Preoperative assessment of microvascular invasion in hepatocellular carcinoma Conference Paper


Authors: Chakraborty, J.; Zheng, J.; Gönen, M.; Jarnagin, W. R.; Dematteo, R. P.; Do, R. K. G.; Simpson, A. L.
Editors: Armato, S. G. 3rd; Petrick, N. A.
Title: Preoperative assessment of microvascular invasion in hepatocellular carcinoma
Conference Title: Medical Imaging 2017: Computer-Aided Diagnosis
Abstract: Hepatocellular carcinoma (HCC) is the most common liver cancer and the third leading cause of cancer-related death worldwide.1 Resection or liver transplantation may be curative in patients with early-stage HCC but early recurrence is common.2, 3 Microvascular invasion (MVI) is one of the most important predictors of early recurrence.3 The identification of MVI prior to surgery would optimally select patients for potentially curative resection or liver transplant. However, MVI can only be diagnosed by microscopic assessment of the resected tumor. The aim of the present study is to apply CT-based texture analysis to identify pre-operative imaging predictors of MVI in patients with HCC. Texture features are derived from CT and analyzed individually as well as in combination, to evaluate their ability to predict MVI. A two-stage classification is employed: HCC tumors are automatically categorized into uniform or heterogenous groups followed by classification into the presence or absence of MVI. We achieve an area under the receiver operating characteristic curve (AUC) of 0.76 and accuracy of 76.7% for uniform lesions and AUC of 0.79 and accuracy of 74.06% for heterogeneous tumors. These results suggest that MVI can be accurately and objectively predicted from preoperative CT scans. © 2017 SPIE.
Keywords: hepatocellular carcinoma; texture analysis; microvascular invasion; naive bayes classifier
Journal Title Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume: 10134
Conference Dates: 2017 Feb 13-16
Conference Location: Orlando, FL
ISBN: 1605-7422
Publisher: SPIE  
Date Published: 2017-03-03
Start Page: 1013410
Language: English
DOI: 10.1117/12.2255622
PROVIDER: scopus
DOI/URL:
Notes: Conference Paper -- Export Date: 3 July 2017 -- Source: Scopus
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MSK Authors
  1. Ronald P DeMatteo
    637 DeMatteo
  2. Mithat Gonen
    1028 Gonen
  3. William R Jarnagin
    903 Jarnagin
  4. Kinh Gian Do
    256 Do
  5. Amber L Simpson
    64 Simpson
  6. Jian Ying Zheng
    17 Zheng