Multiscale Peritumoral Ultrasound Machine Learning Scores and Pathological Response to Neoadjuvant Therapy in Breast Cancer

Background/Objectives: Peritumoral ultrasound may capture tissue characteristics relevant to treatment sensitivity. In this study, we examined whether malignancy-related prediction scores (PS) were associated with pathological response to neoadjuvant therapy in breast cancer. Methods: Support vector machine classifiers were developed for 1, 3, 5, and 7 mm peritumoral regions using 1716 pathologically confirmed lesions. We assessed PS against postoperative Miller–Payne (MP) grade in 132 patients who completed neoadjuvant therapy and surgery. Ordinal logistic regression examined overall associations, interactions with hormone receptor (HR) status and Ki67, and treatment-stratified associations adjusted for both. Holm correction addressed multiple testing. The 7 mm classifier was refitted using 1584 patients outside the response cohort while retaining the originally selected features. Results: Higher 7 mm PS was associated with lower MP grade overall (odds ratio [OR] per 0.1-unit increase, 0.79; 95% confidence interval [CI], 0.66–0.95; Holm-adjusted p = 0.048). HR and Ki67 interactions remained significant at 1 mm (adjusted p = 0.049 and 0.036, respectively). In 60 complete cases receiving chemotherapy plus anti-HER2 therapy, the original 7 mm PS was associated with MP grade after adjustment (OR, 0.60; 95% CI, 0.42–0.85; adjusted p = 0.037). The refitted PS showed an attenuated association in these patients (OR, 0.709; 95% CI, 0.526–0.955; raw p = 0.0239; Holm-adjusted p = 0.0478). Associations in the original classifier test subsets (n = 22–26) were not significant after correction. Conclusions: The 7 mm score may complement HR and Ki67 for response assessment during chemotherapy plus anti-HER2 therapy. Interactions at 1 mm suggest variation with molecular characteristics. These findings warrant independent validation.