Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

Xiawei Li; Yidong Wan; Jianyao Lou; Lei Xu; Aiguang Shi; Litao Yang; Yiqun Fan; Jing Yang; Junjie Huang; Yulian Wu; Tianye Niu

doi:10.1016/j.eclinm.2021.101215

Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography

EClinicalMedicine. 2021 Dec 3:43:101215. doi: 10.1016/j.eclinm.2021.101215. eCollection 2022 Jan.

Authors

Xiawei Li^{1

2

3}, Yidong Wan⁴, Jianyao Lou^{1

2

3}, Lei Xu⁴, Aiguang Shi^{1

2

3}, Litao Yang⁵, Yiqun Fan⁶, Jing Yang⁴, Junjie Huang⁷, Yulian Wu^{1

2

3}, Tianye Niu^{4

8}

Affiliations

¹ Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
² Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
³ Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China.
⁴ Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
⁵ Department of Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
⁶ Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
⁷ Department of Surgery, Changxing People's Hospital, Huzhou, Zhejiang, China.
⁸ Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.

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 retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China 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 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) 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 National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

Keywords: ITV; Nomogram; PDAC; PTV; Radical surgery; Radiomics; Recurrence.