Diffusion-weighted (DW) MR imaging is used for various aspects of the evaluation of pancreas lesions such as detection, diagnosis, and predicting patient prognosis ^1-3. Apparent diffusion coefficient (ADC) histogram analysis is a reproducible technique, and several ADC histogram parameters are more complementarily or effectively reflect the microstructure of tumors ⁴. However, the utility of ADC histogram analysis in characterizing solid pancreatic masses has not been elucidated. Therefore, the purpose of this study was to investigate whether ADC histogram analysis of DW imaging can characterize solid pancreatic masses.One hundred ten patients with histologically confirmed 66 pancreatic adenocarcinomas (PACs), 27 neuroendocrine tumors (NETs), and 17 mass-forming autoimmune pancreatitis (AIPs) underwent respiratory-triggered fat-suppressed single-shot echo-planar DW 3.0-T MRI with b-values of 0, 200, 400, and 800 s/mm². The pulse sequence parameters were as follows: repetition time, which was based on the respiratory interval; echo time, 60 ms; flip angle, 90°; field of view, 350 mm; matrix, 60 x 112; number of excitations, 2 (b-values of 0, 200, and 400 s/mm²) or 4 (b-value of 800 s/mm²); sensitivity encoding acceleration factor, 4; and acquisition time, approximately 3–4 min. Frequency-selective fat saturation was used to reduce chemical shift artifacts. A free-hand region of interest on each equatorial plane delineated the tumors. We evaluated the pixel distribution histogram parameters of the ADC values derived from b-values of 0 and 200 s/mm² (ADC₂₀₀), 0 and 400 s/mm² (ADC₄₀₀), or 0 and 800 s/mm² (ADC⁸⁰⁰). The histogram parameters (i.e., mean, coefficient of variation (CV), kurtosis, skew, and entropy) of the ADC values were compared between PACs, NETs, and AIPs by using the Kruskal-Wallis test, followed by the Mann-Whitney U test. Receiver operating characteristic (ROC) curve analyses for histogram parameters of ADC₂₀₀, ADC₄₀₀, and ADC₈₀₀ were generated to evaluate accuracy in diagnosing PACs, NETs, and AIPs.ADC histogram results are summarized in Table 1. Mean ADC₂₀₀ was significantly higher in NETs than in PACs (P=0.005) and AIPs (P=0.022). Mean ADC₈₀₀ was significantly lower in AIPs than in PACs (P=0.003) and NETs (P=0.014). Kurtosis showed significantly lower in NETs than in PACs with all b-value combinations (P=0.038 for ADC₂₀₀, and P<0.001 for ADC₄₀₀ and ADC₈₀₀), and AIP with ADC₄₀₀ (P=0.008). Skew of ADC₄₀₀ and ADC₈₀₀ showed significantly lower in NETs than in PACs (P<0.001 for ADC₄₀₀ and ADC₈₀₀) and AIPs (P=0.006 for ADC₄₀₀ and P=0.001 for ADC₈₀₀). With all b-value combinations, the entropy of ADC was significantly lower in NETs than in PACs (P=0.002 for ADC₂₀₀, P=0.001 for ADC₄₀₀, and P<0.001 for ADC₈₀₀) and AIPs (P<0.001 for ADC₂₀₀ and ADC₄₀₀, and P=0.005 for ADC₈₀₀). For differentiating PACs from AIPs, the area under the curve (AUC) for mean ADC₈₀₀ was 0.743. The entropy of ADC with every b-value combination showed the highest area under the ROC curve for differentiating NETs from PACs (0.740 for ADC₂₀₀, 0.736 for ADC₄₀₀, and 0.758 for ADC₈₀₀) and AIPs (0.781 for ADC₂₀₀, 0.785 for ADC₄₀₀, and 0.765 for ADC₈₀₀).We used histogram analysis to evaluate not only mean ADC, but also CV, skew, kurtosis, and entropy, which reflect the distribution of ADC values. There have been no reports assessing the usefulness of ADC histogram analysis for characterizing solid pancreatic masses. In our study, a significantly lower entropy of ADC was found in NETs. The entropy of ADC with every b-value combination showed the highest area under the ROC curve for differentiating NETs from PACs and AIPs. Entropy describes the variation in ADC histogram. Therefore, a lower entropy of ADC in NET could be expected as it is more homogenous compared with PAC and AIP.ADC histogram analysis could be helpful for diagnosing solid pancreatic masses, especially in NETs that have higher entropy characteristics with promising potential for differentiating from PACs and AIPs.