Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma

In this multi-center study involving 1,769 participants, we developed a generative deep learning-based abbreviated MRI (DL-aMRI) strategy that provides an efficient, contrast-agent-free alternative for detecting HCC with accuracy comparable to that of conventional complete MRI, significantly reducing acquisition time from 28.1 min to just 4.1 min. This strategy is valuable for clinicians who face significant workloads resulting from long MRI scanning times and the potential adverse effects of contrast agents, as well as for researchers focused on developing cost-effective and accessible diagnostic tools for HCC detection. The proposed DL-aMRI protocol has practical implications for clinical settings, enhancing diagnostic efficiency while maintaining high image quality, eliminating the need for contrast agents and ultimately benefiting patients and healthcare providers.

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Publication Details

Journal
JHEP Reports
Published
2025-03-12
DOI
https://doi.org/10.1016/j.jhepr.2025.101392
Citations
3
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
3.97

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article

Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma

Jianjun Zhou, Mingyue Song, Fei Wu, Mengsu Zeng et al.
3 citations
JHEP Reports
Hepatocellular Carcinoma Treatment and Prognosis
3.97
article

Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma

Jianjun Zhou, Mingyue Song, Fei Wu, Mengsu Zeng, Xianling Qian, Ruofan Sheng, Chun Yang, Heqing Wang, Weiguo Zhang, Yunfei Zhang, Haoran Dai
article en
3 citations

Abstract

In this multi-center study involving 1,769 participants, we developed a generative deep learning-based abbreviated MRI (DL-aMRI) strategy that provides an efficient, contrast-agent-free alternative for detecting HCC with accuracy comparable to that of conventional complete MRI, significantly reducing acquisition time from 28.1 min to just 4.1 min. This strategy is valuable for clinicians who face significant workloads resulting from long MRI scanning times and the potential adverse effects of contrast agents, as well as for researchers focused on developing cost-effective and accessible diagnostic tools for HCC detection. The proposed DL-aMRI protocol has practical implications for clinical settings, enhancing diagnostic efficiency while maintaining high image quality, eliminating the need for contrast agents and ultimately benefiting patients and healthcare providers.

JHEP ReportsVol. 7(5)
Sun Yat-sen University (CN), Soochow University (TW), Fudan University (CN), Soochow University (CN), Zhongshan Hospital of Xiamen University (CN), Second Affiliated Hospital of Soochow University (CN), Zhongshan Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), First Affiliated Hospital of Soochow University (CN)
Shanghai Municipal Health Commission, National Natural Science Foundation of China, Science and Technology Commission of Shanghai Municipality
Zero hunger
Openalex Percentile: Top 7%
Hepatocellular Carcinoma Treatment and Prognosis
3.97
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