Next Article in Journal
Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer
Previous Article in Journal
Bladder Cancer Segmentation in CT for Treatment Response Assessment: Application of Deep-Learning Convolution Neural Network—A Pilot Study
 
 
Tomography is published by MDPI from Volume 7 Issue 1 (2021). Previous articles were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence, and they are hosted by MDPI on mdpi.com as a courtesy and upon agreement with Grapho, LLC.
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features

1
Massachusetts General Hospital, 149 13th St. Charlestown, Boston, MA 02129, USA
2
Columbia University Medical Center, New York, NY, USA
3
University of South Florida, Tampa, FL, USA
4
Stanford University, Stanford, CA, USA
5
University of California Los Angeles, Los Angeles, CA, USA
6
University of Iowa, Iowa City, IA, USA
7
Princess Margaret Cancer Center, Toronto, ON, Canada
8
University of Michigan, Ann Arbor, MI, USA
9
Moffitt Cancer Center, Tampa, FL, USA
*
Author to whom correspondence should be addressed.
Tomography 2016, 2(4), 430-437; https://doi.org/10.18383/j.tom.2016.00235
Submission received: 5 September 2016 / Revised: 6 October 2016 / Accepted: 8 November 2016 / Published: 1 December 2016

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.
Keywords: radiomics; reproducibility; imaging features; lung cancer radiomics; reproducibility; imaging features; lung cancer

Share and Cite

MDPI and ACS Style

Kalpathy-Cramer, J.; Mamomov, A.; Zhao, B.; Lu, L.; Cherezov, D.; Napel, S.; Echegaray, S.; Rubin, D.; McNitt-Gray, M.; Lo, P.; et al. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography 2016, 2, 430-437. https://doi.org/10.18383/j.tom.2016.00235

AMA Style

Kalpathy-Cramer J, Mamomov A, Zhao B, Lu L, Cherezov D, Napel S, Echegaray S, Rubin D, McNitt-Gray M, Lo P, et al. Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features. Tomography. 2016; 2(4):430-437. https://doi.org/10.18383/j.tom.2016.00235

Chicago/Turabian Style

Kalpathy-Cramer, Jayashree, Artem Mamomov, Binsheng Zhao, Lin Lu, Dmitry Cherezov, Sandy Napel, Sebastian Echegaray, Daniel Rubin, Michael McNitt-Gray, Pechin Lo, and et al. 2016. "Radiomics of Lung Nodules: A Multi-Institutional Study of Robustness and Agreement of Quantitative Imaging Features" Tomography 2, no. 4: 430-437. https://doi.org/10.18383/j.tom.2016.00235

Article Metrics

Back to TopTop