Journal
TOMOGRAPHY
Volume 2, Issue 4, Pages 430-437Publisher
GRAPHO PUBLICATIONS
DOI: 10.18383/j.tom.2016.00235
Keywords
radiomics; reproducibility; imaging features; lung cancer
Funding
- NIH/NCI [U24CA180927, U24CA180918, U01CA154601, R01 CA149490, U01 CA140207, U01 CA181156, R01 CA160251, U01 CA187947, U01CA179106, 1U01 CA 143062-01, U24180927]
- NIH/NHLBI [R01HL112986]
- NIH [R01 CA93517]
- Canadian Institute of Health Research
- State of Florida James & Esther King Biomedical Research Program [2KT01]
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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 cooccurrence 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.
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