To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 μm2/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D, MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent.

Background: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. Methods: 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, nomogram1 and nomogram2 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. Results:The independent predictors of pCR were different at the two time points. Both nomograms were found to effectively predict pCR: nomogram2 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 nomogram1 based on pre-NACT HER2, Ki67, and K trans .Conclusions: Nomograms based on quantitative DCE-MRI parameters, ADC, and clinicopathological characteristics can predict pCR in breast cancer and facilitate individualized decision-making for NACT.