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Robust Radiomics Feature Quantification using Semiautomatic Volumetric Segmentation

Institution:
1Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Department of Radiation Oncology (MAASTRO), Maastricht University, Maastricht, The Netherlands.
3Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
4Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
5Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Publisher:
Public Library of Science
Publication Date:
Jul-2014
Journal:
PLoS One
Volume Number:
9
Issue Number:
7
Pages:
e102107
Citation:
PLoS One. 2014 Jul 15;9(7):e102107.
PubMed ID:
25025374
PMCID:
PMC4098900
Appears in Collections:
NAC, NA-MIC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U01 CA143062-01/CA/NCI NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Parmar C., Rios Velazquez E., Leijenaar R., Jermoumi M., Carvalho S., Mak R.H., Mitra S., Shankar B.U., Kikinis R., Haibe-Kains B., Lambin P., Aerts H.J.W.L. Robust Radiomics Feature Quantification using Semiautomatic Volumetric Segmentation. PLoS One. 2014 Jul 15;9(7):e102107. PMID: 25025374. PMCID: PMC4098900.
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Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts.

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