Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma

Tingfeng Xu; Liying Ren; Minjun Liao; Bigeng Zhao; Rongyu Wei; Zhipeng Zhou; Yong He; Hao Zhang; Dongbo Chen; Hongsong Chen; Weijia Liao

doi:10.2147/JHC.S356573

Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma

J Hepatocell Carcinoma. 2022 Mar 20:9:189-201. doi: 10.2147/JHC.S356573. eCollection 2022.

Authors

Tingfeng Xu^#¹, Liying Ren^#¹, Minjun Liao^#^{1

2}, Bigeng Zhao¹, Rongyu Wei¹, Zhipeng Zhou³, Yong He⁴, Hao Zhang³, Dongbo Chen⁵, Hongsong Chen⁵, Weijia Liao¹

Affiliations

¹ Laboratory of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People's Republic of China.
² Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology and Hepatology Unit, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, People's Republic of China.
³ Department of Radiology, Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People's Republic of China.
⁴ Department of Radiology, the Second Affiliated Hospital of Guilin Medical University, Guilin, 541001, Guangxi, People's Republic of China.
⁵ Peking University People's Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Disease, Beijing, 100044, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT).

Patients and methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed.

Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature.

Conclusion: The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.

Keywords: biologic correlation; contrast-enhanced CT; hepatocellular carcinoma; microvascular invasion; preoperative noninvasive diagnosis.

Grants and funding

This work was supported in part by the National Key Sci-Tech Special Project of China (No. 2018ZX10302207), the Natural Science Foundation of Guangxi (No. 2018GXNSFDA138001), the Peking University Medicine Seed Fund for Interdisciplinary Research (No. BMU2021MX007) and the Fundamental Research Funds for the Central Universities, the Peking University People’s Hospital Scientific Research Development Funds (No. RDX2020–06), the Science and Technology Planning Project of Guilin (No. 20190218-1) and the Opening Project of Key laboratory of High-Incidence-Tumor Prevention & Treatment (Guangxi Medical University), Ministry of Education (No. GKE-KF202101).