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<p>Breast cancer remains the most common malignancy among women worldwide, with recurrence representing a major clinical challenge. Although significant progress has been made in early detection and treatment, recurrence affects up to 40% of patients in Brazil, influencing survival outcomes and therapeutic decisions. In this context, Machine Learning offers valuable potential for enhancing recurrence prediction by enabling data-driven risk assessment and personalized patient care. In the present study, clinical and histopathological information was extracted from unstructured medical records of breast cancer patients. A clustering technique (K-Means) was applied to identify patient subgroups with varying tumor aggressiveness profiles. Survival outcomes were further analyzed using the Cox proportional hazards model. Two distinct subgroups were identified: for less aggressive tumors, Quadratic Discriminant Analysis achieved a remarkably high recall of 0.9872, while for more aggressive tumors, Random Forest provided the most favorable trade-off between recall (0.7296) and precision (0.6811). Future research should explore validation across multiple institutions, incorporate molecular biomarkers, and leverage deep learning approaches to enhance predictive performance.</p>

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Machine learning approaches for predicting breast cancer recurrence using clinical and histopathological data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月30日

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  • Mohd Abas Bhat,
  • Mushtaq Ahmad Mir,
  • R. Vijaya Lakshmi,
  • Tejaswini Pradhan,
  • G. V. V. Jagannadha Rao,
  • Ghanshyam G. Tejani,
  • Syed Abid Hussain

摘要

Breast cancer remains the most common malignancy among women worldwide, with recurrence representing a major clinical challenge. Although significant progress has been made in early detection and treatment, recurrence affects up to 40% of patients in Brazil, influencing survival outcomes and therapeutic decisions. In this context, Machine Learning offers valuable potential for enhancing recurrence prediction by enabling data-driven risk assessment and personalized patient care. In the present study, clinical and histopathological information was extracted from unstructured medical records of breast cancer patients. A clustering technique (K-Means) was applied to identify patient subgroups with varying tumor aggressiveness profiles. Survival outcomes were further analyzed using the Cox proportional hazards model. Two distinct subgroups were identified: for less aggressive tumors, Quadratic Discriminant Analysis achieved a remarkably high recall of 0.9872, while for more aggressive tumors, Random Forest provided the most favorable trade-off between recall (0.7296) and precision (0.6811). Future research should explore validation across multiple institutions, incorporate molecular biomarkers, and leverage deep learning approaches to enhance predictive performance.