Diffusion-weighted Imaging: New Paradigm in Diagnosis of Early Acute Pancreatitis.

BACKGROUND: Diffusion-weighted imaging (DWI) has come up as a newer diagnostic modality for the diagnosis of early functional changes in various organs including the pancreas. DWI has shown the ability of early and accurate diagnosis of inflammatory pathologies, before the appearance of morphological changes on imaging. OBJECTIVES: The objectives of this study were to study the diagnostic accuracy of DWI in the diagnosis of early acute interstitial pancreatitis. MATERIALS AND METHODS: The present retrospective observational study was conducted at the department of radiodiagnosis of a tertiary teaching hospital for 1 year. Fifty patients who underwent magnetic resonance imaging of the pancreas with clinical or laboratory diagnosis of early pancreatitis were included in the study. The diagnostic accuracy of DWI was analyzed on the basis of quantitative (apparent diffusion coefficient [ADC] values) evaluation in the diagnosis of early acute pancreatitis. Threshold values for ADC were derived for differentiation of inflamed versus normal pancreas. Sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV) were obtained by drawing the receiver operating characteristic (ROC) curve. RESULTS: The mean ADC values in the patient group (0.882 × 10 -3 mm 2 /s ± 0.102) were significantly lower than the control group (1.178 × 10 -3 mm 2 /s ± 0.232) with P < 0.001. The ROC curve deciphered a cutoff value of 0.937 × 10 -3 mm 2 /s with a sensitivity of 84%, specificity of 86%, PPV of 85.71%, and NPV of 84.31% for diagnosis of acute pancreatitis using quantitative ADC values. CONCLUSION: DWI can thus be a succinct modality for early diagnosis of pancreatitis with excellent diagnostic accuracy and superlative advantage of lack of contrast and radiation in comparison to computed tomography scan.

Résumé Contexte:L'imagerie pondérée en diffusion (DWI) est apparue comme une nouvelle modalité de diagnostic pour le diagnostic des changements fonctionnels précoces.dans divers organes, dont le pancréas. DWI a montré la capacité de diagnostic précoce et précis des pathologies inflammatoires, avant apparition de modifications morphologiques à l'imagerie.Objectifs:Les objectifs de cette étude étaient d'étudier la précision diagnostique du CFA chez le diagnostic de pancréatite interstitielle aiguë précoce.Matériels et méthodes:La présente étude observationnelle rétrospective a été menée à le service de radiodiagnostic d'un CHU pendant 1 an. Cinquante patients ayant bénéficié d'une imagerie par résonance magnétique du les pancréas présentant un diagnostic clinique ou de laboratoire de pancréatite précoce ont été inclus dans l'étude. La précision diagnostique du DWI a été analysée sur la base d'une évaluation quantitative (valeurs du coefficient de diffusion apparent [ADC]) dans le diagnostic de la pancréatite aiguë précoce. Seuil les valeurs de l'ADC ont été dérivées pour différencier le pancréas enflammé du pancréas normal. Sensibilité, spécificité, valeur prédictive positive (VPP),et des valeurs prédictives négatives (NPV) ont été obtenues en traçant la courbe des caractéristiques de fonctionnement du récepteur (ROC).Résultats:L'ADC moyenles valeurs du groupe de patients (0.882 × 10−3 mm2/s ± 0.102) étaient significativement inférieures à celles du groupe témoin (1.178 × 10−3 mm2/s ± 0.232) avec P < 0.001. La courbe ROC a décrypté une valeur seuil de 0.937 × 10−3 mm2/s avec une sensibilité de 84 %, une spécificité de 86 %, une VPP de 85.71 %, et VPN de 84.31 % pour le diagnostic de pancréatite aiguë à l'aide des valeurs quantitatives de l'ADC.Conclusion:La DWI peut donc être une modalité succincte pour le diagnostic précoce de la pancréatite avec une excellente précision diagnostique et l'avantage exceptionnel de l'absence de contraste et de rayonnement par rapport à la tomodensitométrie.

Early detection of necrosis in low-enhanced pancreatic parenchyma using contrast-enhanced computed tomography was a better predictor of clinical outcomes than pancreatic inflammation: A multicentric cohort study of severe acute pancreatitis.

OBJECTIVES: We aim to assess the early use of contrast-enhanced computed tomography (CECT) of patients with severe acute pancreatitis (SAP) using the computed tomography severity index (CTSI) in prognosis prediction. The CTSI combines quantification of pancreatic and extrapancreatic inflammation with the extent of pancreatic necrosis. METHODS: Post-hoc retrospective analysis of a large, multicentric database (44 institutions) of SAP patients in Japan. The area under the curve (AUC) of the CTSI for predicting mortality and the odds ratio (OR) of the extent of pancreatic inflammation and necrosis were calculated using multivariable analysis. RESULTS: In total, 1097 patients were included. The AUC of the CTSI for mortality was 0.65 (95 % confidence interval [CI:] [0.59-0.70]; p < 0.001). In multivariable analysis, necrosis 30-50 % and >50 % in low-enhanced pancreatic parenchyma (LEPP) was independently associated with a significant increase in mortality, with OR 2.04 and 95 % CI 1.01-4.12 (P < 0.05) and OR 3.88 and 95 % CI 2.04-7.40 (P < 0.001), respectively. However, the extent of pancreatic inflammation was not associated with mortality, regardless of severity. CONCLUSIONS: The degree of necrosis in LEPP assessed using early CECT of SAP was a better predictor of mortality than the extent of pancreatic inflammation.

Imaging abnormalities of the pancreas in diabetes: implications for diagnosis and treatment.

PURPOSE OF REVIEW: Radiographic imaging of the pancreas has drawn recent interest as pancreas volume may serve as a biomarker in identifying the likelihood of diabetes development, subtyping diabetes, and identifying prognostic indicators of poor ultimate outcomes. In this review, the role of pancreas imaging is discussed in various forms of diabetes including type 1 diabetes (T1D), type 2 diabetes (T2D), and diabetes of the exocrine pancreas, particularly diabetes following acute or chronic pancreatitis. RECENT FINDINGS: Recent literature of quantitative pancreatic imaging correlating with various forms of diabetes was reviewed. Imaging-derived pancreas volumes are lower in individuals with diabetes, in particular those with T1D. Additionally, morphologic changes, enhancement characteristics, fat content, and MRI signal changes have been observed in different diabetes subtypes. These characteristics, as well as potential confounding variables, are reviewed. Additionally, future areas of research in MRI, CT radiomics, and pancreatitis-related imaging predictors of diabetes are discussed. SUMMARY: Increased understanding of pancreas imaging features which predict diabetes and gauge prognosis has the potential to identify at-risk individuals and will become increasingly important in diabetes care. This article reviews the current knowledge of common pancreas imaging features as well as future directions of ongoing research in diabetes imaging.

Early laparoscopic cholecystectomy in acute mild gallstone pancreatitis. Is there a role for routine admission contrast-enhanced CT Scan?

PURPOSE: This study aims to evaluate the efficacy of admission contrast-enhanced CT scans in formulating strategies for performing early laparoscopic cholecystectomy in cases of acute gallstone pancreatitis. METHODS: Patients diagnosed with acute gallstone pancreatitis underwent a CT scan upon admission (after at least 24 h from symptom onset) to confirm diagnosis and assess peripancreatic fluid, collections, gallstones, and common bile duct stones. Patients with mild acute gallstone pancreatitis, following the Atlanta classification and Baltazar score A or B, were identified as candidates for early cholecystectomy (within 72 h of admission). RESULTS: Within the analyzed period, 272 patients were diagnosed with mild acute gallstone pancreatitis according to the Atlanta Guidelines. A total of 33 patients (12.1%) were excluded: 17 (6.25%) due to SIRS, 10 (3.6%) due to local complications identified in CT (Balthazar D/E), and 6 (2.2%) due to severe comorbidities. Enhanced CT scans accurately detected gallstones, common bile duct stones, pancreatic enlargement, inflammation, pancreatic collections, and peripancreatic fluid. Among the cohort, 239 patients were selected for early laparoscopic cholecystectomy. Routine intraoperative cholangiogram was conducted in all cases, and where choledocholithiasis was present, successful treatment occurred through common bile duct exploration. Only one case required conversion from laparoscopic to open surgery. There were no observed severe complications or mortality. CONCLUSION: Admission CT scans are instrumental in identifying clinically stable patients with local tomographic complications that contraindicate early surgery. Patients meeting the criteria for mild acute gallstone pancreatitis, as per Atlanta guidelines, without SIRS or local complications (Baltazar D/E), can safely undergo early cholecystectomy within the initial 72 h of admission.

