Journal article
Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features
Tomography (Ann Arbor), Vol.2(4), pp.430-437
12/2016
DOI: 10.18383/j.tom.2016.00235
PMCID: PMC5279995
PMID: 28149958
Abstract
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic “feature” sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.
Details
- Title: Subtitle
- Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features
- Creators
- Jayashree Kalpathy-Cramer - Massachusetts General Hospital, Boston, MassachusettsArtem Mamomov - Massachusetts General Hospital, Boston, MassachusettsBinsheng Zhao - Columbia University Medical Center, New York, New YorkLin Lu - Columbia University Medical Center, New York, New YorkDmitry Cherezov - University of South Florida, Tampa, FloridaSandy Napel - Stanford University, Stanford, CaliforniaSebastian Echegaray - Stanford University, Stanford, CaliforniaDaniel Rubin - Stanford University, Stanford, CaliforniaMichael McNitt-Gray - University of California Los Angeles, Los Angeles, CaliforniaPechin Lo - University of California Los Angeles, Los Angeles, CaliforniaJessica C Sieren - University of Iowa, Iowa City, IowaJohanna Uthoff - University of Iowa, Iowa City, IowaSamantha K. N Dilger - University of Iowa, Iowa City, IowaBrandan Driscoll - Princess Margaret Cancer Center, Toronto, Ontario, CanadaIvan Yeung - Princess Margaret Cancer Center, Toronto, Ontario, CanadaLubomir Hadjiiski - University of Michigan, Ann Arbor, Michigan; andKenny Cha - University of Michigan, Ann Arbor, Michigan; andYoganand Balagurunathan - Moffitt Cancer Center, Tampa, FloridaRobert Gillies - Moffitt Cancer Center, Tampa, FloridaDmitry Goldgof - University of South Florida, Tampa, Florida
- Resource Type
- Journal article
- Publication Details
- Tomography (Ann Arbor), Vol.2(4), pp.430-437
- DOI
- 10.18383/j.tom.2016.00235
- PMID
- 28149958
- PMCID
- PMC5279995
- NLM abbreviation
- Tomography
- ISSN
- 2379-1381
- eISSN
- 2379-139X
- Publisher
- Grapho Publications, LLC; Ann Abor, Michigan
- Language
- English
- Date published
- 12/2016
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Internal Medicine
- Record Identifier
- 9984051760702771
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