Vision Transformer for Pretreatment Prediction of Pathologic Complete Response in Multi-center Breast DCE-MRI.

Authors

Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldstein

Published in

Academic radiology. Oct 05, 2026. Epub Oct 05, 2026.

Abstract

Pretreatment prediction of pathologic complete response (pCR) is clinically important for guiding neoadjuvant chemotherapy (NAC), but robust multi-center benchmarks with standardized evaluation protocols remain limited. We evaluated a Vision Transformer (ViT) approach for pCR prediction from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
We used BreastDCEDL, a public multi-center dataset comprising 2070 patients from the I-SPY1, I-SPY2, and Duke cohorts with predefined splits. Pre-contrast, early post-contrast, and late post-contrast phases were mapped to RGB channels for transfer learning from ImageNet-pretrained backbones. The held-out test set (n=175) included 32 pCR+ in I-SPY2 (99), 8 in Duke (41), and 12 in I-SPY1 (35). For I-SPY2, ViT scores were combined with clinical variables (age, HR/HER2 status, tumor volume, genomic score, treatment arm) via a Random Forest classifier.
The ViT achieved AUC 0.72 (95% CI 0.64-0.80), sensitivity 0.27 (95% CI 0.15-0.40), and specificity 0.96 (95% CI 0.92-0.99). Performance varied across cohorts (AUC: I-SPY2 0.78, 95% CI 0.68-0.86, I-SPY1 0.68, 95% CI 0.46-0.89, Duke 0.54, 95% CI 0.27-0.80). In I-SPY2, clinical variables increased AUC to 0.85 overall (n=99), a difference that was not statistically significant (DeLong P=0.06).
Cross-cohort variation highlights challenges in developing generalizable pretreatment response models under real-world heterogeneity. The high specificity supports potential utility for identifying non-responders, enabling earlier transition to alternative strategies. Code, data, and predefined splits are publicly available, establishing a reproducible benchmark.

PMID:
42833970
Bibliographic data and abstract were imported from PubMed on 06 Oct 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.