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<p>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&#xa0;1 contextualizes the clinical relevance of comorbidity burden and quality-of-life metrics in breast cancer care, emphasizing their underutilization in conventional models. Section&#xa0;2 synthesizes prior efforts in machine learning and oncology, identifying limitations in single-modality approaches and underscoring the necessity for holistic data integration. Section&#xa0;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&#xa0;4 outlines experimental protocols for model training, cross-validation, and ablation studies to quantify the incremental value of multimodal inputs. Section&#xa0;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&#xa0;6 interprets these findings through the lens of clinical utility, addressing scalability challenges and ethical considerations for real-world deployment. Finally, Sect.&#xa0;7 proposes translational pathways for embedding multimodal analytics into risk-adapted treatment paradigms. This work bridges computational innovation with patient-centered care, offering a roadmap for advancing precision oncology through data-driven, multidimensional modeling.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月19日

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  • Yongsheng Luo,
  • Hai Huang,
  • Tet Khuan Chen,
  • Rana Muhammad Ehsan,
  • Shanmugam Kamalanathan,
  • Yu Li,
  • Ngan Kuen Lai

摘要

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 interprets these findings through the lens of clinical utility, addressing scalability challenges and ethical considerations for real-world deployment. Finally, Sect. 7 proposes translational pathways for embedding multimodal analytics into risk-adapted treatment paradigms. This work bridges computational innovation with patient-centered care, offering a roadmap for advancing precision oncology through data-driven, multidimensional modeling.