Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data

Area of Science:

• Oncology
• Biomedical Data Science
• Computational Biology

Background:

• Cancer is a dynamic disease characterized by continuous evolution.
• Current oncology research often relies on cross-sectional, single-modality data, limiting understanding of disease heterogeneity.
• This approach hinders effective cancer monitoring and personalized treatment strategies.

Purpose of the Study:

• To review methods for longitudinal and multimodal modeling in cancer research.
• To highlight the synergy of longitudinal and multimodal data for advancing precision oncology.
• To identify challenges and future directions in this field.

Main Methods:

• Survey of existing methods for longitudinal data analysis in cancer.
• Review of techniques for multimodal data integration in oncology.
• Discussion of computational approaches for combining temporal and diverse data sources.

Main Results:

• Longitudinal data capture dynamic patterns of disease progression and treatment response.
• Multimodal data integration provides complementary information for precise risk assessment.
• The combination of longitudinal and multimodal approaches enables multifaceted insights for personalized cancer care.

Conclusions:

• Longitudinal multimodal analysis is crucial for understanding cancer's dynamic nature.
• This approach enhances the potential for timely abnormality detection and dynamic treatment adaptation.
• Addressing current challenges in longitudinal multimodal analysis will advance precision oncology.