Observational studyRadiology. Imaging cancer2025
Improving Detection of Intrahepatic Cholangiocarcinoma with a Contrast-enhanced US-based Deep Learning Model.
Observational study in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Contrast-enhanced ultrasound for precise differentiation of hepatitis-related intrahepatic cholangiocarcinoma and hepatocellular carcinoma.Abdominal radiology (New York) · 2026Review
- [Application and prospects of artificial intelligence from tool empowerment to paradigm reconstruction for whole-course diagnosis and treatment of liver diseases by ultrasound].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Advances in In Vitro Diagnostics for Cholangiocarcinoma: From Biomarker Discovery to Artificial Intelligence.International journal of molecular sciences · 2026Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 receiver operating characteristic curve (AUC). Results A total of 1148 CEUS examinations were divided into training (
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