Study on the Prediction of Liver Injury in Acute Pancreatitis Patients by Radiomic Model Based on Contrast-Enhanced Computed Tomography.

OBJECTIVES: to predict liver injury in acute pancreatitis (AP) patients by establishing a radiomics model based on contrast-enhanced computed tomography (CECT). METHODS: a total of 1223 radiomic features were extracted from late arterial-phase pancreatic CECT images of 209 AP patients (146 in the training cohort and 63 in the test cohort), and the optimal radiomic features retained after dimensionality reduction by least absolute shrinkage and selection operator (LASSO) were used to construct a radiomic model through logistic regression analysis. In addition, clinical features were collected to develop a clinical model, and a joint model was established by combining the best radiomic features and clinical features to evaluate the practicality and application value of the radiomic models, clinical model and combined model. RESULTS: four potential features were selected from the pancreatic parenchyma to construct the radiomic model, and the area under the receiver operating characteristic curve (AUC) of the radiomic model was significantly greater than that of the clinical model for both the training cohort (0.993 vs. 0.653, p = 0.000) and test cohort (0.910 vs. 0.574, p = 0.000). The joint model had a greater AUC than the radiomics model for both the training cohort (0.997 vs. 0.993, p = 0.357) and test cohort (0.925 vs. 0.910, p = 0.302). CONCLUSIONS: the radiomic model based on CECT has good performance in predicting liver injury in AP patients and can guide clinical decision-making and improve the prognosis of patients with AP.

Predictive value of diaphragmatic thickness fraction and integrated pulmonary index on extubation outcome in patients with severe acute pancreatitis.

OBJECTIVE: This study was performed to explore the predictive value of the diaphragmatic thickness fraction (DTF) combined with the integrated pulmonary index (IPI) for the extubation outcome in patients with severe acute pancreatitis (SAP). METHODS: This prospective study involved 93 patients diagnosed with SAP and treated with mechanical ventilation in our hospital from October 2020 to September 2023. The patients were divided into a successful extubation group (61 patients) and an extubation failure group (32 patients) based on the extubation outcomes. The predictive value of the DTF, IPI, and their combination for extubation failure was analyzed. RESULTS: The DTF and IPI were independent risk factors for extubation failure in patients with SAP undergoing mechanical ventilation. In addition, the combination of the DTF and IPI showed predictive value for extubation failure in these patients. CONCLUSION: The DTF and IPI hold predictive value for extubation failure in patients with SAP undergoing mechanical ventilation, and their combined use may improve the predictive efficiency.

A deep learning-powered diagnostic model for acute pancreatitis.

BACKGROUND: Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis. MATERIALS AND METHODS: In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People's Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model's performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU). RESULTS: Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978-0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790-0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis. CONCLUSION: The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition. TRIAL REGISTRATION: This is a retrospective study.

Usefulness of pancreatic volume quantitative analysis as a predictor of development and severity of post-endoscopic retrograde cholangiopancreatography pancreatitis.

BACKGROUND: Post-endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP) is one of the most common and serious adverse events associated with ERCP. Thus, we aimed to investigate the usefulness of pre-ERCP pancreatic volume, which is deeply involved in exocrine pancreatic function, as a predictor of PEP development and severity. METHODS: In total, 1107 patients who underwent their first ERCP were recruited from January 2012 to December 2022 for this retrospective study. Pancreatic volume was measured by cross-sectional analysis using pre-ERCP computed tomography images. The potential risk factors for PEP were analyzed using multivariate logistic regression. RESULTS: Of the 745 patients included in the study, 34 (4.6 %) developed PEP: severe, moderate, or mild PEP in 1, 7, and 26 cases, respectively. Multivariate analysis revealed that only a large pancreatic volume (>70 cm3) was an independent risk factor for the development of PEP (odds ratio, 7.98; 95 % confidence interval, 11.80-67.50; P < 0.001). Additionally, the incidence of PEP was significantly higher in patients with a pancreatic volume >70 cm3 than in those with a pancreatic volume ≤70 cm3 (18.5 % [31/168] vs. 0.5 % [3/577]; P < 0.001). Also, the association between the pre-ERCP pancreatic volume and PEP severity was positively correlated (r = 0.625, P < 0.005), with a larger pancreatic volume corresponding to increased PEP severity. CONCLUSIONS: A large pancreatic volume before ERCP may be a novel risk factor for PEP incidence and severity. This finding suggests that quantitative analysis of the pre-ERCP pancreatic volume could be a useful predictor of PEP.

Clinical characteristics, treatment and outcome of pembrolizumab-induced acute pancreatitis.

Acute pancreatitis (AP) is a rare adverse event of pembrolizumab with unclear clinical features. This study investigated the clinical features of pembrolizumab-induced AP to provide a reference for prevention and treatment. Case reports, case series and clinical studies of pembrolizumab-induced AP were collected by searching Chinese and English databases up to January 31, 2024. Thirty-one patients were included, with a median age of 59 years (range 39, 82). The median time from administration to onset of AP was 5.05 months (range 0.5, 16) and the median cycle was 7 cycles (range 1, 35). Twenty-two (71.0%) patients had elevated pancreatic amylase with a median value of 860 IU/L (range 105-12562), and 16 (51.6%) patients had elevated lipase with a median value of 282 IU/L (range 153-1034). Pancreatic biopsy showed neutrophil infiltration (9.7%) and lymphocyte infiltration (6.5%). Immunohistochemical staining showed CD8 dominated inflammatory infiltration (6.5%). The computed tomography showed diffuse enlargement (51.6%) and focal enlargement (51.6%) of the pancreas. Endoscopic ultrasound showed enlarged hypoechoic pancreas(16.1%). PET/CT showed increased FDG uptake (16.1%). The magnetic resonance cholangial pancreatography showed narrowing of main pancreatic duct (12.9%). AP symptoms and pancreatic enzymes improved after discontinuation of pembrolizumab and administration of steroids and infliximab. Clinicians should be aware that AP is a rare adverse reaction to pembrolizumab. Pembrolizumab induced AP can be initiated with steroids for control, and infliximab can be initiated with steroid-refractory AP.

Acute pancreatitis and computed tomography: Interest of portal venous phase alone in the initial phase.

BACKGROUND: There are no guidelines in the literature for the use of a computed tomography (CT) protocol in the initial phase of acute pancreatitis (AP). PURPOSE: To evaluate the contribution of single portal venous phase CT compared to triple-phase CT protocol, performed in the initial phase of AP for severity assessment. MATERIAL AND METHODS: In this retrospective study, a total of 175 patients with acute pancreatitis who underwent initial triple-phase CT protocol (non-contrast, arterial phase, and portal venous phase) between D3 and D7 after the onset of symptoms were included. Analysis of AP severity and complications was independently assessed by two readers using three validated CT severity scores (CTSI, mCTSI, EPIC). All scores were applied to the triple-phase CT protocol and compared to the single portal venous phase. Inter-observer analyses were also performed. RESULTS: No significant difference whatever the severity score was observed after analysis of the single portal venous phase compared with the triple-phase CT protocol (interstitial edematous pancreatitis: CTSI: 2 vs. 2, mCTSI: 2 vs. 2, EPIC: 1 vs. 1; necrotizing pancreatitis: CTSI: 6 vs. 6, mCTSI: 8 vs. 8, EPIC: 5 vs. 5). Inter-observer agreement was excellent (ICC = 0.96-0.99), whatever the severity score. CONCLUSION: A triple-phase CT protocol performed at the initial phase of AP was no better than a single portal venous for assessing the severity of complications and could lead to a 63% reduction in irradiation.

Endoscopic Retrograde Cholangiopancreatography for the Management of Pancreatic Duct Leaks and Fistulas.

Pancreatic duct (PD) leaks are a common complication of acute and chronic pancreatitis, trauma to the pancreas, and pancreatic surgery. Diagnosis of PD leaks and fistulas is often made with contrast-enhanced pancreatic protocol computed tomography or magnetic resonance imaging with MRCP. Endoscopic retrograde pancreatography with pancreatic duct stenting in appropriately selected patients is often an effective treatment, helps to avoid surgery, and is considered first-line therapy in cases that fail conservative management.

Early prediction of acute gallstone pancreatitis severity: a novel machine learning model based on CT features and open access online prediction platform.

