Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.
We study the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and 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 three-dimensional and 110 two-dimensional) was computed, and 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 R(2) Bet ≥ 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).
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Yoganand
Y
Balagurunathan
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Yuhua
Y
Gu
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL ; Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.
Hua
H
Wang
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Virendra
V
Kumar
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Olya
O
Grove
Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
Sam
S
Hawkins
Department of Biostatistics, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Jongphil
J
Kim
Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
Dmitry B
DB
Goldgof
Department of Computer Science and Engineering, University of South Florida, Tampa, FL.
Lawrence O
LO
Hall
Department of Radiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Robert A
RA
Gatenby
Department of Cancer Imaging and Metabolism, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL ; Department of Radiology, H Lee Moffitt Cancer Center and Research Institute, Tampa, FL.
Robert J
RJ
Gillies