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Archive > Volume 33 Issue 5 > 2026,33(5):554-562. DOI:10.3872/j.issn.1007-385X.2026.05.010 Prev Next
Clinical Research
Factors influencing treatment outcomes and the construction of a prediction model following neoadjuvant endocrine therapy in patients with ER-positive breast cancer
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Abstract:
[Abstract] Objective: To investigate the factors influencing pathologic complete response (pCR) following neoadjuvant endocrine therapy in patients with estrogen receptor (ER)-positive breast cancer and to develop and validate a predictive model. Methods: A retrospective study was conducted on 715 patients with ER-positive breast cancer who received neoadjuvant endocrine therapy and surgical treatment at the Fourth Hospital of Hebei Medical University between January 2020 and January 2024. Based on postoperative pathological results, the patients were divided into a pCR group (72 cases) and a non-pCR group (643 cases). The cohort was randomly divided into a training set and a testing set in a 7∶3 ratio, and clinicopathological and laboratory data were collected. Independent risk factors for failure to achieve pCR were identified using univariate and multivariate logistic regression analyses, and a predictive model was constructed. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) for both the training and testing sets. Results: There were no significant differences in clinical data between patients in the training set and the test set. In the training set, there were significant differences between the pCR group and the non-pCR group in terms of the tumor proliferation marker protein Ki-67 index, pre-treatment serum cancer antigen 15-3 (CA15-3) levels, pre-treatment estrogen receptor 1 (ESR1) gene mutation status, pre-treatment miR-1-3p expression levels, pre-treatment SRY-box transcription factor 9 expression levels, clinical stage, and lymph node metastasis (P < · · 554 张华, 等. ER阳性乳腺癌患者新辅助内分泌治疗后疗效影响因素及预测模型构建 0.05). Multivariate logistic regression analysis revealed that the Ki-67 index, pre-treatment serum CA15-3 levels, pre-treatment ESR1 mutation status, pre-treatment miR-1-3p expression levels, and clinical stage were independent influencing factors associated with failure to achieve pCR/non-pCR following neoadjuvant endocrine therapy in patients with ER-positive breast cancer (P < 0.05). A nomogram prediction model was constructed based on these factors, and validation using ROC curves, calibration curves, and DCA confirmed that the model possesses good discriminatory power, predictive accuracy, and clinical utility. Conclusion: The nomogram prediction model incorporating factors such as the Ki-67 index, serum CA15-3 levels, and ESR1 gene mutation status demonstrates good predictive performance for the risk of failing to achieve pCR following neoadjuvant endocrine therapy in patients with ER-positive breast cancer, and can serve as a reference for clinical decision-making.
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