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Journal of Medical Imaging

Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma
Author(s): Sebastian Echegaray; Olivier Gevaert; Rajesh Shah; Aya Kamaya; John Louie; Nishita Kothary; Sandy Napel
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Paper Abstract

The purpose of this study is to investigate the utility of obtaining “core samples” of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers’ segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.

Paper Details

Date Published: 18 November 2015
PDF: 10 pages
J. Med. Img. 2(4) 041011 doi: 10.1117/1.JMI.2.4.041011
Published in: Journal of Medical Imaging Volume 2, Issue 4
Show Author Affiliations
Sebastian Echegaray, Stanford Univ. (United States)
Olivier Gevaert, Stanford Univ. (United States)
Rajesh Shah, Stanford Univ. (United States)
Aya Kamaya, Stanford Univ. (United States)
John Louie, Stanford Univ. (United States)
Nishita Kothary, Stanford Univ. (United States)
Sandy Napel, Stanford Univ. (United States)


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