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September 27, 2025Open Access

Personalized prediction of pathological complete response in breast cancer neoadjuvant therapy: a nomogram combining quantitative MRI biomarkers and molecular subtypes

Key Points

The model achieved an accuracy of 77.44% in predicting neoadjuvant therapy outcomes with MRI, supporting its diagnostic role.
Independent predictors of pathological complete response included ER-negative and HER2-positive status, indicating significant molecular characteristics.
The predictive nomogram showed strong discrimination with AUC values of 0.894 in the training cohort and 0.888 in the validation cohort.
Challenges in diagnostic accuracy were noted with ER-negative tumors and the absence of ductal carcinoma in situ, highlighting areas for improvement.

Abstract

Purpose In this study, we aimed to determine the diagnostic performance of MRI in assessing neoadjuvant therapy (NAT) response, investigate determinants of its accuracy, and develop a nomogram for predicting pathological complete response (pCR) following NAT. Methods A retrospective analysis was conducted on 554 female patients who received NAT between January 2019 and December 2022 and underwent MRI scans pre- and post-treatment. Clinicopathological and MRI characteristics were collected. Univariable logistic regression identified predictors of diagnostic accuracy. Patients were then randomly allocated to training (n=388, 70%) and validation (n=166, 30%) cohorts. Using multivariable logistic regression in the training cohort, we identified independent predictors of pCR and constructed a predictive nomogram. Model performance was assessed in both cohorts using receiver operating characteristic (ROC) curves, area under the curve (AUC), and goodness-of-fit tests. Results The overall accuracy of breast MRI in evaluating NAT response was 77.44%. Multivariable analysis identified three factors independently associated with reduced MRI accuracy: ER-negative status, absence of ductal carcinoma in situ (DCIS), and coexistence of mass lesions with non-mass enhancement (NME). Independent predictors of pCR included: ER-negative, HER2-positive, without the presence of DCIS, the coexistence of mass lesions and NME on pre-NAT MRI, radiologic complete remission (rCR), smaller tumor size, and increasing/plateau TIC on post-NAT MRI. The predictive nomogram demonstrated robust discrimination, with AUC values of 0.894 (95% CI: 0.857–0.932) in the training cohort and 0.888 (95% CI: 0.841–0.935) in the validation cohort. Conclusion Breast MRI accuracy was reduced in ER-negative tumors, those lacking DCIS, and lesions exhibiting coexistent mass and NME. A clinicopathological-MRI integrated nomogram demonstrated robust predictive performance for pCR after NAT completion, potentially aiding in surgical strategy planning.

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