Synapse - Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis

Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis Journal Article

Authors:	Zheng, J.; Chakraborty, J.; Chapman, W. C.; Gerst, S.; Gonen, M.; Pak, L. M.; Jarnagin, W. R.; DeMatteo, R. P.; Do, R. K. G.; Simpson, A. L.; Hepatopancreatobiliary Service in the Department of Surgery of the Memorial Sloan Kettering Cancer Center; Research Staff in the Department of Surgery at Washington University School of Medicine
Contributors:	Allen, P. J.; Balachandran, V. P.; D'Angelica, M. I.; Kingham, T. P.
Article Title:	Preoperative prediction of microvascular invasion in hepatocellular carcinoma using quantitative image analysis
Abstract:	Background Microvascular 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 Design One 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. Results Histologic 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%. Conclusions This study reveals the potential importance of quantitative image analysis as a predictor of MVI. © 2017 American College of Surgeons
Journal Title:	Journal of the American College of Surgeons
Volume:	225
Issue:	6
ISSN:	1072-7515
Publisher:	Elsevier Science, Inc.
Date Published:	2017-12-01
Start Page:	778
End Page:	788.e1
Language:	English
DOI:	10.1016/j.jamcollsurg.2017.09.003
PROVIDER:	scopus
PMCID:	PMC5705269
PUBMED:	28941728
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031325330&doi=10.1016%2fj.jamcollsurg.2017.09.003&partnerID=40&md5=f60caf43f113ce61d1bcb1b123df98ba
Notes:	Article -- Export Date: 4 December 2017 -- Source: Scopus