Value of CT imaging radiomics in predicting the clinical efficacy of extracorporeal shock wave lithotripsy for pancreatic duct stones
10.3760/cma.j.cn115667-20240117-00017
- VernacularTitle:CT影像组学特征预测胰管结石体外冲击波碎石术疗效的价值
- Author:
Chunying WU
1
;
Xiaofei JIAO
;
Chunjie WANG
;
Weigang GU
;
Zhongxiang DING
;
Xiaofeng ZHANG
Author Information
1. 西湖大学附属杭州市第一人民医院放射科，杭州 310006
- Keywords:
Pancreatic stone;
Lithotripsy;
Tomography, x-ray computed;
Imaging omics
- From:
Chinese Journal of Pancreatology
2024;24(4):287-292
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of CT imaging radiomics in predicting the therapeutic effect of extracorporeal shock wave lithotripsy (ESWL) for pancreatic duct stones.Methods:The clinical data of 167 patients with pancreatic duct stones treated with ESWL in the Department of Gastroenterology, the First People's Hospital of Hangzhou, Westlake University from July 2016 to January 2023 were retrospectively analyzed. Patients were divided into complete lithotripsy group (stone diameter ≤3 mm, n=94) and incomplete lithotripsy group (stone diameter>3 mm, n=73), according to the size of the largest residual stone after the first ESWL treatment. ITK SNAP software was used to delineate the images of pancreatic duct stones, and the artificial intelligence tool kit developed by United Shadow Company was used to extract the image radiomics characteristics. The pancreatic duct stone data set was randomly assigned into the training set ( n=118) and the test set ( n=29) in the ratio of 8∶2, and the absolute maximum normalization treatment was used, followed by peacekeeping selection through the minimum absolute contraction and selection operator (Lasso) to calculate the CT image radiomics score, and the logistic regression classifier was used to construct the ESWL treatment effect prediction model of pancreatic duct stones. Receiver operating characteristic curves (ROC) were plotted, and the area under the curve (AUC) and sensitivity, specificity, and accuracy were calculated to assess the performance of the prediction model. Decision curve analysis was used to evaluate the clinical value of CT radiomics score in the diagnosis of ESWL for pancreatic duct stones. Results:A total of 2 287 imaging radiomics characteristics were extracted, and 11 optimal imaging radiomics characteristics were finally screened by Lasso regression dimensionality reduction to establish a prediction model for ESWL treatment effect of pancreatic duct stones. The AUC values of the training set and the test set were 0.89 and 0.87, respectively, and the sensitivity, specificity, and accuracy were 82% and 79%, 82% and 82%, 82% and 80%, respectively. The AUC value in the independent validation set was 0.90, and the sensitivity, specificity, and accuracy were 78%, 90%, and 85%, respectively. The results of decision curve analysis showed that when the probability of ESWL efficacy in the diagnosis of pancreatic duct stones with CT image radiomics score was >0.05, the use of CT image radiomics score in the diagnosis of ESWL efficacy in pancreatic duct stones was more beneficial to patients in clinical practice than not.Conclusions:The treatment effect of ESWL for pancreatic duct stones can be predicted by CT imaging radiomics model.
