Academic radiology · 2026
Efficacy of Quantitative Parameter Changes Derived from Voigt Model-Based Sound-Touch Visco in Predicting Early Response to Neoadjuvant Chemotherapy for Breast Cancer.
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Zhang Anran, Han Xiaolu, Ge Yu, Wang Wenwen, Li Chunjie, Liang Tingting, Li Qian, Hou Xiaodong
Аннотация
OBJECTIVES: To evaluate the efficacy of quantitative parameter changes in Sound-Touch Visco (STVi) for predicting the response to neoadjuvant chemotherapy (NAC) in breast cancer.
MATERIALS AND METHODS: This prospective single-center study enrolled 79 consecutive female patients receiving NAC between October 2024 and July 2025. STVi assessments were performed at four time points: pre-NAC, post-NAC cycle 2, post-NAC cycle 4, and post-NAC cycle 6. Patients were classified according to postoperative pathological Miller-Payne grading (MPG): minor responders (MR) as MPG 1-3, and good responders (GR) as MPG 4-5. The optimal STVi parameter was identified by the highest area under the receiver operating characteristic curve (AUC) among all parameters measured before and after 2 cycles of NAC. The percentage change relative to baseline was calculated at each time point. Predictive performance for a favorable response was assessed for the overall cohort and each molecular subtype using the AUC.
RESULTS: A total of 79 patients were enrolled, comprising 53 good responders (67.1%). ΔVmax exhibited the highest AUC (0.79, 95% CI, 0.68-0.91) for predicting response among all STVi parameters measured pre-NAC and post-NAC cycle 2, establishing it as the optimal parameter. Overall, ΔVmax demonstrated consistent performance in predicting a good response across NAC cycles: post-cycle 2 AUC (0.79, 95% CI, 0.68-0.91), post-cycle 4 AUC (0.86, 95% CI, 0.77-0.94) (P > 0.05), with sensitivities of 83.0%/73.6% and specificities of 69.2%/84.6% at optimal thresholds of 7%/26%, respectively. In molecular subtype analysis, ΔVmax in triple-negative breast cancer (TNBC) achieved a high AUC (0.80-0.93), favorable sensitivity (73.4-84.2%), and 100% specificity at each time point.
CONCLUSION: Tumor ΔVmax changes from STVi facilitate dynamic monitoring of NAC response.
MATERIALS AND METHODS: This prospective single-center study enrolled 79 consecutive female patients receiving NAC between October 2024 and July 2025. STVi assessments were performed at four time points: pre-NAC, post-NAC cycle 2, post-NAC cycle 4, and post-NAC cycle 6. Patients were classified according to postoperative pathological Miller-Payne grading (MPG): minor responders (MR) as MPG 1-3, and good responders (GR) as MPG 4-5. The optimal STVi parameter was identified by the highest area under the receiver operating characteristic curve (AUC) among all parameters measured before and after 2 cycles of NAC. The percentage change relative to baseline was calculated at each time point. Predictive performance for a favorable response was assessed for the overall cohort and each molecular subtype using the AUC.
RESULTS: A total of 79 patients were enrolled, comprising 53 good responders (67.1%). ΔVmax exhibited the highest AUC (0.79, 95% CI, 0.68-0.91) for predicting response among all STVi parameters measured pre-NAC and post-NAC cycle 2, establishing it as the optimal parameter. Overall, ΔVmax demonstrated consistent performance in predicting a good response across NAC cycles: post-cycle 2 AUC (0.79, 95% CI, 0.68-0.91), post-cycle 4 AUC (0.86, 95% CI, 0.77-0.94) (P > 0.05), with sensitivities of 83.0%/73.6% and specificities of 69.2%/84.6% at optimal thresholds of 7%/26%, respectively. In molecular subtype analysis, ΔVmax in triple-negative breast cancer (TNBC) achieved a high AUC (0.80-0.93), favorable sensitivity (73.4-84.2%), and 100% specificity at each time point.
CONCLUSION: Tumor ΔVmax changes from STVi facilitate dynamic monitoring of NAC response.