Improving Detection of Intrahepatic Cholangiocarcinoma with a Contrast-enhanced US-based Deep Learning Model.

ABSTRACT: Purpose To develop a deep learning (DL) model based on contrast-enhanced US (CEUS) to help radiologists diagnose intrahepatic cholangiocarcinoma (iCCA). Materials and Methods In this retrospective study (July 2017-December 2023), CEUS examinations from 49 centers were used to train and validate a DL model using four algorithms (BNInception, MobileNet-v2, ResNet-50, and VGG-19). External test set A, collected from two independent centers, was used to evaluate model performance. External test set B, collected from 51 centers, was used to compare the DL model's performance on iCCA diagnosis with that of three CEUS radiologists and one MRI radiologist and to assess the effect of DL-assisted interpretation on radiologist performance. Diagnostic performance was assessed using the area under the

SUBMITTER: Ding W 

PROVIDER: S-EPMC12670025 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature