Incremental Value of Synthetic MRI Radiomics in Predicting Neoadjuvant Response in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.

Authors

Siyi Chen, Yongxin Chen, Chen Jin, Bingjie Zheng, Shishen Ding, Zijin Weng, Wenjie Tang, Weifeng Liu, Yihong Lin, Qingcong Kong, Qianwei Zhou, Yuan Guo

Published in

Radiology. Imaging cancer. Volume 8. Issue 5. Pages e250758.

Abstract

Purpose To evaluate the added value of a synthetic radiomics model (S-Radiomics) that integrates topologic and Hessian-based features with traditional radiomic features for predicting a pathologic complete response (pCR) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Materials and Methods Patients with HER2-positive breast cancer from three centers were retrospectively included between January 2018 and January 2024. Patients from centers 1 and 2 (n = 201) were randomly assigned to training (n = 150) and internal testing (n = 51) sets, whereas patients from center 3 (n = 82) constituted external testing set I. An additional genomic set (n = 64, I-SPY 2) and external testing set II (n = 57, I-SPY 1) were used for independent validation. Multivariable logistic regression with all clinical variables was used to identify independent predictors (P < .05) and develop the clinical model, adjusting for potential confounders. Traditional radiomics model (T-Radiomics) and S-Radiomics were constructed. Performance was evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement, net reclassification index, and survival analysis. Results A total of 404 female patients with HER2-positive breast cancer (mean age ± SD, 50 years ± 9) were included. The S-Radiomics outperformed the T-Radiomics on both the internal (AUC, 0.86 vs 0.78; P = .37) and external test (AUC, 0.84 vs 0.73; P = .11) sets, with improvements in the integrated discrimination improvement (P = .24 and .01) and net reclassification index (P = .21 and .01). Shapley additive explanations analysis revealed topologic features as the most important predictors. For center 1, survival analysis revealed longer disease-free survival in predicted responders (P = .02). Conclusion The S-Radiomics outperformed the T-Radiomics in predicting pathologic complete responses in patients with HER2-positive breast cancer. Keywords: Radiogenomics, MRI, Breast, Human Epidermal Growth Factor Receptor 2, Breast Cancer, Neoadjuvant Chemotherapy, Pathologic Complete Response, Radiomics Supplemental material is available for this article. © RSNA, 2026.

PMID:
42599169
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.

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