aceA deep learning radiomics model based on multiparametric MRI for predicting the efficacy of neoadjuvant chemotherapy in axillary lymph nodes of breast cancer
Objectives In this study, we developed and validated a deep learning radiomics clinical (DLRC) model integrating multiparametric MRI (mpMRI) and clinical data to predict axillary pathological complete response (apCR) in cN+ breast cancer patients receiving neoadjuvant chemotherapy (NAC). Materials and methods We retrospectively collected data from 221 eligible breast cancer patients. The DLRC model integrated radiomics features and deep learning scores (DL-Scores) derived from mpMRI, together with clinical risk…
A deep learning radiomics model combining multiparametric MRI and clinical data predicted axillary pathological complete response after neoadjuvant chemotherapy, potentially helping clinicians identify candidates for axillary de-escalation.
The model was developed and validated on data from 221 patients collected retrospectively, which limits how far its performance can be assumed to apply prospectively.
Evidence
- Peer-reviewedMagnetic Resonance Imaging2026-10-21
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Truvace Impact Record TRV-2026-2229, v1: “A deep learning radiomics model based on multiparametric MRI for predicting the efficacy of neoadjuvant chemotherapy in axillary lymph nodes of breast cancer.” Truvace, 2026-10-24. /record/TRV-2026-2229 (accessed at citation time). sha256 575360f3961f76ef…
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