• Jian Zheng, Jayasree Chakraborty, William C Chapman, Scott Gerst, Mithat Gonen, Linda M Pak, William R Jarnagin, Ronald P DeMatteo, DoRichard K GRKGDepartment of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY., Amber L Simpson, Hepatopancreatobiliary Service in the Department of Surgery of the Memorial Sloan Kettering Cancer Center, and Research Staff in the Department of Surgery at Washington University School of Medicine.
• Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY.
• J. Am. Coll. Surg. 2017 Dec 1; 225 (6): 778-788.e1.

BackgroundMicrovascular invasion (MVI) is a significant risk factor for early recurrence after resection or transplantation for hepatocellular carcinoma (HCC). Knowledge of MVI status would help guide treatment recommendations, but is generally identified after operation. This study aims to predict MVI preoperatively using quantitative image analysis.Study DesignOne hundred and twenty patients from 2 institutions underwent resection of HCC from 2003 to 2015 were included. The largest tumor from preoperative CT was subjected to quantitative image analysis, which uses an automated computer algorithm to capture regional variation in CT enhancement patterns. Quantitative imaging features by automatic analysis, qualitative radiographic descriptors by 2 radiologists, and preoperative clinical variables were included in multivariate analysis to predict histologic MVI.ResultsHistologic MVI was identified in 19 (37%) patients with tumors ≤5 cm and 34 (49%) patients with tumors >5 cm. Among patients with tumors ≤5 cm, none of the clinical findings or radiographic descriptors were associated with MVI; however, quantitative features based on angle co-occurrence matrix predicted MVI with an area under curve of 0.80, positive predictive value of 63%, and negative predictive value of 85%. In patients with tumors >5 cm, higher α-fetoprotein level, larger tumor size, and viral hepatitis history were associated with MVI, and radiographic descriptors were not. However, a multivariate model combining α-fetoprotein, tumor size, hepatitis status, and quantitative feature based on local binary pattern predicted MVI with area under curve of 0.88, positive predictive value of 72%, and negative predictive value of 96%.ConclusionsThis study reveals the potential importance of quantitative image analysis as a predictor of MVI.Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

31 keywords...

hide…