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; Gaoxiong Duan; Bin Lin; Qiuli Han; Shasha Li; Wei Qin; Perry J Pickhardt; Demao Deng

doi:10.3389/fonc.2022.800787

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

Front Oncol. 2022 Mar 14:12:800787. doi: 10.3389/fonc.2022.800787. eCollection 2022.

Authors

Yichen Wei¹, Jie Gong², Xin He³, Bo Liu⁴, Tiejun Liu⁵, Shuohui Yang⁶, Zhipeng Zhou⁷, Lingyan Liang¹, Songhua Zhan⁶, Ziqiang Xia⁴, Gaoxiong Duan¹, Bin Lin⁷, Qiuli Han⁵, Shasha Li³, Wei Qin², Perry J Pickhardt⁸, Demao Deng¹

Affiliations

¹ Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
² Life Science Research Center, School of Life Science and Technology, Xidian University, Xi'an, China.
³ Department of Radiology, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning, China.
⁴ Department of Radiology, Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
⁵ Department of Radiology, Affiliated Hospital, Guangxi Medicine University, Liuzhou People's Hospital, Liuzhou, China.
⁶ Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
⁷ Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, China.
⁸ School of Medicine and Public Health, University of Wisconsin, Madison, WI, United States.

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.

Keywords: cirrhosis; hepatocellular carcinoma; magnetic resonance imaging; nomogram; prediction; radiomics.