Deep learning-assisted diagnosis of liver tumors using non-contrast magnetic resonance imaging: a multicenter study.

ABSTRACT:

Objectives

Non-contrast MRI(NC-MRI) is an attractive option for liver tumors screening and follow-up. This study aims to develop and validate a deep convolutional neural network for the classification of liver lesions using non-contrast MRI.

Methods

A total of 50418 enhanced MRI images from 1959 liver tumor patients across three centers were included. Inception-ResNet V2 was used to generate four models through transfer-learning for three-way lesion classification, which processed T2-weighted, diffusion-weighted (DWI) and multiphasic T1-weighted images. The models were then validated using one independent internal and two external datasets with 5172, 2916, and 1338 images, respectively. The efficacy of non-contrast models (T2,T2+DWI) in differentiating between benign and mal

SUBMITTER: Zhen S 

PROVIDER: S-EPMC12287020 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature