Elsevier

Translational Oncology

Volume 7, Issue 1, February 2014, Pages 72-87
Translational Oncology

Reproducibility and Prognosis of Quantitative Features Extracted from CT Images1,2

https://doi.org/10.1593/tlo.13844Get rights and content
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open access

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).

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1

We acknowledge research support to the work from the following grants: National Institutes of Health (NIH) U01CA143062, Radiomics of NSCLC, Florida Biomedical Research Programs, King Team Science grant 2KT01, Radiomics of Lung Cancer Screening.

2

This article refers to supplementary materials, which are designated by Tables W1 to W7 and Figure W1 and are available online at www.transonc.com.