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April 21, 2026Scientific ReportsOpen Access

Multimodal machine learning integrates clinical and comorbidity data to predict breast cancer prognosis and treatment outcomes

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Authors

YLYongsheng LuoHHHai HuangTCTet Khuan Chen

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Overview

This framework integrates clinical and comorbidity data to predict outcomes in breast cancer, suggesting better prognostication methods.

Key Points

  • The research aims to enhance breast cancer prognosis and treatment decisions by integrating various data types.
  • Developed a framework to integrate clinical parameters, comorbidity profiles, and patient-reported outcomes.
  • Harmonized data from a cohort of 1727 patients, addressing issues like missing data and feature engineering.
  • Conducted model training, cross-validation, and ablation studies to evaluate multimodal data impact.
  • Demonstrated how specific comorbidities and patient-reported outcomes influence survival rates.
  • Highlighted the limitations of single-modality approaches in predicting breast cancer outcomes.
  • Provided a roadmap for incorporating multimodal analytics into treatment planning.

Cite This Study

Luo et al. (2026) studied this question.

synapsesocial.com/papers/69e713decb99343efc98d43fhttps://doi.org/10.1038/s41598-026-47972-y
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