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Background <p>This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.</p> Methods <p>Clinical and pathological data from 1143 patients were analyzed, encompassing variables such as age, gender, marital status, histologic grade, T stage, N stage, months from diagnosis to treatment, molecular subtype, and response to neoadjuvant therapy. Seven machine learning models were trained and validated using both internal and external datasets. Model performance was evaluated using multiple metrics, and interpretability analysis was conducted to assess feature importance.</p> Results <p>Key variables influencing pCR included grade, N stage, months from diagnosis to treatment, and molecular subtype. The Naive Bayes model emerged as the most effective, with accuracy (0.746), sensitivity (0.699), specificity (0.808), and F1 score (0.759) surpassing other models. Both internal and external validation confirmed the model’s robust predictive power. A web tool was developed for clinical use, aiding in personalized treatment planning. Interpretability analysis further elucidated the contribution of features to pCR prediction, enhancing clinical applicability.</p> Conclusion <p>The Naive Bayes model provides a robust tool for personalized treatment decisions in patients with breast cancer undergoing neoadjuvant therapy. By accurately predicting pCR rates, it enables clinicians to tailor treatment strategies, potentially improving outcomes.</p>

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Clinical prediction of pathological complete response in breast cancer: a machine learning study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月23日

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  • Chongwu He,
  • Tenghua Yu,
  • Liu Yang,
  • Longbo He,
  • Jin Zhu,
  • Jing Chen

摘要

Background

This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients.

Methods

Clinical and pathological data from 1143 patients were analyzed, encompassing variables such as age, gender, marital status, histologic grade, T stage, N stage, months from diagnosis to treatment, molecular subtype, and response to neoadjuvant therapy. Seven machine learning models were trained and validated using both internal and external datasets. Model performance was evaluated using multiple metrics, and interpretability analysis was conducted to assess feature importance.

Results

Key variables influencing pCR included grade, N stage, months from diagnosis to treatment, and molecular subtype. The Naive Bayes model emerged as the most effective, with accuracy (0.746), sensitivity (0.699), specificity (0.808), and F1 score (0.759) surpassing other models. Both internal and external validation confirmed the model’s robust predictive power. A web tool was developed for clinical use, aiding in personalized treatment planning. Interpretability analysis further elucidated the contribution of features to pCR prediction, enhancing clinical applicability.

Conclusion

The Naive Bayes model provides a robust tool for personalized treatment decisions in patients with breast cancer undergoing neoadjuvant therapy. By accurately predicting pCR rates, it enables clinicians to tailor treatment strategies, potentially improving outcomes.