Purpose: To study the correlation between apparent diffusion coefficient (ADC) and pathology in patients with undefined breast lesion, to validate how accurately ADC is related to histology, and to define a threshold value of ADC to distinguish malignant from benign lesions. Materials and Methods:Seventy-eight patients (110 lesions) were referred for positive or dubious findings. Threedimensional fast low-angle shot (3D-FLASH) with contrast injection was applied. EPI diffusion-weighted imaging (DWI) with fat saturation was performed, and ROIs were selected on subtraction 3D-FLASH images before and after contrast injection, and copied on an ADC map. Inter-and intraobserver analyses were performed.Results: At pathology 22 lesions were benign, 65 were malignant, and 23 were excluded. The ADCs of malignant and benign lesions were statistically different. In malignant tumors the ADC was (mean Ϯ SEM) 0.95 Ϯ 0.027 ϫ 10 -3mm 2 /second, and in benign tumors it was 1.51 Ϯ 0.068 ϫ 10 -3 mm 2 /second. According to receiver operating characteristic (ROC) curves, we found a threshold between malignant and benign lesions for highest sensitivity and specificity (both 86%) around 1.13 Ϯ 0.10 ϫ 10 -3 mm 2 /second. For a threshold of 0.95 Ϯ 0.10 ϫ 10 -3 mm 2 /second, specificity was 100% but sensitivity was very low. Inter-and intraobserver studies showed good reproducibility. Conclusion:The ADC may help to differentiate benign and malignant lesions with good specificity, and may increase the overall specificity of breast MRI.

• DCE-MRI-derived pharmacokinetic parameters can predict response status of neoadjuvant chemotherapy treatment. • Ktrans can better predict pCR for the triple negative group. • No pharmacokinetic parameter could predict response for the ER+/HER2- group.

Purpose: To explore the role of histogram analysis of apparent diffusion coefficient (ADC) MRI maps based on entire tumor volume data in determining pancreatic neuroendocrine tumor (PNT) grade. Methods and Materials: Retrospective evaluation of 22 patients with PNTs included low-grade (G1; n = 15), intermediate-grade (G2; n = 4), and high-grade (G3; n = 3) tumors. Regions of interest containing the lesion were drawn on every section of the ADC map containing the tumor and summated to obtain histograms for entire tumor volume. Calculated histographic parameters included mean ADC (mADC), 5th percentile ADC, 10th percentile ADC, 25th percentile ADC, 50th percentile ADC, 75th percentile ADC (ADC75), 90th percentile ADC (ADC90) and 95th percentile ADC (ADC95), skewness and kurtosis. Histogram parameters were correlated with tumor grade by repeated measures analysis of variance with Tukey-Kramer post hoc comparisons. Results: The mADC, ADC75, ADC90, and ADC95 were significantly higher in