摘要
BACKGROUND One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma(PDAC)is that most patients are usually diagnosed at late stages.There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages.METHODS A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography(CT)within 30 d prior to surgery were included in the study.Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system.Radiomics features were extracted from the region of interest(ROI)for each patient using Analysis Kit software.The most important and predictive radiomics features were selected using Mann-Whitney U test,univar-iate logistic regression analysis,and minimum redundancy maximum relevance(MRMR)method.Random forest(RF)method was used to construct the radiomics model,and 10-times leave group out cross-validation(LGOCV)method was used to validate the robustness and reproducibility of the model.RESULTS A total of 792 radiomics features(396 from late arterial phase and 396 from portal venous phase)were extracted from the ROI for each patient using Analysis Kit software.Nine most important and predictive features were selected using Mann-Whitney U test,univariate logistic regression analysis,and MRMR method.RF method was used to construct the radiomics model with the nine most predictive radiomics features,which showed a high discriminative ability with 97.7%accuracy,97.6%sensitivity,97.8%specificity,98.4%positive predictive value,and 96.8%negative predictive value.The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models.CONCLUSION The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.
基金
Supported by the National Natural Science foundation of China,No.82202135,82371919,82372017,and 82171925
China Postdoctoral Science Foundation,No.2023M741808
Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology,No.JSTJ-2023-WJ027
Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine,No.2023QB0112
Nanjing Postdoctoral Science Foundation,Natural Science Foundation of Nanjing University of Chinese Medicine,No.XZR2023036 and XZR2021050
Medical Imaging Artificial Intelligence Special Research Fund Project,Nanjing Medical Association Radiology Branch,Project of National Clinical Research Base of Traditional Chinese Medicine in Jiangsu Province,China,No.JD2023SZ16.
