasingly favored due to reduced risk of introducing noise from inaccurate missing data reconstruction.
• Robustness to missing modalities remains a major challenge, with performance degradation observed when more than 30% of data is missing.
• Interpretability and clinical plausibility of model predictions are frequently underemphasized, despite their importance for clinician trust and adoption.
• The integration of multi-omics data with medical imaging and clinical records has shown the highest potential for biomarker discovery and personalized therapy planning.