Differential diagnoses of gallbladder tumors using CT-based deep learning.

ABSTRACT:

Background

The differential diagnosis between gallbladder cancer (GBC) and xanthogranulomatous cholecystitis (XGC) remains quite challenging, and can possibly lead to improper surgery. This study aimed to distinguish between XGC and GBC by combining computed tomography (CT) images and deep learning (DL) to maximize the therapeutic success of surgery.

Methods

We collected a dataset, including preoperative CT images, from 28 cases of GBC and 21 XGC patients undergoing surgery at our facility. It was subdivided into training and validation (n = 40), and test (n = 9) datasets. We built a CT patch-based discriminating model using a residual convolutional neural network and employed 5-fold cross-validation. The discriminating performance of the model was analyzed in the test datas

SUBMITTER: Fujita H 

PROVIDER: S-EPMC9628252 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature