Acute pancreatitis is a common clinical acute abdomen. Imaging examinations play an important role in the management of acute pancreatitis. MR imaging is a noninvasive examination with high tissue contrast and a variety of acquisition sequences that can help determine the diagnosis, complications and severity of acute pancreatitis. The acute pancreatitis classification working group modified the Atlanta classification in 2012 to improve clinical evaluations and standardize the radiologic nomenclature for acute pancreatitis. In particular, the redefinition of necrotizing pancreatitis offers a new understanding of this disease. In clinical practice, there is still a lack of unifying standards between radiologists and physicians, such as for the imaging features of pseudocysts, walled-off necrosis, peripancreatic necrosis and especially for the MR imaging features of acute pancreatitis. In this article, we review the 2012 revised Atlanta classification of acute pancreatitis and recent advances in the clinical applications of MR imaging (MRI) in acute pancreatitis by showing how MRI can provide more optimized information for clinical diagnosis and treatment plan.

Background Computed tomography (CT) or MR images may cause the severity of early acute pancreatitis (AP) to be underestimated. As an innovative image analysis method, radiomics may have potential clinical value in early prediction of AP severity. Purpose To develop a contrast‐enhanced (CE) MRI‐based radiomics model for the early prediction of AP severity. Study Type Retrospective. Subjects A total of 259 early AP patients were divided into two cohorts, a training cohort (99 nonsevere, 81 severe), and a validation cohort (43 nonsevere, 36 severe). Field Strength/Sequence 3.0T, T1‐weighted CE‐MRI. Assessment Radiomics features were extracted from the portal venous‐phase images. The "Boruta" algorithm was used for feature selection and a support vector machine model was established with optimal features. The MR severity index (MRSI), the Acute Physiology and Chronic Health Evaluation (APACHE) II, and the bedside index for severity in acute pancreatitis (BISAP) were calculated to predict the severity of AP. Statistical Tests Independent t‐test, Mann–Whitney U‐test, chi‐square test, Fisher's exact tests, Boruta algorithm, receiver operating characteristic analysis, DeLong test. Results Eleven potential features were chosen to develop the radiomics model. In the training cohort, the area under the curve (AUC) of the radiomics model, APACHE II, BISAP, and MRSI were 0.917, 0.750, 0.744, and 0.749, and the P value of AUC comparisons between the radiomics model and scoring systems were all less than 0.001. In the validation cohort, the AUC of the radiomics model, APACHE II, BISAP, and MRSI were 0.848, 0.725, 0.708, and 0.719, respectively, and the P value of AUC comparisons were 0.96 (radiomics vs. APACHE II), 0.40 (radiomics vs. BISAP), and 0.46 (radiomics vs. MRSI). Data Conclusion The radiomics model had good performance in the early prediction of AP severity. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:397–406.

Objectives This work aimed to study the early predictive value of extrapancreatic inflammation on magnetic resonance imaging (EPIM) for acute pancreatitis (AP) severity. Methods The EPIM score, magnetic resonance severity index, Acute Physiology and Chronic Health Evaluation (APACHE II) score, bedside index of severity in AP, and high-sensitivity C-reactive protein levels were evaluated for 337 AP patients. The extrapancreatic inflammation on computed tomography (EPIC) was also assessed for 86 patients undergoing computed tomography. The predictive values of these scores for severe AP and organ failure were evaluated using receiver operating characteristic curve analyses. Results Of the 337 AP patients, 55 (16.3%) had organ failure and 17 (5.0%) had severe AP. The EPIM showed a strong correlation with the EPIC (r = 0.794, P < 0.001) and had a higher correlation with the APACHE II and hospital stay compared with the EPIC. The accuracy of the EPIM in predicting severe AP and organ failure (areas under the curve, 0.844 and 0.817) was consistent with that of the APACHE II and bedside index of severity in AP, and higher than that of the magnetic resonance severity index. Conclusion The EPIM is more helpful in assessing AP severity than the EPIC and can indicate the occurrence of severe AP and organ failure early.

PurposeTo compare conventional diffusion weighted imaging (DWI), intravoxel incoherent motion imaging (IVIM) and diffusion kurtosis imaging (DKI) in differentiating malignant and benign lung lesions.MethodFifty-five consecutive patients with lung lesions underwent multiple b-value DWI. The apparent diffusion coefficient (ADC), IVIM and DKI parameters were calculated using postprocessing software and compared between the malignant and benign groups. Receiver operating characteristic (ROC) analysis was performed for all parameters.ResultsADC and D were lower in malignant lesions than in benign lesions, while Kapp was higher (P &lt; 0.05). The differences in D*, f, and Dapp between the two groups were not significant (P &gt; 0.05). The areas under the curves (AUCs) of ADC, D, and Kapp were 0.816, 0.864, and 0.822. The combination of all the significant parameters yielded an AUC of 0.880. There were no significant differences in diagnostic efficacy among ADC, D, Kapp and the predictor factor (PRE).ConclusionsIn this study, traditional DWI (ADC), IVIM (D), and DKI (Kapp) all had good diagnostic performance in differentiating malignant lung lesions from benign lesions, but the combination of ADC, D, and Kapp value had better diagnostic efficacy than these parameters alone.

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