Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features.
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.
Massachusetts General Hospital, Boston, Massachusetts.
Jayashree
J
Kalpathy-Cramer
Massachusetts General Hospital, Boston, Massachusetts.
Artem
A
Mamomov
Columbia University Medical Center, New York, New York.
Binsheng
B
Zhao
Columbia University Medical Center, New York, New York.
Lin
L
Lu
University of South Florida, Tampa, Florida.
Dmitry
D
Cherezov
Stanford University, Stanford, California.
Sandy
S
Napel
Stanford University, Stanford, California.
Sebastian
S
Echegaray
Stanford University, Stanford, California.
Daniel
D
Rubin
University of California Los Angeles, Los Angeles, California.
Michael
M
McNitt-Gray
University of California Los Angeles, Los Angeles, California.
Pechin
P
Lo
University of Iowa, Iowa City, Iowa.
Jessica C
JC
Sieren
University of Iowa, Iowa City, Iowa.
Johanna
J
Uthoff
University of Iowa, Iowa City, Iowa.
Samantha K N
SK
Dilger
Princess Margaret Cancer Center, Toronto, Ontario, Canada.
Brandan
B
Driscoll
Princess Margaret Cancer Center, Toronto, Ontario, Canada.
Ivan
I
Yeung
University of Michigan, Ann Arbor, Michigan.
Lubomir
L
Hadjiiski
University of Michigan, Ann Arbor, Michigan.
Kenny
K
Cha
Moffitt Cancer Center, Tampa, Florida.
Yoganand
Y
Balagurunathan
Moffitt Cancer Center, Tampa, Florida.
Robert
R
Gillies
University of South Florida, Tampa, Florida.
Dmitry
D
Goldgof
imaging features; lung cancer; radiomics; reproducibility