Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma - Ren, Shuai; Zhao, Rui; Cui, Wenjing; Qiu, Wenli; Guo, Kai; Cao, Yingying; Duan, Shaofeng; Wang, Zhongqiu; Chen, Rong (Frontiers in Oncology, 2020)
Purpose: The purpose was to assess the predictive ability of computed tomography (CT) based radiomics signature in differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC). Materials and Methods: Eighty-one patients (63.6  8.8y) with PDAC and 31 patients (64.7  11.1y) with PASC who underwent preoperative CE-CT were included. A total of 792 radiomics features were extracted from the late arterial phase (n=396) and portal venous phase (n=396) for each case. Significantly differential features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance (MRMR) method. A radiomics signature was constructed using random forest (RF) method, the robustness and reliability of which was validated using 10-times leave group out cross-validation (LGOCV) method. Results: Seven radiomics features from late arterial phase images and 3 from portal venous phase images were finally selected. The radiomics signature performed well in differential diagnosis between PASC and PDAC with 94.5% accuracy, 98.3% sensitivity, 90.1% specificity, 91.9% positive predictive value (PPV), and 97.8% negative predictive value (NPV). Moreover, the radiomics signature was proved to be robust and reliable using LGOCV method with 76.4% accuracy, 91.1% sensitivity, 70.8% specificity, 56.7% PPV, and 96.2% NPV. Conclusion: CT-based Radiomics signature may serve as a promising non-invasive method in differential diagnosis between PASC and PDAC.