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.

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.

Diagnostic performance of acoustic radiation force impulse for acute pancreatitis: A meta-analysis.

OBJECTIVE: The objective of this meta-analysis is to evaluate the diagnostic performance of acoustic radiation force impulse (ARFI) in acute pancreatitis (AP) patients. METHODS: PubMed, Web of Science, Embase, Wanfang, Chinese Biological Medicine databases, and Chinese Biomedical Literature Service System were searched for relevant studies to explore the potential diagnostic performance of ARFI in AP from inception to November 2023. STATA 14.0 was used to analyze the standardized mean difference (SMD) with 95% confidence interval (CI), pooled sensitivity, specificity, area under the curve, meta-regression analysis, sensitivity analysis, and publication bias. RESULTS: Nine studies, involving 533 AP patients and 585 healthy controls, were included. AP patients had significantly higher ARFI levels than healthy controls (SMD: 3.13, 95% CI: 1.88-4.39, P = .001). The area under the curve of ARFI for diagnosing AP was 0.99 (95% CI: 0.98-1.00), with 98% sensitivity and 94% specificity. Meta-regression identified the study region and study period as the sources of heterogeneity. Sensitivity analysis showed that the exclusion of any single study did not materially alter the overall combined effect. No evidence of publication bias was observed in the included studies. CONCLUSION: This meta-analysis demonstrated that ARFI exerted satisfactory diagnostic performance in AP.

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.

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.

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.

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.

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.

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.

Prediction of severe hypertriglyceridemia-associated acute pancreatitis using a nomogram based on CT findings and blood biomarkers.

Hypertriglyceridemia is a common cause of acute pancreatitis (AP). Fatty liver, a manifestation of metabolic syndrome, is related to the severity of AP. The present study aimed to construct an accurate predictive model for severe AP (SAP) by combining the fatty liver infiltration on a computerized tomography (CT) scan with a series of blood biomarkers in patients with hypertriglyceridemia-associated AP (HTG-AP). A total of 213 patients diagnosed with HTG-AP were included in the present retrospective study. Clinical information and imageological findings were retrospectively analyzed. The model was constructed from independent risk factors using univariate analysis, the least absolute shrinkage and selection operator method. Subsequently, the data from the training group of 111 patients with HTG-AP was analyzed using logistic regression analysis. The efficacy of the model was verified using an external validation group of 102 patients through the receiver operating characteristic curve (ROC). Independent predictors, including serum calcium, C-reactive protein, lactate dehydrogenase and liver-to-spleen CT attenuation ratio (L/S ratio), were incorporated into the nomogram model for SAP in HTG-AP. The model achieved a sensitivity of 91.3% and a specificity of 88.6% in the training group. Compared with the Ranson model, the established nomogram model exhibited a better discriminative ability in the training group [area under the curve (AUC): 0.957] and external validation group (AUC: 0.930), as well as better calibration and clinical benefits. The present study demonstrates that the constructed nomogram based on CT findings and blood biomarkers is useful for the accurate prediction of SAP in HTG-AP.

Visual Interpretation of Radiomics Features in Filtered Computed Tomography Images during the Portal Phase of Acute Pancreatitis.

BACKGROUND: Current research on radiomics for diagnosing and prognosing acute pancreatitis predominantly revolves around model development and testing. However, there is a notable absence of ongoing interpretation and analysis regarding the physical significance of these models and features. Additionally, there is a lack of extensive exploration of visual information within the images. This limitation hinders the broad applicability of radiomics findings. This study aims to address this gap by specifically analyzing filtered Computed Tomography (CT) image features of acute pancreatitis to identify meaningful visual markers in the pancreas and peripancreatic area. METHODS: Numerous filtered CT images were obtained through pyradiomics. The window width and window level were fine-tuned to emphasize the pancreas and peripancreatic regions. Subsequently, the LightGBM algorithm was employed to conduct an embedded feature screening, followed by statistical analysis to identify features with statistical significance (p-value < 0.01). Within the purview of the study, for each filtering method, features of high importance to the preceding prediction model were incorporated into the analysis. The image visual markers were then systematically sought in reverse, and their medical interpretation was undertaken to a certain extent. RESULTS: In Laplacian of Gaussian filtered images within the pancreatic region, severe acute pancreatitis (SAP) exhibited fewer small areas with repetitive greyscale patterns. Conversely, in the peripancreatic region, SAP displayed greater irregularity in both area size and the distribution of greyscale levels. In logarithmic images, SAP demonstrated reduced low greyscale connectivity in the pancreatic region, while showcasing a higher average variation in greyscale between two adjacent pixels in the peripancreatic region. Moreover, in gradient images, SAP presented with decreased repetition of two adjacent pixel greyscales within the pancreatic region, juxtaposed with an increased inhomogeneity in the size of the same greyscale region within the δ range in the peripancreatic region. CONCLUSIONS: Various filtered images convey distinct physical significance and properties. The selection of the appropriate filtered image, contingent upon the characteristics of the Region of Interest (ROI), enables a more comprehensive capture of the heterogeneity of the disease.

A Case of Doxycycline-Induced Pancreatitis.

INTRODUCTION: Acute pancreatitis is a common cause of hospitalizations in the United States, causing approximately 230 000 to 275 000 annual admissions We present the case of a patient with acute pancreatitis likely due to doxycycline. CASE PRESENTATION: A 64-year-old male was admitted after developing acute epigastric pain radiating to his back, a lipase of 6611 (units/L), and a computed tomography scan showing moderate peripancreatic inflammation. He had no recent alcohol use, his gallbladder was surgically absent, and he had no gallbladder pathology on evaluation; however, he had been started on doxycycline 10 days prior. While hospitalized, he was treated with pain medications, fluids, and antibiotics for aspiration pneumonia. His acute symptoms resolved, except for minor intermittent abdominal pain 2 months after discharge. DISCUSSION: Doxycycline-induced pancreatitis has been reported within 3 to 17 days of medication initiation. Given the temporal correlation and lack of other inciting etiologies, we determined the most likely etiology was doxycycline. CONCLUSIONS: Further study is needed to understand the pathophysiology and incidence of doxycycline-induced pancreatitis.

Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction.

OBJECTIVES: Since neither abdominal pain nor pancreatic enzyme elevation is specific for acute pancreatitis (AP), the diagnosis of AP in patients with pancreaticobiliary maljunction (PBM) may be challenging when the pancreas appears normal or nonobvious on CT. This study aimed to develop a quantitative radiomics-based nomogram of pancreatic CT for identifying AP in children with PBM who have nonobvious findings on CT. METHODS: PBM patients with a diagnosis of AP evaluated at the Children's Hospital of Soochow University from June 2015 to October 2022 were retrospectively reviewed. The radiological features and clinical factors associated with AP were evaluated. Based on the selected variables, multivariate logistic regression was used to construct clinical, radiomics, and combined models. RESULTS: Two clinical parameters and 6 radiomics characteristics were chosen based on their significant association with AP, as demonstrated in the training (area under curve [AUC]: 0.767, 0.892) and validation (AUC: 0.757, 0.836) datasets. The radiomics-clinical nomogram demonstrated superior performance in both the training (AUC, 0.938) and validation (AUC, 0.864) datasets, exhibiting satisfactory calibration (P > .05). CONCLUSIONS: Our radiomics-based nomogram is an accurate, noninvasive diagnostic technique that can identify AP in children with PBM even when CT presentation is not obvious. ADVANCES IN KNOWLEDGE: This study extracted imaging features of nonobvious pancreatitis. Then it developed and evaluated a combined model with these features.