Ultrasound-based predictive models for response to neoadjuvant chemotherapy in breast cancer: a systematic review and...

This meta-analysis aimed to assess the diagnostic efficacy of ultrasound-based prediction models in evaluating the response to neoadjuvant chemotherapy (NAC) in breast cancer patients.

A systematic review was conducted using PubMed, Embase, and Web of Science from inception to May 2025. We focused on studies evaluating ultrasound-based prediction models for NAC response in breast cancer. Two investigators independently performed study selection and data extraction according to predefined eligibility criteria. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed using Stata 14.0 software, with the area under the curve (AUC) and its 95% confidence interval (CI) as the primary measure of diagnostic accuracy.

Fourteen retrospective studies with a total of 35 prediction models were included. PROBAST assessment indicated a high overall risk of bias in most studies (10 out of 14). The pooled AUC for all ultrasound-based prediction models in predicting NAC response was 0.84 (95% CI: 0.82, 0.86). However, substantial heterogeneity was observed among the studies (I² = 69.7%, p < 0.001). Subgroup analyses suggested that model features and the prediction outcomes were major sources of heterogeneity. In the test cohort, the model combining ultrasound with clinical features demonstrated the best performance in predicting pathologic complete response (pCR), with a pooled AUC of 0.88 (95% CI: 0.84-0.92) and no heterogeneity (I² = 0.0%). Sensitivity analyses confirmed the robustness of the findings. Although the funnel plot appeared symmetrical, both Begg's and Egger's tests indicated publication bias (p < 0.01). Notably, 10 of 14 included studies (71.4%) were at high overall risk of bias, and all studies were single-center and conducted in China, which substantially limits the immediate clinical translation of these findings.

Ultrasound-based prediction models show encouraging discriminative ability for predicting pCR (pooled AUC 0.84, 95% CI 0.82-0.86), suggesting preliminary potential. However, given the high risk of bias, substantial heterogeneity, and geographic limitation (all studies from China), the current evidence should be considered exploratory and hypothesis-generating rather than practice-changing. Future prospective, multicenter, and rigorously designed studies are needed to confirm these findings.