BACKGROUND: Early diagnosis of acute gallstone pancreatitis severity (GSP) is challenging in clinical practice. We aimed to investigate the efficacy of CT features and radiomics for the early prediction of acute GSP severity. METHODS: We retrospectively recruited GSP patients who underwent CT imaging within 48 h of admission from tertiary referral centre. Radiomics and CT features were extracted from CT scans. The clinical and CT features were selected by the random forest algorithm to develop the ML GSP model for the identification of severity of GSP (mild or severe), and its predictive efficacy was compared with radiomics model. The predictive performance was assessed by the area under operating characteristic curve. Calibration curve and decision curve analysis were performed to demonstrate the classification performance and clinical efficacy. Furthermore, we built a web-based open access GSP severity calculator. The study was registered with ClinicalTrials.gov (NCT05498961). RESULTS: A total of 301 patients were enrolled. They were randomly assigned into the training (n = 210) and validation (n = 91) cohorts at a ratio of 7:3. The random forest algorithm identified the level of calcium ions, WBC count, urea level, combined cholecystitis, gallbladder wall thickening, gallstones, and hydrothorax as the seven predictive factors for severity of GSP. In the validation cohort, the areas under the curve for the radiomics model and ML GSP model were 0.841 (0.757-0.926) and 0.914 (0.851-0.978), respectively. The calibration plot shows that the ML GSP model has good consistency between the prediction probability and the observation probability. Decision curve analysis showed that the ML GSP model had high clinical utility. CONCLUSIONS: We built the ML GSP model based on clinical and CT image features and distributed it as a free web-based calculator. Our results indicated that the ML GSP model is useful for predicting the severity of GSP.

ML GSP model based on machine learning has good severity discrimination in both training and validation cohorts (0.916 (0.872­0.958), 0.914 (0.851­0.978), respectively).We built an online user-friendly platform for the ML GSP model to help clinicians better identify the severity of GSP.

Artificial intelligence-based tools with automated segmentation and measurement on CT images to assist accurate and fast diagnosis in acute pancreatitis.

OBJECTIVES: To develop an artificial intelligence (AI) tool with automated pancreas segmentation and measurement of pancreatic morphological information on CT images to assist improved and faster diagnosis in acute pancreatitis. METHODS: This study retrospectively contained 1124 patients suspected for AP and received non-contrast and enhanced abdominal CT examination between September 2013 and September 2022. Patients were divided into training (N = 688), validation (N = 145), testing dataset [N = 291; N = 104 for normal pancreas, N = 98 for AP, N = 89 for AP complicated with PDAC (AP&PDAC)]. A model based on convolutional neural network (MSAnet) was developed. The pancreas segmentation and measurement were performed via eight open-source models and MSAnet based tools, and the efficacy was evaluated using dice similarity coefficient (DSC) and intersection over union (IoU). The DSC and IoU for patients with different ages were also compared. The outline of tumour and oedema in the AP and were segmented by clustering. The diagnostic efficacy for radiologists with or without the assistance of MSAnet tool in AP and AP&PDAC was evaluated using receiver operation curve and confusion matrix. RESULTS: Among all models, MSAnet based tool showed best performance on the training and validation dataset, and had high efficacy on testing dataset. The performance was age-affected. With assistance of the AI tool, the diagnosis time was significantly shortened by 26.8% and 32.7% for junior and senior radiologists, respectively. The area under curve (AUC) in diagnosis of AP was improved from 0.91 to 0.96 for junior radiologist and 0.98 to 0.99 for senior radiologist. In AP&PDAC diagnosis, AUC was increased from 0.85 to 0.92 for junior and 0.97 to 0.99 for senior. CONCLUSION: MSAnet based tools showed good pancreas segmentation and measurement performance, which help radiologists improve diagnosis efficacy and workflow in both AP and AP with PDAC conditions. ADVANCES IN KNOWLEDGE: This study developed an AI tool with automated pancreas segmentation and measurement and provided evidence for AI tool assistance in improving the workflow and accuracy of AP diagnosis.

Opportunistic screening for long-term muscle wasting in critically ill patients: insights from an acute pancreatitis cohort.

