Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosqua

• 作 者: Shuai REN Hui-juan TANG Rui ZHAO Shao-feng DUAN Rong CHEN Zhong-qiu WANG
• 机 构: Department of Radiology,Jiangsu Province Hospital of Chinese Medicine,Affiliated Hospital of Nanjing
• 出 处: 当代医学科学（英文）
• 发表日期: 2022年1期
• 关键词: platelet doubling computed tomography pancreatic neoplasms adenocarcinoma adenos

Objective:The objective of this study was to investigate the application of unenhanced computed tomography(CT)texture analysis in differentiating pancreatic adenosquamous carcinoma(PASC)from pancreatic ductal adenocarcinoma(PDAC).Methods:Preoperative CT images of 112 patients(31 with PASC,81 with PDAC)were retrospectively reviewed.A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis.Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test,univariate logistic regression analysis,and the minimum redundancy maximum relevance algorithm.Furthermore,receiver operating characteristic(ROC)curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest(RF)method.Finally,the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation(LGOCV)method.Results:In the present study,10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection.The predictive model had a good classification performance in differentiating PASC from PDAC,with the following characteristics:sensitivity,95.7％;specificity,92.5％;accuracy,94.3％;positive predictive value(PPV),94.3％;negative predictive value(NPV),94.3％;and area under the ROC curve(AUC),0.98.Moreover,the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm(sensitivity,90.0％;specificity,71.3％;accuracy,76.8％;PPV,59.0％;NPV,95.2％;and AUC,0.80).Conclusion:The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.