Construction of a nomogram prediction model for the pathological complete response after neoadjuvant chemotherapy in breast cancer: a study based on ultrasound and clinicopathological features.

OBJECTIVE

To explore the application value of ultrasound in evaluating the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer and construct a nomogram prediction model for pathological complete response (pCR) following different cycles of NAC based on ultrasound and clinicopathological features, and further investigate the optimal prediction cycle.

METHODS

A total of 249 breast cancer patients who received NAC were recruited. Ultrasound assessment was performed before NAC and after two cycles of NAC (NAC2), four cycles of NAC (NAC4), and six cycles of NAC (NAC6). All patients underwent surgical resection after NAC6 and the samples were sent for histopathological and immunohistochemical examination. Clinical efficacy was determined according to the Response Evaluation Criteria in Solid Tumors (RECIST). Pathological efficacy was determined according to the Miller-Payne evaluation system (MP); grade 5 was classified as pCR group, while Grades 1-4 were classified as the non-pCR group (npCR). The patients were randomly divided into the training set and the validation set at a ratio of 7:3. The ultrasound and clinicopathological features of the training set were compared, and a nomogram prediction model was constructed based on these features. Finally, the ROC curve, calibration curve, and DCA were used for verification.

RESULT

Among the 249 patients, 71 (28.5%) achieved pCR, whereas the remaining 178 (71.5%) exhibited npCR. The maximum tumor diameter measured by ultrasound after NAC6 was 1.20 (0.70, 2.10) cm, which was significantly positively correlated with the maximum tumor diameter measured by pathology after surgical resection (=0.626, <0.05). In the training set, multivariate logistic regression analysis revealed that tumor size, posterior echo, RECIST evaluation, and PR status were significantly correlated with pCR after NAC2, NAC4, and NAC6 (<0.05). These indicators were incorporated into static and dynamic nomogram models, demonstrating high predictive performance, calibration, and clinical value in both the training and validation sets.

CONCLUSION

Regardless of the cycle of NAC, patients with a small tumor, no posterior shadow, a valid RECIST, and a negative PR were more likely to achieve pCR. Evaluation after NAC2 can provide early predictive value in clinical practice.