Biomedical Imaging GroupSTI
English only   BIG > Publications > Texture Analysis

 Home Page
 News & Events
 Tutorials and Reviews
 Download Algorithms

 All BibTeX References

Revealing Tumor Habitats from Texture Heterogeneity Analysis for Classification of Lung Cancer Malignancy and Aggressiveness

D. Cherezov, D. Goldgof, L. Hall, R. Gillies, M. Schabath, H. Müller, A. Depeursinge

Scientific Reports, vol. 9, no. 4500, pp. 1-9, March 14, 2019.

We propose an approach for characterizing structural heterogeneity of lung cancer nodules using Computed Tomography Texture Analysis (CTTA). Measures of heterogeneity were used to test the hypothesis that heterogeneity can be used as predictor of nodule malignancy and patient survival. To do this, we use the National Lung Screening Trial (NLST) dataset to determine if heterogeneity can represent differences between nodules in lung cancer and nodules in non-lung cancer patients. 253 participants are in the training set and 207 participants in the test set. To discriminate cancerous from non-cancerous nodules at the time of diagnosis, a combination of heterogeneity and radiomic features were evaluated to produce the best area under receiver operating characteristic curve (AUROC) of 0.85 and accuracy 81.64%. Second, we tested the hypothesis that heterogeneity can predict patient survival. We analyzed 40 patients diagnosed with lung adenocarcinoma (20 short-term and 20 long-term survival patients) using a leave-one-out cross validation approach for performance evaluation. A combination of heterogeneity features and radiomic features produce an AUROC of 0.9 and an accuracy of 85% to discriminate long- and short-term survivors.

AUTHOR="Cherezov, D. and Goldgof, D. and Hall, L. and Gillies, R. and
        Schabath, M. and M{\"{u}}ller, H. and Depeursinge, A.",
TITLE="Revealing Tumor Habitats from Texture Heterogeneity Analysis for
        Classification of Lung Cancer Malignancy and Aggressiveness",
JOURNAL="Scientific Reports",
month="March 14,",

© 2019 The Authors CC-BY. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from The Authors CC-BY.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.