Presentation + Paper
3 March 2017 Preoperative assessment of microvascular invasion in hepatocellular carcinoma
Jayasree Chakraborty, Jian Zheng, Mithat Gönen, William R. Jarnagin, Ronald P. DeMatteo, Richard K. G. Do, Amber L. Simpson
Author Affiliations +
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.
Conference Presentation
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jayasree Chakraborty, Jian Zheng, Mithat Gönen, William R. Jarnagin, Ronald P. DeMatteo, Richard K. G. Do, and Amber L. Simpson "Preoperative assessment of microvascular invasion in hepatocellular carcinoma", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013410 (3 March 2017); https://doi.org/10.1117/12.2255622
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KEYWORDS
Tumors

Feature extraction

Liver

Computed tomography

Image segmentation

Cancer

Image classification

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