An Integrative Review of Computational Methods Applied to Biomarkers, Psychological Metrics, and Behavioral Signals...

Area of Science:

• Oncology
• Digital Health
• Computational Biology

Background:

• Cancer incidence and mortality pose significant global health challenges.
• Current cancer screening primarily uses histological or immunological biomarkers.
• Psychological factors (stress, anxiety, depression) and behaviors influence cancer vulnerability and progression but are underutilized in screening.

Purpose of the Study:

• To review evidence on integrating biological, psychometric, and digital behavioral data for early cancer risk assessment.
• To explore the role of computational sciences and artificial intelligence (AI) in multimodal data fusion for cancer screening.
• To highlight the potential of digital health infrastructure in improving screening accessibility and coverage.

Main Methods:

• Systematic review of current evidence on multimodal data integration for cancer risk evaluation.
• Examination of advances in AI and computational sciences for harmonizing and fusing diverse data types.
• Discussion of the role of digital health and telemedical platforms in screening.

Main Results:

• Multimodal approaches combining biological markers, psychometric profiling, and digital phenotyping offer potential for early cancer risk evaluation.
• AI and computational methods can enable sophisticated data fusion and interpretation for screening.
• Digital health infrastructure is crucial for enhancing screening accessibility, monitoring, and coverage.

Conclusions:

• Integrating psychological and behavioral data alongside biological markers may significantly improve cancer risk assessment.
• AI-assisted screening paradigms leveraging multimodal data hold promise for future healthcare.
• Further research is needed to validate the predictive value of psychological and behavioral modalities in cancer screening.