Preoperative evaluating early recurrence in resectable pancreatic ductal adenocarcinoma by using CT radiomics

• Published in: Abdominal imaging 2024-02, Vol.49 (2), p.484-491
• Main Authors: Wang, Gang, Lei, Weijie, Duan, Shaofeng, Cao, Aihong, Shi, Hongyuan
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Computed tomography; Gastroenterology; Hepatology; Humans; Imaging; Medicine; Medicine & Public Health; Multivariate analysis; Pancreas; Pancreatic cancer; Pancreatic Neoplasms - diagnostic imaging; Pancreatic Neoplasms - pathology; Pancreatic Neoplasms - surgery; Patients; Radiology; Radiomics; Regression analysis; Retrospective Studies; Risk assessment; ROC Curve; Tomography, X-Ray Computed - methods; Training
• ISSN: 2366-0058; 2366-004X; 2366-0058
• DOI: 10.1007/s00261-023-04074-x

Objective To investigate the feasibility of a radiomics model based on contrast-enhanced CT for preoperatively predicting early recurrence after curative resection in patients with resectable pancreatic ductal adenocarcinoma (PDAC). Methods One hundred and eighty-six patients with resectable PDAC who underwent curative resection were included and allocated to training set (131 patients) and validation set (55 patients). Radiomics features were extracted from arterial phase and portal venous phase images. The Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature construction. The radiomics model based on radiomics signature and clinical features was developed by the multivariate logistic regression analysis. Performance of the radiomics model was investigated by the area under the receiver operating characteristic (ROC) curve. Results The radiomics signature, consisting of three arterial phase and three venous phase features, showed optimal prediction performance for early recurrence in both training (AUC = 0.73) and validation sets (AUC = 0.66). Multivariate logistic analysis identified the radiomics signature (OR, 2.58; 95% CI 2.36–3.17; p = 0.002) and clinical stage (OR, 1.60; 95% CI 1.15–2.30; p = 0.007) as independent predictors. The AUC values for risk evaluation of early recurrence using the radiomics model incorporating clinical stage were 0.80 (training set) and 0.75 (validation set). Conclusion The radiomics-based model integrating with clinical stage can predict early recurrence after upfront surgery in patients with resectable PDAC.