Computed Tomography-Based Radiomics Signature for the Preoperative Differentiation of Pancreatic Adenosquamous Carcinoma From Pancreatic Ductal Adenocarcinoma

Abstract

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 ± 8.8 years old) with PDAC and 31 patients (64.7 ± 11.1 years old) 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 different features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy and maximum relevance method. A radiomics signature was constructed using random forest method, the robustness and the 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 three 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 the 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.

Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma

“…For each patient’s CT scan, a representative axial image with the largest cross-sectional measurement of the renal tumor was selected. In order to eliminate the potential variance of CT images obtained on the three different scanners, all original CT images underwent normalization using the gray-scale discretization method before textural feature extraction, with a final 256 bins (Analysis Kit software, version V3.0.0.R, GE Healthcare) ( 29 , 30 ). Subsequently, we used the textural analysis software (MaZda Version 4.6, Institute of Electronics, Technical University of Lodz, Poland) ( 31 ) to perform the image analysis.…”

CT-Based Radiomics Analysis for Preoperative Diagnosis of Pancreatic Mucinous Cystic Neoplasm and Atypical Serous Cystadenomas

“…In order to avoid over-optimized estimation, 10-fold cross validation was applied to assess both the Rad-score and the radiological model. The 10-fold cross validation has been a commonly used method in previously reported studies to avoid confounders arisen from single data assignment (23)(24)(25). In the 10-fold cross validation, the patients were randomly allocated to training and validation sets in a 9:1 ratio for 10 times.…”

Artificial Intelligence Applied to Pancreatic Imaging: A Narrative Review

“…In pancreatic imaging, CT features both an excellent spatial and temporal resolution, allowing for a precise assessment of small structures as well as enabling the evaluation of multiple contrast phases. These characteristics have made it possible for CT to be used in a variety of oncological and non-oncological settings [ 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ].…”

“…For adenocarcinoma, radiomics and CT have been studied for differential diagnosis and survival prediction. Ren et al [ 28 ] used CT and radiomics to assess their predictive ability in the differential diagnosis between pancreatic adenosquamous carcinoma (PASC) and PDAC. For this purpose, 81 patients with PDAC and 31 patients with PASC who underwent preoperative CECT were included.…”