A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study - Zhu, Xuelin; Shen, Jing; Zhang, Huanlei; Wang, Xiulin; Zhang, Huihui; Yu, Jing; Zhang, Qing; Song, Dongdong; Guo, Liping; Zhang, Dianlong; Zhu, Ruiping; Wu, Jianlin (Frontiers in Oncology, 2022)
Objective: To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST). Methods: We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the model was evaluated by the Concordance Index (C-index) and calibration curves. The clinical net benefit of the model was evaluated using decision curve analysis (DCA). Results: The mpMRI RAD-score had the highest diagnostic performance, with an area under the curve (AUC) of 0.85 among the four RAD-scores. The RAD-score, T stage, human epidermal growth factor receptor-2 (HER2) status, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The C-index of the training cohort and validation cohort of the predictive model based on these factors was 0.93 and 0.85, respectively. The calibration curve showed that the predicted probabilities agreed well with the actual probabilities, and the decision curve analysis indicated that the combined model had high clinical predictive efficacy. Conclusion: A comprehensive new model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.