Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma

作者：Shuai Ren, Li-Chao Qian, Yingying Cao, Marcus J. Daniels, Lina Song, Ying Tian, Zhongqiu Wang · 发表于：World Journal of Gastrointestinal Oncology · 年份：2024 · DOI：10.4251/wjgo.v16.i4.1256 · 被引用次数：62 · 研究领域：Pancreatic and Hepatic Oncology Research、Radiomics and Machine Learning in Medical Imaging、Cholangiocarcinoma and Gallbladder Cancer Studies

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. AIM To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. 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, univariate 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 selecte...