ArticleEuropean journal of medical research2025
Prediction model incorporating dynamic changes in serum tumor markers for evaluating the efficacy of neoadjuvant chemotherapy in patients with breast cancer: a retrospective cohort study.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and evaluation of clinical models based on ultrasound, serum CA153 and molecular subtype in predicting the efficacy of neoadjuvant chemotherapy in breast cancer.Oncology letters · 2026Article
- Simultaneous Preoperative Prediction of Locally Advanced Breast Cancer, DCIS Component, and Multifocality Using Structured Mammographic Features and Gradient-Boosting Machine Learning.Diagnostics (Basel, Switzerland) · 2026Article
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8 authors.
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Abstract
backgroundCurrent prediction models for the efficacy of neoadjuvant chemotherapy (NAC) in patients with breast cancer (BC) include only static measurements of serum tumor markers, while the dynamic measurement data of these markers have not been fully utilized. This study aimed to develop and validate a prediction model for evaluating the efficacy of NAC in BC patients on the basis of dynamic changes in CEA, CA125, and CA15-3 levels.
methodsWe retrospectively screened 565 patients with BC who received NAC at our department from December 2016 to November 2021. A total of 446 patients were included and randomly divided into a training cohort (n = 312) and a test cohort (n = 134) at a 7:3 ratio. Least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation was applied to select the most relevant features, and multivariate logistic regression was used to construct the predictive model on the basis of the selected features. The performance of the model was evaluated by the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA).
resultsA nomogram integrating age, human epidermal growth factor receptor 2 (HER2) status, pre-chemotherapy levels of CEA, principal component 1 (PC1) for CEA, PC2 for CA125, and PC2 for CA15-3 was developed for the dynamic CEA&CA125&CA15-3 model. Another nomogram integrating estrogen receptor (ER) status, HER2 status, and pre-chemotherapy CEA levels was developed for the pre-chemotherapy CEA&CA125&CA15-3 model. The area under the receiver operating characteristic curve (AUC) of the dynamic model was 0.739 (95% CI 0.680-0.797) in the training cohort and 0.712 (95% CI 0.613-0.811) in the test cohort. The AUC was 0.658 (95% CI 0.593-0.722) in the training cohort and 0.715 (95% CI 0.624-0.807) in the test cohort for the pre-chemotherapy model. Compared with the pre-chemotherapy model, the dynamic model demonstrated significantly improved predictive accuracy. Our dynamic model also exhibited good predictive performance in subgroup analyses.
conclusionsThis study developed and validated nomogram models using clinicopathological features and serum tumor marker characteristics to predict NAC efficacy in BC patients. Although the dynamic model demonstrated comparable discriminative ability (AUC) to the pre-chemotherapy model in the test cohort, it showed significantly improved performance in net reclassification. While promising, this model requires further validation in multi-center prospective studies before clinical application.
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