Contrast‐enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two‐center study. Issue 2 (21st June 2020)

• Record Type: Journal Article
• Authors: Zhang, Xiuming; Ruan, Shijian; Xiao, Wenbo; Shao, Jiayuan; Tian, Wuwei; Liu, Weihai; Zhang, Zhao; Wan, Dalong; Huang, Jiacheng; Huang, Qiang; Yang, Yunjun; Yang, Hanjin; Ding, Yong; Liang, Wenjie; Bai, Xueli; Liang, Tingbo
• Is Part Of: Clinical and translational medicine. Volume 10:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-21
• Subjects: contrast‐enhanced CT -- hepatocellular carcinoma -- microvascular invasion -- multivariable logistic regression -- radiomics; Clinical medicine -- Periodicals; Medicine, Experimental -- Periodicals; Medical innovations -- Periodicals; Molecular biology -- Periodicals; Pathology, Molecular -- Periodicals; 616.027
• DOI: 10.1002/ctm2.111
• Languages: English
• ISSNs: 2001-1326

Abstract

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. Abstract : (1) Preoperative computed tomography images of hepatocellular carcinoma (HCC) were collected from two institutions for training and independent validation. (2) The least absolute shrinkage and selection operator regression algorithm was used to construct radiomics signatures. (3) Radiomics‐based prediction models predicted the microvascular invasion status (positive vs. negative) and risk (low vs. high) of HCC.