Program Information
Impact of Morphologic Characteristics On Radiomics Features From Contast-Enhanced CT for Primary Lung Tumors
D Fried1*, L Zhang2 , X Fave2 , G Ibbott2 , S Zhou2 , O Mawlawi2 , Z Liao2 , L Court2 , (1) University of North Carolina at Chapel Hill, Chapel Hill, NC, (2) UT MD Anderson Cancer Center, Houston, TX,
Presentations
MO-DE-207B-10 (Monday, August 1, 2016) 1:45 PM - 3:45 PM Room: 207B
Purpose: Determine the impact of morphologic characteristics (e.g. necrosis, vascular enhancement, and cavitation) on radiomic features from contrast enhanced CT (CE-CT) in primary lung tumors.
Methods: We developed an auto-segmentation algorithm to separate lung tumors on contrast-enhanced CT into cavitation (air), necrosis, tissue, and enhancing vessels using a combination of thresholding and region-growing. An auto-segmentation algorithm was also designed to identify necrosis on FDG-PET scans. Wilcoxon rank-sum tests were used to determine if significant differences existed in radiomics features (histogram-uniformity and Laplacian-of-Gaussian average) from 249 patients, found to prognostic in previous work, based on the presence/absence of morphologic features. Feature values were also compared between the original tumor contours and contours excluding a specific morphologic feature. Comparison of necrosis segmentation on CE-CT versus FDG-PET was performed in 78 patients to assess for agreement using the concordance correlation coefficient (CCC).
Results: Tumors with cavitation and enhancing vasculature had lower uniformity values (p = 0.001 and p = 0.03, respectively). Tumors with enhancing vasculature and necrosis had higher Laplacian-of-Gaussian average values (measure of “edges” within the tumor) (p < 0.001). Removing these tissue types from regions-of-interest did not drastically alter either radiomic feature value (all scenarios had R² > 0.8). This suggests there may be interactions between morphologic characteristics and the radiomic feature value of tumor tissue. Comparison of necrosis volume and percent necrosis volume of tumor were found to have CCC values of 0.85 and 0.76, respectively between CE-CT and FDG-PET segmentation methods.
Conclusions: Tumors with enhancing vasculature, necrosis, and cavitation have higher radiomic feature values that are associated with poor prognosis than tumors without these features. Removing these tissue types from quantitative assessment did not drastically impact radiomic feature values. High reproducibility of CE-CT segmented necrosis compared to FDG-PET segmented necrosis provides a reasonable validation of segmentation accuracy on CE-CT.
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