Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study

Clinical and Translational Medicine

Shijian Ruan¹  Jiayuan Shao¹  Yong Ding¹  Wuwei Tian¹  Tingbo Liang²  Xueli Bai²  Dalong Wan²  Jiacheng Huang²  Xiuming Zhang³  Hanjin Yang³  Zhao Zhang⁴  Yunjun Yang⁴  Weihai Liu⁵  Wenjie Liang⁶  Qiang Huang⁶  Wenbo Xiao⁶ 

[1] College of Information Science and Electronic Engineering Zhejiang University Hangzhou China;Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China;Department of Pathology, The First Affiliated Hospital, College of Medicine Zhejiang University Hangzhou China;Department of Radiology The First Affiliated Hospital, Wenzhou Medical University Wenzhou China;Department of Radiology The People's Hospital of Beilun District Ningbo China;Department of Radiology, The First Affiliated Hospital, College of Medicine Zhejiang University Hangzhou China;

关键词: contrast‐enhanced CT;    hepatocellular carcinoma;    microvascular invasion;    multivariable logistic regression;    radiomics;   

DOI  :  10.1002/ctm2.111

来源: DOAJ

【 摘 要 】

Abstract Background The present study constructed and validated the use of contrast‐enhanced computed tomography (CT)‐based radiomics to preoperatively predict microvascular invasion (MVI) status (positive vs negative) and risk (low vs high) in patients with hepatocellular carcinoma (HCC). Methods We enrolled 637 patients from two independent institutions. Patients from Institution I were randomly divided into a training cohort of 451 patients and a test cohort of 111 patients. Patients from Institution II served as an independent validation set. The LASSO algorithm was used for the selection of 798 radiomics features. Two classifiers for predicting MVI status and MVI risk were developed using multivariable logistic regression. We also performed a survival analysis to investigate the potentially prognostic value of the proposed MVI classifiers. Results The developed radiomics signature predicted MVI status with an area under the receiver operating characteristic curve (AUC) of .780, .776, and .743 in the training, test, and independent validation cohorts, respectively. The final MVI status classifier that integrated two clinical factors (age and α‐fetoprotein level) achieved AUC of .806, .803, and .796 in the training, test, and independent validation cohorts, respectively. For MVI risk stratification, the AUCs of the radiomics signature were .746, .664, and .700 in the training, test, and independent validation cohorts, respectively, and the AUCs of the final MVI risk classifier‐integrated clinical stage were .783, .778, and .740, respectively. Survival analysis showed that our MVI status classifier significantly stratified patients for short overall survival or early tumor recurrence. Conclusions Our CT radiomics‐based models were able to predict MVI status and MVI risk of HCC and might serve as a reliable preoperative evaluation tool.

【 授权许可】

Unknown