Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic studyResearch in context

Development and validation of an ultrasound-based interpretable machine learning model for the classification of ≤3 cm hepatocellular carcinoma: a multicentre retrospective diagnostic studyResearch in context

Summary: Background: Our study aimed to develop a machine learning (ML) model utilizing grayscale ultrasound (US) to distinguish ≤3 cm small hepatocellular carcinoma (sHCC) from non-HCC lesions. Methods: A total of 1052 patients with 1058 liver lesions ≤3 cm from 55 hospitals were collected between...

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Bibliographic Details
Main Authors: Zhicheng Du, Fangying Fan, Jun Ma, Jing Liu, Xing Yan, Xuexue Chen, Yangfang Dong, Jiapeng Wu, Wenzhen Ding, Qinxian Zhao, Yuling Wang, Guojun Zhang, Jie Yu, Ping Liang
Format: Article
Language:English
Published: Elsevier 2025-03-01
Series:EClinicalMedicine
Subjects:
sHCC
Ultrasound
Radiomics
SHAP
Multi-center
Online Access:http://www.sciencedirect.com/science/article/pii/S2589537025000306
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