Evidence map›Paper›PMID 40337547›Full record

ArticleJHEP reports : innovation in hepatology2025

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

Yunfei Zhang, Ruofan Sheng, Xianling Qian, Heqing Wang, Fei Wu, Haoran Dai, Mingyue Song, Chun Yang, Jianjun Zhou, Weiguo Zhang and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Yunfei ZhangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Ruofan ShengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Xianling QianDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Heqing WangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Fei WuDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Haoran DaiDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Mingyue SongDepartment of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China.
Chun YangDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Jianjun ZhouDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.
Weiguo ZhangDepartment of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China.
Mengsu ZengDepartment of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & Aims: By reducing some magnetic resonance imaging (MRI) sequences, abbreviated MRI (aMRI) has shown extensive promise for detecting hepatocellular carcinoma (HCC). We aim to develop deep learning (DL)-based gadolinium-free contrast-enhanced (CE) aMRI protocols (DL-aMRI) for detecting HCC. Methods: In total, 1,769 patients (913 with HCC) were retrospectively included from three institutions for training, testing, and external validation. Stable diffusion-based DL models were trained to generate CE-MRI, including T1-weighted arterial, portal venous, transitional, and hepatobiliary phase images (AP-syn, VP-syn, TP-syn, and HBP-syn, respectively). Non-contrast-MRI (NC-MRI), including T2-weighted, diffusion-weighted, and pre-contrast T1-weighted (Pre) sequences, along with either actual or DL-synthesized CE-MRI (AP, VP, TP, and HBP or AP-syn, VP-syn, TP-syn, and HBP-syn), were used to create conventional complete MRI (cMRI) and DL-aMRI protocols. An inter-method comparison of image quality between DL-aMRI and cMRI was conducted using a non-inferiority test. The sensitivity and specificity of DL-aMRI and cMRI for detecting HCC were statistically compared using the non-inferiority test and generalized estimating equations models. Results: DL-aMRI showed a remarkable reduction in acquisition time compared with cMRI (4.1 Conclusions: The proposed DL-aMRI could facilitate precise HCC diagnosis with no need for contrast agents, a substantial reduction in acquisition time, and preservation of both NC-MRI and CE-MRI data. DL-aMRI may serve as a valuable tool for HCC diagnosing. Impact and implications: 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.

Indexed as

Abbreviated magnetic resonance imagingGenerative artificial intelligenceHepatocellular carcinoma diagnosisImage synthesisStable diffusion models

Identifiers

PMID40337547
PMCPMC12056404

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