Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis

Hu, Yuntao; Liu, Nian; Tang, Lingling; Liu, Qianqian; Pan, Ke; Lei, Lixing; Huang, Xiaohua

doi:10.3389/fmed.2022.777368

Cited by 6 publications

(5 citation statements)

References 24 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chen et al (17) first showed that the radiomics model based on CECT exhibits promising value in the early prediction of RAP. In another similar study, Hu et al (18) constructed a multivariate logistic regression radiomics model, radiomics, and clinical characteristics combined model based on MRI-T2WI, and their results were consistent with those of Chen et al (17).…”

Section: Clinical Applications Predicting Recurrent Acute Pancreatitissupporting

confidence: 71%

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Yan

Gaowen²,

et al. 2022

Front. Med.

View full text Add to dashboard Cite

Radiomics involves high-throughput extraction and analysis of quantitative information from medical images. Since it was proposed in 2012, there are some publications on the application of radiomics for (1) predicting recurrent acute pancreatitis (RAP), clinical severity of acute pancreatitis (AP), and extrapancreatic necrosis in AP; (2) differentiating mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC), focal autoimmune pancreatitis (AIP) from PDAC, and functional abdominal pain (functional gastrointestinal diseases) from RAP and chronic pancreatitis (CP); and (3) identifying CP and normal pancreas, and CP risk factors and complications. In this review, we aim to systematically summarize the applications and progress of radiomics in pancreatitis and it associated situations, so as to provide reference for related research.

show abstract

Section: Clinical Applications Predicting Recurrent Acute Pancreatitissupporting

confidence: 71%

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Yan

Gaowen²,

et al. 2022

Front. Med.

View full text Add to dashboard Cite

show abstract

“…The fact that texture analysis of the pancreas is an important predictor of AP prognosis confirms its significance in the initial diagnosis of patients with CT indication. Previous studies pointed out the relevance of radiomics analyses in the prediction of AP recurrence [19,34,35]. Furthermore, the prediction of AP severity is of high clinical relevance.…”

Section: Discussionmentioning

confidence: 99%

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Bette,

Canalini,

Feitelson

et al. 2024

Diagnostics

View full text Add to dashboard Cite

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

show abstract

“…Body mass index (BMI) ≥ 25 kg/m 2 was defined as overweight, and BMI <25 kg/m 2 was defined as not overweight ( Caballero, 2019 ). Patients with a prior history of acute pancreatitis admitted to the hospital for treatment were defined as Recurrent AP ( Hu et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Analysis of risk factors for acute pancreatitis complicated with pancreatic sinistral portal hypertension and construction of predictive model

Zhao,

Mao,

Jiang

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

Objective: Pancreatic sinistral portal hypertension (PSPH) is a common complication of acute pancreatitis (AP) and can cause massive gastrointestinal bleeding, which is one of the causes of AP-related mortality. However, there is currently no predictive model for AP concurrent with PSPH. This study aimed to identify the risk factors for AP concurrent with PSPH and use these factors to build a related predictive model.Materials and methods: We collected clinical data from 282 patients with AP. 192 patients were used as a training group and 90 patients as a validation group. Univariate and multivariate analyses were used to identify independent risk factors for AP complicated with PSPH, and then a nomogram was established. The models are cross verification and Internal verification. The predictive ability and accuracy of the model were evaluated based on the working curve of the subjects and the calibration curve, respectively. The clinical value of the model was evaluated using decision curve analysis (DCA).Results: The univariate analysis revealed significant differences in the occurrence of PSPH with respect to sex, recurrent AP, history of hypertension, smoking history, patency of the splenic vein, pancreatic necrosis or pancreatic pseudocyst formation, the most significant site of pancreatic swelling, presence of a Dmure D polymer, MCTSI, and involvement of lipase and amylase. The logistic multivariate regression analysis showed that male sex, splenic-vein stenosis or occlusion and swelling were located in the body-tail, and MCTSI was an independent risk factor for PSPH. The nomogram and ROC curve were constructed. The area under the working curve of the subjects was 0.91, and the sensitivity and specificity were 82.5% and 89.1%, respectively. In the validation group, the C-index is 0.826. The nomogram was internally validated using 1,000 bootstrap samples, and the c-index was 0.898. The calibration curve demonstrated that the predicted probability was concordant with the observed probability, and the DCA confirmed that the model had robust clinical utility.Conclusion: Male sex, splenic-vein stenosis or occlusion, recurrent AP, and swelling are located in the body-tail, and MCTSI is an independent risk factor for the occurrence of PSPH. The predictive model developed for AP complicated with PSPH may serve toward developing preventive and therapeutic approaches for PSPH.

show abstract

Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis

Cited by 6 publications

References 24 publications

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Radiomics and Its Applications and Progress in Pancreatitis: A Current State of the Art Review

Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography

Analysis of risk factors for acute pancreatitis complicated with pancreatic sinistral portal hypertension and construction of predictive model

Contact Info

Product

Resources

About