aceIntratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer
Rationale and objectives This study investigated the clinical utility of a machine learning (ML) model based on ultrasoundomics features (UFs) from primary foci to predict pathological complete response (pCR) in patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NAC). Materials and methods Ultrasound images from 333 patients with BC treated with NAC were retrospectively analyzed. Patients in the internal training cohort and external validation cohort were from two different hospitals, respecti…
Including tissue 5 mm around the tumor raised external-cohort AUC for predicting pathological complete response from 0.632 (tumor only) to 0.779.
The study was retrospective, and model performance dropped substantially from the internal training cohort to the external validation cohort, indicating limited generalizability across hospitals.
Evidence
- Peer-reviewedAcademic Radiology2026-10-05
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Truvace Impact Record TRV-2026-1853, v1: “Intratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.” Truvace, 2026-10-18. /record/TRV-2026-1853 (accessed at citation time). sha256 768961ebe30c5e90…
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