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.