Multimodal machine learning integrates clinical and comorbidity data to predict breast cancer prognosis and treatment outcomes
Abstract
Breast cancer prognosis and treatment outcomes are shaped by intricate interplay between tumor biology, systemic comorbidities, and patient-reported functional status. This paper introduces a framework to address critical gaps in precision oncology by systematically integrating multimodal data-including clinical parameters, comorbidity profiles, and patient-reported outcomes (PROs)-to refine prognostic stratification and therapeutic decision-making. Section 1 contextualizes the clinical relevance of comorbidity burden and quality-of-life metrics in breast cancer care, emphasizing their underutilization in conventional models. Section 2 synthesizes prior efforts in machine learning and oncology, identifying limitations in single-modality approaches and underscoring the necessity for holistic data integration. Section 3 describes the harmonization of structured clinical variables, binary comorbidity flags, and EORTC QLQ-C30/BR23 scores from a prospective cohort of 1727 patients, alongside methodological innovations for handling missingness and feature engineering. Section 4 outlines experimental protocols for model training, cross-validation, and ablation studies to quantify the incremental value of multimodal inputs. Section 5 highlights key insights into prognostic heterogeneity, including the differential impacts of specific comorbidities (e.g., urinary tract infections, depression) and PRO domains (e.g., fatigue, global health status) on survival trajectories. Section 6…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,727 participants or records.
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