CT-based deep learning enables early postoperative recurrence prediction for intrahepatic cholangiocarcinoma.

ABSTRACT: Preoperatively accurate evaluation of risk for early postoperative recurrence contributes to maximizing the therapeutic success for intrahepatic cholangiocarcinoma (iCCA) patients. This study aimed to investigate the potential of deep learning (DL) algorithms for predicting postoperative early recurrence through the use of preoperative images. We collected the dataset, including preoperative plain computed tomography (CT) images, from 41 patients undergoing curative surgery for iCCA at multiple institutions. We built a CT patch-based predictive model using a residual convolutional neural network and used fivefold cross-validation. The prediction accuracy of the model was analyzed. We defined early recurrence as recurrence within a year after surgical resection. Of the 41 patients, early re

SUBMITTER: Wakiya T 

PROVIDER: S-EPMC9120508 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature