To investigate the predictive value of a nomogram model that integrates intratumoral and peritumoral radiomic features derived from conventional ultrasound images obtained before and after two cycles of neoadjuvant chemotherapy (NAC), along with clinicopathological factors, for assessing pathological complete response (pCR) in breast cancer patients following NAC.

A total of 332 female patients with pathologically confirmed breast cancer who received a standard six-cycle neoadjuvant chemotherapy (NAC) regimen at the Tenth Affiliated Hospital of Southern Medical University between July 1, 2019 and December 1, 2023 were retrospectively enrolled. All patients underwent ultrasound examinations before NAC initiation and after the second cycle of NAC, and these imaging data were used for early treatment response prediction. Patients were randomly divided into a training cohort (n=233) and a validation cohort (n=99) at a 7∶3 ratio. Intratumoral regions and peritumoral areas at 1 mm, 2 mm, and 3 mm margins were delineated on ultrasound images obtained before NAC and after two cycles of NAC. Radiomic features were extracted, and their stability was assessed using the intraclass correlation coefficient (ICC). Stable features were selected via Student's t-test, Pearson correlation analysis, and least absolute shrinkage and selection operator (LASSO) regression. Six machine learning algorithms—CatBoost, LightGBM, logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost—were used to construct radiomics models. Independent clinical predictors were identified by multivariate logistic regression to build a clinical model. The optimal radiomics model was then combined with clinical predictors to establish a combined model, which was visualized as a nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).

In the clinical model, estrogen receptor (ER), progesterone receptor (PR), Her-2, and Ki-67 were identified as independent predictors of pCR, yielding an area under the curve (AUC) of 0.797 in the validation cohort. Among the radiomics models, the optimal pre-NAC model was the RS_XGBoost model based on intratumoral and peritumoral (2-mm margin) features (AUC = 0.726), while the optimal post–two-cycle NAC model was the RS_SVM model based on peritumoral (2-mm margin) features (AUC = 0.770). The combined models demonstrated superior predictive performance compared with the single models, achieving AUCs of 0.842 before NAC and 0.864 after two NAC cycles. DCA demonstrated that both combined models provided clinical net benefit across threshold probability ranges of 6.7%–90.4% and 5.6%–94.1%, respectively, with area under the decision curve (AUDC) values of 0.092 and 0.116, respectively. Calibration curves indicated good agreement between predicted and observed outcomes (P=0.48). The nomogram achieved a concordance index (C-index) of 0.866.

The nomogram model integrating post-NAC ultrasound radiomic features with clinicopathological factors effectively predicts the therapeutic response of breast cancer to neoadjuvant chemotherapy. Notably, radiomic features from the peritumoral 2-mm region after two NAC cycles, when combined with clinical indicators, provide enhanced predictive accuracy and offer valuable guidance for individualized treatment decision-making.