Preoperative Recurrence Prediction in Pancreatic Ductal Adenocarcinoma after Radical Resection Using Radiomics of Diagnostic Computed Tomography

Abstract

Background: The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters. Methods: Datasets were collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk. Findings: The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.920 in the training set and 0.744 in the validation set, and 2-year recurrence with an AUROC of 0.902 in the training set and 0.780 in the validation set. Interpretation: This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies. Funding: This work was supported by the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017] (Yulian Wu), the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08] (Xiawei Li), the National Key R&D Program of China [grant number: 2018YFE0114800](Tianye Niu), the General Program of Natural Science Foundation of China [grant number: 81871351, 2018](Tianye Niu) and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJZJ- 2033] (Tianye Niu). Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: This retrospective analysis was approved by the institutional ethical review boards of three centers including the Second Affiliated Hospital (Institution I), the Forth Affiliated Hospital (Institution II), Zhejiang University school of Medicine and the Zhejiang Cancer Hospital (Institution III).