Tumor-preserving Deformable Registration of Longitudinal DCE Breast MRI during Neoadjuvant Chemotherapy

Purpose To develop a deep learning-based deformable registration method for dynamic contrast-enhanced (DCE) breast MRI that preserves tumor regions while maintaining global anatomic alignment during neoadjuvant chemotherapy (NAC) response assessment. Materials and Methods This retrospective study included internal and external cohorts of patients with breast cancer who were undergoing NAC. The internal cohort comprised patients who underwent DCE MRI from 2017 to 2020, and the external cohort was derived from the I-SPY2 trial. A conditional pyramid registration network integrating unsupervised keypoint detection with a volume-preserving mechanism was developed. Registration performance was evaluated using the Dice similarity coefficient (DSC), average landmark error, and tumor volume difference. A local-global biomarker derived from registered images was evaluated for predicting pathologic complete response (pCR) using the area under the receiver operating characteristic curve (AUC) and accuracy. Paired t tests were used for statistical comparisons. Results In 314 patients (all female; age, 50.6 years ± 12.0 [SD]) with 1630 scans in the internal cohort, the proposed method achieved a DSC of 0.95 ± 0.02, an average landmark error of 5.35 mm ± 3.46, and a tumor volume difference of 11.0% ± 10.7. In 100 patients (all female; age, 48.5 years ± 12.3) with 372 scans in the external cohort, the method achieved a DSC of 0.91 ± 0.09 and a tumor volume difference of 15.5% ± 13.8. Improvements in landmark distance and tumor preservation were statistically significant (P < .05) compared with most methods. For pCR prediction, incorporation of the proposed biomarker achieved an AUC of 0.81 ± 0.04 and an accuracy of 72.1% ± 5.0. Conclusion The proposed framework improved anatomic alignment while preserving tumor volume in longitudinal DCE breast MRI during NAC response assessment and enabled a registration-based biomarker for predicting treatment response. Keywords: MR Imaging, Image Postprocessing, Breast, Neural Networks, Radiomics, Prognosis, DCE Breast MRI, Deep Learning, Deformable Registration, Neoadjuvant Chemotherapy, Unsupervised Keypoint Detection Supplemental material is available for this article. © RSNA, 2026 See also the commentary by Zhang in this issue.