Radiomics-Assisted Presurgical Prediction for Surgical Portal Vein-Superior Mesenteric Vein Invasion in Pancreatic Ductal Adenocarcinoma

Fangming Chen; Yongping Zhou; Xiumin Qi; Rui Zhang; Xin Gao; Wei Xia; Lei Zhang

doi:10.3389/fonc.2020.523543

Radiomics-Assisted Presurgical Prediction for Surgical Portal Vein-Superior Mesenteric Vein Invasion in Pancreatic Ductal Adenocarcinoma

Front Oncol. 2020 Nov 16:10:523543. doi: 10.3389/fonc.2020.523543. eCollection 2020.

Authors

Fangming Chen¹, Yongping Zhou², Xiumin Qi³, Rui Zhang⁴, Xin Gao⁴, Wei Xia⁴, Lei Zhang¹

Affiliations

¹ Department of Radiology, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University, Wuxi, China.
² Department of Hepatobiliary Surgery, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University, Wuxi, China.
³ Department of Pathology, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University, Wuxi, China.
⁴ Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.

Abstract

Objectives: To develop a radiomics signature for predicting surgical portal vein-superior mesenteric vein (PV-SMV) in patients with pancreatic ductal adenocarcinoma (PDAC) and measure the effect of providing the predictions of radiomics signature to radiologists with different diagnostic experiences during imaging interpretation.

Methods: Between February 2008 and June 2020, 146 patients with PDAC in pancreatic head or uncinate process from two institutions were retrospectively included and randomly split into a training (n = 88) and a validation (n =58) cohort. Intraoperative vascular exploration findings were used to identify surgical PV-SMV invasion. Radiomics features were extracted from the portal venous phase CT images. Radiomics signature was built with a linear elastic-net regression model. Area under receiver operating characteristic curve (AUC) of the radiomics signature was calculated. A senior and a junior radiologist independently review CT scans and made the diagnosis for PV-SMV invasion both with and without radiomics score (Radscore) assistance. A 2-sided Pearson's chi-squared test was conducted to evaluate whether there was a difference in sensitivity, specificity, and accuracy between the radiomics signature and the unassisted radiologists. To assess the incremental value of providing Radscore predictions to the radiologists, we compared the performance between unassisted evaluation and Radscore-assisted evaluation by using the McNemar test.

Results: Numbers of patients identified as presence of surgical PV-SMV invasion were 33 (37.5%) and 19 (32.8%) in the training and validation cohort, respectively. The radiomics signature achieved an AUC of 0.848 (95% confidence interval, 0.724-0.971) in the validation cohort and had a comparable sensitivity, specificity, and accuracy as the senior radiologist in predicting PV-SMV invasion (all p-values > 0.05). Providing predictions of radiomics signature increased both radiologists' sensitivity in identifying PV-SMV invasion, while only the increase of the junior radiologist was significant (63.2 vs 89.5%, p-value = 0.025) instead of the senior radiologist (73.7 vs 89.5%, p-value = 0.08). Both radiologists' accuracy had no significant increase when provided radiomics signature assistance (both p-values > 0.05).

Conclusions: The radiomics signature can predict surgical PV-SMV invasion in patients with PDAC and may have incremental value to the diagnostic performance of radiologists during imaging interpretation.

Keywords: X-ray computed; neoplasm invasion; pancreatic ductal adenocarcinomas; presurgical evaluation; radiomics; tomography.