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Background <p>This study aimed to evaluate the ability of mammography-based radiomics combined with machine learning to predict pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NACT), addressing the limited research on mammography radiomics.</p> Methods <p>A retrospective cohort of 408 female breast cancer patients who underwent neoadjuvant chemotherapy (NACT) followed by surgery from January 2022 to December 2023 was analyzed. Breast-only pCR was defined as the absence of residual invasive carcinoma in the breast tumor bed and did not incorporate axillary lymph node status. Delta radiomics features, representing quantitative changes from pre- and post-NACT mammography images, were extracted. After evaluating 18 machine learning algorithms using cross-validation, CatBoost, ExtraTrees, and Logistic Regression were selected, optimized, and validated independently. A stacking ensemble was also constructed. Model performance was assessed using AUC, accuracy, and precision, with DeLong’s test for statistical comparisons.</p> Results <p>Among the 408 patients, 87 (21.32%) achieved pCR. On the independent test set, ExtraTrees, CatBoost, and Logistic Regression models achieved AUCs of 0.854 (95% CI 0.773–0.936), 0.836 (95% CI 0.745–0.926), and 0.775 (95% CI 0.683–0.867), respectively. The stacking ensemble model achieved the numerically highest AUC of 0.884 (95% CI 0.807–0.961). HER2 status, hormone receptor status, and some specific radiomic features were consistently identified as important contributors to pCR assessment. DeLong’s test indicated that the stacking model had a significantly higher AUC than CatBoost (p = 0.03) and Logistic Regression (p = 0.01), whereas its difference from ExtraTrees was not statistically significant (p = 0.47).</p> Conclusions <p>Mammography-based delta radiomics combined with machine learning showed promising performance for predicting pCR after NACT in breast cancer patients; however, external validation is needed to confirm clinical applicability.</p>

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Mammography based delta radiomics with machine learning predicts pathologic complete response after neoadjuvant chemotherapy in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月27日

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  • Xiaohang Hu,
  • Yanfang Liu,
  • Yuan Gao,
  • Yuhui Ge,
  • Yang Wang,
  • Yongping Du,
  • Nan Zhang,
  • Dandan Cao,
  • Haiping Zhang,
  • Qi Zhou

摘要

Background

This study aimed to evaluate the ability of mammography-based radiomics combined with machine learning to predict pathologic complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NACT), addressing the limited research on mammography radiomics.

Methods

A retrospective cohort of 408 female breast cancer patients who underwent neoadjuvant chemotherapy (NACT) followed by surgery from January 2022 to December 2023 was analyzed. Breast-only pCR was defined as the absence of residual invasive carcinoma in the breast tumor bed and did not incorporate axillary lymph node status. Delta radiomics features, representing quantitative changes from pre- and post-NACT mammography images, were extracted. After evaluating 18 machine learning algorithms using cross-validation, CatBoost, ExtraTrees, and Logistic Regression were selected, optimized, and validated independently. A stacking ensemble was also constructed. Model performance was assessed using AUC, accuracy, and precision, with DeLong’s test for statistical comparisons.

Results

Among the 408 patients, 87 (21.32%) achieved pCR. On the independent test set, ExtraTrees, CatBoost, and Logistic Regression models achieved AUCs of 0.854 (95% CI 0.773–0.936), 0.836 (95% CI 0.745–0.926), and 0.775 (95% CI 0.683–0.867), respectively. The stacking ensemble model achieved the numerically highest AUC of 0.884 (95% CI 0.807–0.961). HER2 status, hormone receptor status, and some specific radiomic features were consistently identified as important contributors to pCR assessment. DeLong’s test indicated that the stacking model had a significantly higher AUC than CatBoost (p = 0.03) and Logistic Regression (p = 0.01), whereas its difference from ExtraTrees was not statistically significant (p = 0.47).

Conclusions

Mammography-based delta radiomics combined with machine learning showed promising performance for predicting pCR after NACT in breast cancer patients; however, external validation is needed to confirm clinical applicability.