Reproducibility and Prognosis of Quantitative Features Extracted from CT Images

• Published in: Translational oncology 2014-02, Vol.7 (1), p.72-87
• Main Authors: Balagurunathan, Yoganand, Gu, Yuhua, Wang, Hua, Kumar, Virendra, Grove, Olya, Hawkins, Sam, Kim, Jongphil, Goldgof, Dmitry B, Hall, Lawrence O, Gatenby, Robert A, Gillies, Robert J
• Format: Article
• Language: English
• Subjects: Oncology
• ISSN: 1936-5233
• DOI: 10.1593/tlo.13844

Abstract We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, texture from computed tomography (CT) scans of non-small cell lung cancer (NSCLC). CT images are dependent on various scanning factors. We focus on characterizing image features that are reproducible in the presence of variations due to patient factors and segmentation methods. Thirty-two NSCLC nonenhanced lung CT scans were obtained from the Reference Image Database to Evaluate Response data set. The tumors were segmented using both manual (radiologist expert) and ensemble (software-automated) methods. A set of features (219 threedimensional and 110 two-dimensional) was computed, quantitative image features were statistically filtered to identify a subset of reproducible and nonredundant features. The variability in the repeated experiment was measured by the test-retest concordance correlation coefficient (CCCTreT ). The natural range in the features, normalized to variance, was measured by the dynamic range (DR). In this study, there were 29 features across segmentation methods found with CCCTreT and DR ≥ 0.9 and R2Bet ≥ 0.95. These reproducible features were tested for predicting radiologist prognostic score; some texture features (run-length and Laws kernels) had an area under the curve of 0.9. The representative features were tested for their prognostic capabilities using an independent NSCLC data set (59 lung adenocarcinomas), where one of the texture features, run-length gray-level nonuniformity, was statistically significant in separating the samples into survival groups ( P ≤ .046).