OBJECTIVES: To assess the feasibility of long-term muscle monitoring, we implemented an AI-guided segmentation approach on clinically indicated Computed Tomography (CT) examinations conducted throughout the hospitalization period of patients admitted to the intensive care unit (ICU) with acute pancreatitis (AP). In addition, we aimed to investigate the potential of muscle monitoring for early detection of patients at nutritional risk and those experiencing adverse outcomes. This cohort served as a model for potential integration into clinical practice. MATERIALS: Retrospective cohort study including 100 patients suffering from AP that underwent a minimum of three CT scans during hospitalization, totaling 749 assessments. Sequential segmentation of psoas muscle area (PMA) was performed and was relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan was calculated. Subgroup and outcome analyses were performed including ANOVA. Discriminatory power of muscle decay rates was evaluated using ROC analysis. RESULTS: Monitoring PMA decay revealed significant long-term losses of 48.20% throughout the hospitalization period, with an average daily decline of 0.98%. Loss rates diverged significantly between survival groups, with 1.34% PMA decay per day among non-survivors vs. 0.74% in survivors. Overweight patients exhibited significantly higher total PMA losses (52.53 vs. 42.91%; p = 0.02) and average PMA loss per day (of 1.13 vs. 0.80%; p = 0.039). The first and the maximum decay rate, in average available after 6.16 and 17.03 days after ICU admission, showed convincing discriminatory power for survival in ROC analysis (AUC 0.607 and 0.718). Both thresholds for maximum loss (at 3.23% decay per day) and for the initial loss rate (at 1.98% per day) proved to be significant predictors of mortality. CONCLUSIONS: The innovative AI-based PMA segmentation method proved robust and effortless, enabling the first comprehensive assessment of muscle wasting in a large cohort of intensive care pancreatitis patients. Findings revealed significant muscle wasting (48.20% on average), particularly notable in overweight individuals. Higher rates of initial and maximum muscle loss, detectable early, correlated strongly with survival. Integrating this tool into routine clinical practice will enable continuous muscle status tracking and early identification of those at risk for unfavorable outcomes.

Construction and alidation of a severity prediction model for acute pancreatitis based on CT severity index: A retrospective case-control study.

To construct and internally and externally validate a nomogram model for predicting the severity of acute pancreatitis (AP) based on the CT severity index (CTSI).A retrospective analysis of clinical data from 200 AP patients diagnosed at the Hefei Third Clinical College of Anhui Medical University from June 2019 to June 2022 was conducted. Patients were classified into non-severe acute pancreatitis (NSAP, n = 135) and severe acute pancreatitis (SAP, n = 65) based on final clinical diagnosis. Differences in CTSI, general clinical features, and laboratory indicators between the two groups were compared. The LASSO regression model was used to select variables that might affect the severity of AP, and these variables were analyzed using multivariate logistic regression. A nomogram model was constructed using R software, and its AUC value was calculated. The accuracy and practicality of the model were evaluated using calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed using the bootstrap method. Finally, 60 AP patients treated in the same hospital from July 2022 to December 2023 were selected for external validation.LASSO regression identified CTSI, BUN, D-D, NLR, and Ascites as five predictive factors. Unconditional binary logistic regression analysis showed that CTSI (OR = 2.141, 95%CI:1.369-3.504), BUN (OR = 1.378, 95%CI:1.026-1.959), NLR (OR = 1.370, 95%CI:1.016-1.906), D-D (OR = 1.500, 95%CI:1.112-2.110), and Ascites (OR = 5.517, 95%CI:1.217-2.993) were independent factors influencing SAP. The established prediction model had a C-index of 0.962, indicating high accuracy. Calibration curves demonstrated good consistency between predicted survival rates and actual survival rates. The C-indexes for internal and external validation were 0.935 and 0.901, respectively, with calibration curves close to the ideal line.The model based on CTSI and clinical indicators can effectively predict the severity of AP, providing a scientific basis for clinical decision-making by physicians.

The impact of skeletal muscle index at the third lumbar spine on nosocomial deterioration and short-term prognosis in acute pancreatitis: a retrospective observational study.

