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Proceedings Paper

Change descriptors for determining nodule malignancy in national lung screening trial CT screening images
Author(s): Benjamin Geiger; Samuel Hawkins; Lawrence O. Hall; Dmitry B. Goldgof; Yoganand Balagurunathan; Robert A. Gatenby; Robert J. Gillies
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Paper Abstract

Pulmonary nodules are effectively diagnosed in CT scans, but determining their malignancy has been a challenge. The rate of change of the volume of a pulmonary nodule is known to be a prognostic factor for cancer development. In this study, we propose that other changes in imaging characteristics are similarly informative. We examined the combination of image features across multiple CT scans, taken from the National Lung Screening Trial, with individual scans of the same patient separated by approximately one year. By subtracting the values of existing features in multiple scans for the same patient, we were able to improve the ability of existing classification algorithms to determine whether a nodule will become malignant. We trained each classifier on 83 nodules determined to be malignant by biopsy and 172 nodules determined to be benign by their clinical stability through two years of no change; classifiers were tested on 77 malignant and 144 benign nodules, using a set of features that in a test-retest experiment were shown to be stable. An accuracy of 83.71% and AUC of 0.814 were achieved with the Random Forests classifier on a subset of features determined to be stable via test-retest reproducibility analysis, further reduced with the Correlation-based Feature Selection algorithm.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978535 (24 March 2016); doi: 10.1117/12.2217444
Show Author Affiliations
Benjamin Geiger, Univ. of South Florida (United States)
Samuel Hawkins, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Yoganand Balagurunathan, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert A. Gatenby, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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