CT radiomics and clinical indicators combined model in early prediction the severity of acute pancreatitis
10.3760/cma.j.issn.1671-0282.2024.10.007
- VernacularTitle:CT影像组学-临床指标联合模型早期预测急性胰腺炎严重程度
- Author:
Dandan XU
1
;
Aoqi XIAO
;
Weisen YANG
;
Yan GU
;
Dan JIN
;
Guojian YIN
;
Hongkun YIN
;
Guohua FAN
;
Junkang SHEN
;
Liang XU
Author Information
1. 苏州大学附属第二医院放射科，苏州 215004
- Keywords:
Acute pancreatitis;
Degree of severity;
Radiomics;
Predictive model;
Nomogram
- From:
Chinese Journal of Emergency Medicine
2024;33(10):1383-1389
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the value of the Nomogram model established by CT radiomics combined with clinical indicators for prediction of the severity of early acute pancreatitis (AP).Methods:From January 2016 to March 2023, the AP patients in the Second Affiliated Hospital of Soochow University were retrospectively collected. According to the revised Atlanta classification and definition of acute pancreatitis in 2012, all patients were divided into the severe group and the non-severe group. All patients were first diagnosed, and abdominal CT plain scan and enhanced scan were completed within 1 week. Patients were randomly (random number) divided into training and validation groups at a ratio of 7:3. The pancreatic parenchyma was delineated as the region of interest on each phase CT images, and the radiomics features were extracted by python software. LASSO regression and 10-fold cross-validation were used to reduce the dimension and select the optimal features to establish the radiomics signature. Multivariate Logistic regression was used to select the independent predictors of severe acute pancreatitis (SAP), and a clinical model was established. A Nomogram model was established by combining CT radiomics signature and clinical independent predictors. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the predictive efficacy of each model.Results:Total of 205 AP patients were included (59 cases in severe group, 146 cases in non-severe group). 3, 5, 5 and 5 optimal radiomics features were selected from the plain CT scan, arterial phase, venous phase and delayed phase images of all patients, and the radiomics models were established. Among them, the arterial phase radiomics model had relatively better performance in predicting SAP, with an area under curve (AUC) of 0.937 in the training group and 0.913 in the validation group. Multivariate Logistic regression showed that C-reactive protein (CRP) and lactate dehydrogenase (LDH) were independent predictors of SAP, and they were used to establish a clinical model. The AUC in the training and validation groups were 0.879 and 0.889, respectively. The Nomogram model based on arterial phase CT radiomics signature, CRP and LDH was established, and the AUC was 0.956 and 0.947 in the training group and validation group, respectively. DCA showed that the net benefit of Nomogram model was higher than that of clinical model or radiomics model alone.Conclusions:The Nomogram model established by CT radiomics combined with clinical indicators has high application value for early prediction of the severity of AP, which is conducive to the formulation of clinical treatment plans and prognosis evaluation.
