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; Mengsu Zeng

JHEP Reports (May 2025)

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,
Mengsu Zeng

Affiliations

Yunfei Zhang: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
Ruofan Sheng: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
Xianling Qian: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
Heqing Wang: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
Fei Wu: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
Haoran Dai: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
Mingyue Song: Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China
Chun Yang: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China
Jianjun Zhou: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
Weiguo Zhang: Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China; Corresponding authors. Addresses: Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China. Tel.: 86 512 65955267 (W. Zhang); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China (M. Zeng).
Mengsu Zeng: Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China; Corresponding authors. Addresses: Department of Radiology, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, China. Tel.: 86 512 65955267 (W. Zhang); Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China (M. Zeng).

Journal volume & issue: Vol. 7, no. 5
p. 101392

Abstract

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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 vs. 28.1 min). The image quality of DL-synthesized CE-MRI was not inferior to that of actual CE-MRI (p <0.001). There was an excellent inter-method agreement between the HCC sizes measured by the two protocols (R2 = 0.9436–0.9683). The pooled sensitivity and specificity of cMRI and DL-aMRI were 0.899 and 0.925 and 0.866 and 0.922, respectively. No significant differences were found between the sensitivity and specificity of the two protocols. 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.

Published in JHEP Reports

ISSN: 2589-5559 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Internal medicine: Specialties of internal medicine: Diseases of the digestive system. Gastroenterology
Website: https://www.journals.elsevier.com/jhep-reports

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