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TRUVACE RECORD VERSION
record: TRV-2026-2229
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-24T00:00:33.038682Z
status: published
lens: g_space
sector: health
headline: A deep learning radiomics model based on multiparametric MRI for predicting the efficacy of neoadjuvant chemotherapy in axillary lymph nodes of breast cancer
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: identifying potential candidates for axillary de-escalation
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers built a model that merges radiomics features and deep learning scores from multiparametric breast MRI with clinical risk factors. It was designed to estimate whether cancer in the axillary lymph nodes would be completely cleared after neoadjuvant chemotherapy in patients with node-positive disease.

If the approach holds up, it could give clinicians an auxiliary tool for judging which patients might safely receive less extensive axillary treatment, supporting more personalized care. The evidence comes from a retrospective cohort of 221 patients, so how well it performs in routine practice remains untested.
limitation: 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.
tag: Evidence-backed gain
key_points: The study included 221 eligible breast cancer patients whose data were collected retrospectively. | The model combined radiomics features, deep learning scores from multiparametric MRI, and clinical risk factors. | AUC was 0.949 in the training set and 0.932 in the validation set. | Progesterone receptor, HER2 status, clinical T stage, and time-intensity curve were significantly associated with apCR. | SHAP plots were used to show how the model reached its predictions.
rundown: The target outcome was axillary pathological complete response in patients with clinically node-positive breast cancer who received neoadjuvant chemotherapy. The authors tested seven classifiers and selected the best-performing one to build the final model.

Performance was assessed with ROC curves, AUC, calibration curves, decision curve analysis, and a confusion matrix. Multivariate analysis linked progesterone receptor, HER2, clinical T stage (OR = 0.258), and time-intensity curve to the outcome.

The authors present the model as support for personalized precision treatment and for identifying candidates for axillary de-escalation. They report following the TRIPOD-AI reporting checklist.
sources:
- peer_reviewed | Magnetic Resonance Imaging | https://doi.org/10.1016/j.mri.2026.110823 | 2026-10-21
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