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Proceedings Paper

Preoperative assessment of microvascular invasion in hepatocellular carcinoma
Author(s): Jayasree Chakraborty; Jian Zheng; Mithat Gönen; William R. Jarnagin; Ronald P. DeMatteo; Richard K. G. Do; Amber L. Simpson
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Paper 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.

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013410 (3 March 2017); doi: 10.1117/12.2255622
Show Author Affiliations
Jayasree Chakraborty, Memorial Sloan-Kettering Cancer Ctr. (United States)
Jian Zheng, Memorial Sloan-Kettering Cancer Ctr. (United States)
Mithat Gönen, Memorial Sloan-Kettering Cancer Ctr. (United States)
William R. Jarnagin, Memorial Sloan-Kettering Cancer Ctr. (United States)
Ronald P. DeMatteo, Memorial Sloan-Kettering Cancer Ctr. (United States)
Richard K. G. Do, Memorial Sloan-Kettering Cancer Ctr. (United States)
Amber L. Simpson, Memorial Sloan-Kettering Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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