Title: | Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software |
Authors: | Lee, Myungeun Woo, Boyeong Kuo, Michael D. Jamshidi, Neema Kim, Jong Hyo 電機工程學系 Department of Electrical and Computer Engineering |
Keywords: | Radiomics;Semi-automated segmentation;Feature quality;Glioblastoma multiforme;The Cancer Genome Atlas;The Cancer Imaging Archive |
Issue Date: | 1-May-2017 |
Abstract: | Objective: The purpose of this study was to evaluate the reliability and quality of radiomic features in glioblastoma multiforme (GBM) derived from tumor volumes obtained with semi-automated tumor segmentation software. Materials and Methods: MR images of 45 GBM patients (29 males, 16 females) were downloaded from The Cancer Imaging Archive, in which post-contrastT1-weighted imaging and fluid-attenuated inversion recovery MR sequences were used. Two raters independently segmented the tumors using two semi-automated segmentation tools (TumorPrism3D and 3D Slicer). Regions of interest corresponding to contrast-enhancing Lesion, necrotic portions, and non-enhancing T2 high signal intensity component were segmented for each tumor. A total of 180 imaging features were extracted, and their quality was evaluated in terms of stability, normalized dynamic range (NDR), and redundancy, using intra-class correlation coefficients, cluster consensus, and Rand Statistic. Results: Our study results showed that most of the radiomic features in GBM were highly stable. Over 90% of 180 features showed good stability (intra-class correlation coefficient [ICC] >= 0.8), whereas only 7 features were of poor stability (ICC < 0.5). Most first order statistics and morphometric features showed moderate-to-high NDR (4 > NDR >= 1.), while above 35% of the texture features showed poor NDR (< 1). Features were shown to cluster into only 5 groups, indicating that they were highly redundant. Conclusion: The use of semi-automated software tools provided sufficiently reliable tumor segmentation and feature stability; thus helping to overcome the inherent inter-rater and intra-rater variability of user intervention. However, certain aspects of feature quality, including NDR and redundancy, need to be assessed for determination of representative signature features before further development of radiomics. |
URI: | http://dx.doi.org/10.3348/kjr.2017.18.3.498 http://hdl.handle.net/11536/145524 |
ISSN: | 1229-6929 |
DOI: | 10.3348/kjr.2017.18.3.498 |
Journal: | KOREAN JOURNAL OF RADIOLOGY |
Volume: | 18 |
Begin Page: | 498 |
End Page: | 509 |
Appears in Collections: | Articles |