Nomogram for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using MRI-based Intratumoral Heterogeneity Quantification

Abstract

Background Intratumoral heterogeneity (ITH) in breast cancer contributes to treatment failure and relapse. Noninvasive methods to quantify ITH are currently limited. Purpose To quantify ITH in breast cancer using pretreatment MRI, develop a nomogram to predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) and recurrence-free survival (RFS), and investigate biologic pathways associated with nomogram scores. Materials and Methods This retrospective study included patients with breast cancer who underwent NAC at nine centers between April 1988 and December 2023. Tumor regions on MRI scans were clustered and integrated with global pixel distribution patterns to calculate ITH scores. A nomogram for predicting pCR was developed using multivariable logistic regression. A survival dataset was used to evaluate the association between nomogram score and RFS, and a genomics dataset was used to explore the relationship between nomogram score and biologic pathways. Results The study included 1448 women (median age, 49 years [IQR, 43-54 years]). To predict pCR to NAC, the 505 patients from center A served as the training set, and the patients from center B, centers C-F, and center G served as three external validation sets (n = 331, 107, and 384, respectively). The survival set included patients from centers A and H (n = 179), and the genomics set included patients from center I (n = 74). The ITH score was an independent predictor of pCR (odds ratio, 0.12 [95% CI: 0.03, 0.43]; P < .001). The nomogram model achieved area under the receiver operating characteristic curve values of 0.82, 0.81, and 0.79, respectively, in the three external validation sets. A lower nomogram score was correlated with poorer RFS (hazard ratio, 4.04 [95% CI: 1.90, 8.60]; P < .001) and was associated with upregulation of biologic pathways related to tumor proliferation. Conclusion A nomogram model combining ITH score and clinicopathologic variables showed good performance in predicting pCR to NAC and RFS. Published under a CC BY 4.0 license. Supplemental material is available for this article.