Early prediction of acute pancreatitis severity based on changes in pancreatic and peripancreatic computed tomography radiomics nomogram

Abstract

Background: Early identification of severe acute pancreatitis (SAP) is key to reducing mortality and improving prognosis. We aimed to establish a radiomics model and nomogram for early prediction of acute pancreatitis (AP) severity based on contrast-enhanced computed tomography (CT) images. Methods: We retrospectively analyzed 215 patients with first-episode AP, including 141 in the training cohort (87 men and 54 women, mean age 51.37±16.09 years) and 74 in the test cohort (40 men and 34 women, mean age 55.49±17.83 years). Radiomics features were extracted from portal venous phase images based on pancreatic and peripancreatic regions. The light gradient boosting machine (LightGBM) algorithm was used for feature selection, a logistic regression (LR) model was established and trained by 10-fold crossvalidation, and a nomogram was established based on the best features. The model's predictive performance was evaluated according to the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy.Results: A total of 13 optimal radiomics features were selected by LightGBM for LR model building. The AUC of the radiomics (LR) model was 0.992 [95% confidence interval (CI): 0.963-0.996] in the training cohort, 0.965 (95% CI: 0.924-0.981) in the validation cohort, and 0.894 (95% CI: 0.789-0.966) in the test cohort. The sensitivity was 0.862 (95% CI: 0.674-0.954), the specificity was 0.800 (95% CI: 0.649-0.899), and the accuracy was 0.824 (95% CI: 0.720-0.919). The nomogram based on the 13 radiomics features showed that SAP would be predicted when the total score was greater than 124. Conclusions:The radiomics model based on enhanced-CT images of pancreatic and peripancreatic regions performed well in the early prediction of AP severity. The nomogram based on selected radiomics features could provide a reference for AP clinical assessment.

Predictive value of machine learning for the severity of acute pancreatitis: A systematic review and meta-analysis

“…These newly identified studies were screened based on the inclusion and exclusion criteria. Finally, a total of 33 original studies [ [14] , [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] ] were included. The literature screening process is illustrated in Fig.…”

“…In terms of statistical methods, ten studies [ 17 , 18 , 25 , 26 , [39] , [40] , [41] , 44 , 45 ] had an EPV <10 in the training set, and nine studies [ [20] , [21] , [22] , 24 , 30 , 33 , 34 , 36 , 46 ] were unable to calculate EPV. In addition, eleven studies [ 14 , 16 , 17 , 21 , 22 , 24 , 29 , 30 , 36 , 37 , 39 ] had no independent validation sets or had independent validation sets with fewer than 100 cases. Two studies [ 15 , 18 ] handled missing values in an unreasonable manner.…”

“…Five studies [ 14 , [16] , [17] , [18] , 21 , 34 ] used a univariate screening method. Eighteen studies [ 17 , 19 , 23 , 24 , [27] , [28] , [29] , [30] , [32] , [33] , [34] , [36] , [37] , [38] , [39] , [43] , [44] , [45] ] did not consider the overfitting, underfitting, and optimal fitting of the model. The final assessment result is shown in Fig.…”

Radiomic model of contrast-enhanced computed tomographyfor predicting liver injury in acute pancreatitis patients

“…As an emerging technology, radiomics is a noninvasive method for extracting quantitative features in a high-throughput manner, discovering information about the heterogeneity of lesions that cannot be observed by the naked eye, improving the diagnostic rate of diseases, and providing support for clinical decision-making (11,12).Radiomics has important applications in predicting the severity of AP, the recurrence of AP, the incidence of early extrapancreatic necrosis in AP patients and post-AP diabetes mellitus and has achieved good results (13)(14)(15). However, there are no radiomic reports on AP complicated with liver injury.…”

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

“…Acute pancreatitis (AP) is one of the most common abdominal acute conditions ( Liang et al, 2021 ; Sheng et al, 2021 ). The 2012 revised Atlanta Classification divides acute pancreatitis into three categories: mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP), and severe acute pancreatitis (SAP) ( Zhao et al, 2023 ). The outcome of acute pancreatitis is determined by the seriousness of the condition ( Lin et al, 2019 ).…”