Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis

Xi Ma,1,&ast; Yu-Rui Wang,2,&ast; Li-Yong Zhuo,1 Xiao-Ping Yin,1 Jia-Liang Ren,3 Cai-Ying Li,4 Li-Hong Xing,1 Tong-Tong Zheng4 1CT/MRI Room, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People’s Republic of China; 2Department of Computed Tomography, Tangshan Gongren Hospital, Tangshan, Hebei Province, 063000, People’s Republic of China; 3GE Healthcare[Shanghai] Co Ltd, Shanghai, 210000, People’s Republic of China; 4Department of Radiology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei Province, 050000, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiao-Ping YinCT/MRI Room, Affiliated Hospital of Hebei University, No. 212 Eastern Yuhua Road, Baoding City, Hebei Province, 071000, People’s Republic of ChinaTel +86 0312-5981699Email xpydr_sea123@163.comCai-Ying LiDepartment of Radiology, The Second Hospital of Hebei Medical University, 215 Eastern Heping Road, Shijiazhuang city, Hebei Province, 050000, People’s Republic of ChinaTel +86 0311-87046901Email lcy_dr06@126.comPurpose: To investigate the feasibility of enhanced computed tomography (CT) radiomics analysis to differentiate between pancreatic cancer (PC) and chronic pancreatitis.Methods and materials: The CT images of 151 PCs and 24 chronic pancreatitis were retrospectively analyzed in the three-dimensional regions of interest on arterial phase (AP) and venous phase (VP) and segmented by MITK software. A multivariable logistic regression model was established based on the selected radiomics features. The radiomics score was calculated, and the nomogram was established. The discrimination of each model was analyzed by the receiver operating characteristic curve (ROC). Decision curve analysis (DCA) was used to evaluate clinical utility. The precision recall curve (PRC) was used to evaluate whether the model is affected by data imbalance. The Delong test was adopted to compare the diagnostic efficiency of each model.Results: Significant differences were observed in the distribution of gender (P = 0.034), carbohydrate antigen 19-9 (P < 0.001), and carcinoembryonic antigen (P < 0.001) in patients with PC and chronic pancreatitis. The area under the ROC curve (AUC) value of AP multivariate regression model, VP multivariate regression model, AP combined with VP features model (Radiomics), clinical feature model, and radiomics combined with clinical feature model (COMB) was 0.905, 0.941, 0.941, 0.822, and 0.980, respectively. The sensitivity and specificity of the COMB model were 0.947 and 0.917, respectively. The results of DCA showed that the COMB model exhibited net clinical benefits and PRC shows that COMB model have good precision and recall (sensitivity).Conclusion: The COMB model could be a potential tool to distinguish PC from chronic pancreatitis and aid in clinical decisions.Keywords: pancreatic cancer, chronic pancreatitis, radiomics, computed tomography, differential diagnosis