Evidence map›Paper›PMID 35359425›Full record

ArticleFrontiers in oncology2022

An MRI-Based Radiomic Model for Individualized Prediction of Hepatocellular Carcinoma in Patients With Hepatitis B Virus-Related Cirrhosis.

Yichen Wei, Jie Gong, Xin He, Bo Liu, Tiejun Liu, Shuohui Yang, Zhipeng Zhou, Lingyan Liang, Songhua Zhan, Ziqiang Xia and 7 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

17 authors.

Yichen WeiDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Jie GongLife Science Research Center, School of Life Science and Technology, Xidian University, Xi'an, China.
Xin HeDepartment of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China.
Bo LiuDepartment of Radiology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Tiejun LiuDepartment of Radiology, Affiliated Hospital, Guangxi Medicine University, Liuzhou People's Hospital, Liuzhou, China.
Shuohui YangDepartment of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Zhipeng ZhouDepartment of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China.
Lingyan LiangDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Songhua ZhanDepartment of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Ziqiang XiaDepartment of Radiology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Gaoxiong DuanDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
Bin LinDepartment of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China.
Qiuli HanDepartment of Radiology, Affiliated Hospital, Guangxi Medicine University, Liuzhou People's Hospital, Liuzhou, China.
Shasha LiDepartment of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China.
Wei QinLife Science Research Center, School of Life Science and Technology, Xidian University, Xi'an, China.
Perry J PickhardtSchool of Medicine and Public Health, University of Wisconsin, Madison, WI, United States.
Demao DengDepartment of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop and validate a radiomic nomogram for individualized prediction of hepatocellular carcinoma (HCC) in HBV cirrhosis patients based on baseline magnetic resonance imaging examinations and clinical data. Methods: 364 patients with HBV cirrhosis from five hospitals were assigned to the training, internal validation, external validation-1 or external validation-2 cohort. All patients underwent baseline magnetic resonance image (MRI) scans and clinical follow-up within three-year time. Clinical risk factors and MRI-based features were extracted and analyzed. The radiomic signatures were built using the radiomics-score (Rad-score) that calculated for each patient as a linear weighted combination of selected MRI-based features. Prognostic performances of the clinical and radiomic nomograms were evaluated with Cox modeling in the training and validation cohorts. Results: Eighteen features were selected for inclusion in the Rad-score prognostic model. The radiomic signature from multi-sequence MRI yielded a concordance index (C-index) of 0.710, 0.681, 0.632 and 0.658 in the training, internal validation, external validation-1, external validation-2 cohorts, respectively. Sex and Child-Turcotte-Pugh (CTP) class were the most prognostic clinical risk factors in univariate Cox proportional hazards analyses. The radiomic combined nomogram that integrated the radiomic signature with the clinical factors yielded a C-index of 0.746, 0.710, and 0.641 in the training, internal validation, and external validation-1 cohorts, respectively, which was an improvement over either the clinical nomogram or radiomic signature alone. Conclusion: We developed an MRI-based radiomic combined nomogram with good discrimination ability for the individualized prediction of HCC in HBV cirrhosis patients within three-year time.

Indexed as

cirrhosishepatocellular carcinomamagnetic resonance imagingnomogrampredictionradiomics

Identifiers

PMID35359425
PMCPMC8964115

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