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Proceedings Paper

The effects of variations in parameters and algorithm choices on calculated radiomics feature values: initial investigations and comparisons to feature variability across CT image acquisition conditions
Author(s): Nastaran Emaminejad; Muhammad Wahi-Anwar; John Hoffman; Grace H. Kim; Matthew S. Brown; Michael McNitt-Gray
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Paper Abstract

Translation of radiomics into clinical practice requires confidence in its interpretations. This may be obtained via understanding and overcoming the limitations in current radiomic approaches. Currently there is a lack of standardization in radiomic feature extraction. In this study we examined a few factors that are potential sources of inconsistency in characterizing lung nodules, such as 1)different choices of parameters and algorithms in feature calculation, 2)two CT image dose levels, 3)different CT reconstruction algorithms (WFBP, denoised WFBP, and Iterative). We investigated the effect of variation of these factors on entropy textural feature of lung nodules. CT images of 19 lung nodules identified from our lung cancer screening program were identified by a CAD tool and contours provided. The radiomics features were extracted by calculating 36 GLCM based and 4 histogram based entropy features in addition to 2 intensity based features. A robustness index was calculated across different image acquisition parameters to illustrate the reproducibility of features. Most GLCM based and all histogram based entropy features were robust across two CT image dose levels. Denoising of images slightly improved robustness of some entropy features at WFBP. Iterative reconstruction resulted in improvement of robustness in a fewer times and caused more variation in entropy feature values and their robustness. Within different choices of parameters and algorithms texture features showed a wide range of variation, as much as 75% for individual nodules. Results indicate the need for harmonization of feature calculations and identification of optimum parameters and algorithms in a radiomics study.

Paper Details

Date Published: 2 March 2018
PDF: 10 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753W (2 March 2018); doi: 10.1117/12.2293864
Show Author Affiliations
Nastaran Emaminejad, Univ. of California, Los Angeles (United States)
Muhammad Wahi-Anwar, Univ. of California, Los Angeles (United States)
John Hoffman, Univ. of California, Los Angeles (United States)
Grace H. Kim, Univ. of California, Los Angeles (United States)
Matthew S. Brown, Univ. of California, Los Angeles (United States)
Michael McNitt-Gray, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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