Nomogram based on quantitative dynamic contrast-enhanced magnetic resonance imaging, apparent diffusion coefficient, and clinicopathological features for early prediction of pathologic complete response in breast cancer patients receiving neoadjuvant chemotherapy

The aim of this study was to develop two nomograms for predicting pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) for breast cancer based on quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), and clinicopathological characteristics at two time-points: before and after two cycles of NACT, respectively.

3.0 T MRI scans were performed before and after 2 cycles of NACT in 215 patients. A total of 74 female patients with stage II-III breast cancer were included. According to univariate and multivariate logistic regression analysis, nomogram model 1 and nomogram model 2 were developed based on the independent predictors for pCR before and after 2 cycles of NACT, respectively. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope.

The independent predictors of pCR were different at the two time points. Both nomograms were found to effectively predict pCR: nomogram model 2 based on Ki67, ΔK_trans%, and ΔADC% after 2 cycles of NACT showed better predictive discrimination [AUC =0.900 (0.829, 0.970) vs. 0.833 (0.736, 0.930)] and calibration ability (mean absolute error of the agreement: 0.017 vs. 0.051) compared to nomogram model 1 based on pre-NACT HER2, Ki67, and K_trans.

Nomograms based on quantitative DCE-MRI parameters, ADC, and clinicopathological characteristics can predict pCR in breast cancer and facilitate individualized decision-making for NACT.