Objective: To investigate the impact of the third lumbar skeletal muscle index (L3-SMI) assessed by CT on the in-hospital severity and short-term prognosis of acute pancreatitis. Methods: A total of 224 patients with severe acute pancreatitis admitted to Yantaishan Hospital from January 2021 to June 2022 were selected as the subjects. Based on the in-hospital treatment outcomes, they were divided into a mortality group of 59 cases as well as a survival group of 165 cases. Upon admission, general information such as the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, along with the abdominal CT images of each patient, were analyzed. The L3-SMI was calculated, and the Modified CT Severity Index (MCTSI) and Balthazar CT grade were used to assess the severity of in-hospital complications of acute pancreatitis. The evaluation value of L3-SMI for the prognosis of severe acute pancreatitis was analyzed, as well as the factors influencing the prognosis of severe acute pancreatitis. Results: No statistically significant differences in gender, age, BMI, etiology, duration of anti-inflammatory drug use, and proportion of surgical patients between the survival and mortality groups were observed. But the mortality group showed higher proportions of patients with an elevated APACHE II score upon admission, mechanical ventilation, and renal replacement therapy, compared to the survival group, with statistically significant differences (P < 0.001). Furthermore, the mortality group had higher MCTSI scores (6.42 ± 0.69) and Balthazar CT grades (3.78 ± 0.45) than the survival group, with statistically significant differences (P < 0.001). The mortality group also had a lower L3-SMI (39.68 ± 3.25) compared to the survival group (42.71 ± 4.28), with statistically significant differences (P < 0.001). L3-SMI exhibited a negative correlation with MCTSI scores and Balthazar CT grades (r = -0.889, -0.790, P < 0.001). Logistic regression analysis, with mortality of acute pancreatitis patients as the dependent variable and MCTSI scores, Balthazar CT grades, L3-SMI, APACHE II score upon admission, mechanical ventilation, and renal replacement therapy as independent variables, revealed that MCTSI scores and L3-SMI were risk factors for mortality in acute pancreatitis patients (P < 0.001). Logistic regression analysis using the same variables confirmed that all these factors were risk factors for mortality in acute pancreatitis patients. Conclusion: This study confirmed that diagnosing muscle depletion using L3-SMI is a valuable radiological parameter for predicting in-hospital severity and short-term prognosis in patients with acute pancreatitis.

Role of imaging in evaluating the complications of endoscopic management of pancreatic fluid collections in acute pancreatitis.

Acute pancreatitis is associated with local and systemic complications. Pancreatic fluid collection (PFC) is the most common local complication. Infected or symptomatic PFCs need drainage. Endoscopic drainage (ED) is the first-line procedure for accessible PFCs adjacent to the stomach and duodenum. ED is performed under endoscopic ultrasound (EUS) guidance. The technical and clinical success rates of EUS-guided ED in well-encapsulated PFCs are high. ED of poorly encapsulated PFCs is associated with complications. Bleeding and perforation are the most common complications. Contrast-enhanced computed tomography is critical in planning ED and early detection and management of complications. With the increasing utilization of ED for PFC, the radiologist must be familiar with the ED techniques, types of stents, and the complications related to ED. In this review, we discuss the technical aspects of the ED as well as the imaging findings of ED-related complications.

Anti-programmed-death-receptor-1 (PD-1) antibody (Pembrolizumab) induced pancreatitis diagnosed on 18F-FDG PET/CT.

Immunotherapy related adverse events are commonly seen with immune check point inhibitors therapy. We report the case of a 40-year-old female diagnosed with stage IVB endometroid grade III endometrial cancer, on pembrolizumab immunotherapy, an anti-programmed-death-receptor-1 (PD-1) antibody. Patient was referred for 18F-FDG PET/CT for restaging. 18F-FDG PET/CT demonstrated diffuse increased FDG uptake throughout the body of the pancreas associated with fat stranding in the peripancreatic region, suggestive of pembrolizumab-induced pancreatitis. The diagnosis was confirmed by elevated amylase and lipase levels. immune-related adverse events (irAE) are frequently identified on 18F-FDG PET-CT, which may lead to early diagnosis, close clinical follow-up, and appropriate clinical management of immune-related adverse events.

Concomitant Pancreatitis and Pancreatic Neuroendocrine Tumor on MRI and 68 Ga-DOTATATE PET/CT.

ABSTRACT: A 66-year-old woman had an episode of pancreatitis with symptoms starting in October 2023. MRI showed an enhancing soft tissue mass along the superior border of the pancreatic body, as well as signal changes in the pancreatic body and the tail consistent with pancreatitis. The 68 Ga-DOTATATE PET/CT demonstrated intense radiotracer uptake within the peripancreatic soft tissue mass, significantly greater than the spleen background. Biopsy of this peripancreatic mass revealed well-differentiated grade 1 neuroendocrine tumor. The body and tail of the pancreas showed diffusely increased 68 Ga-DOTATATE uptake but obviously lower than the peripancreatic neuroendocrine tumor, consistent with pancreatitis.