Korean J Radiol.  2017 Jun;18(3):498-509. 10.3348/kjr.2017.18.3.498.

Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software

• Lee M
• Woo B
• Kuo MD
• Jamshidi N
• Kim JH

• KMID: 2427305
• DOI: http://doi.org/10.3348/kjr.2017.18.3.498

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-contrast T1-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.

Keyword

Radiomics; Semi-automated segmentation; Feature quality; Glioblastoma multiforme; The Cancer Genome Atlas; The Cancer Imaging Archive

MeSH Terms

Adult
Aged
Automation
Female
Glioblastoma/*diagnosis/diagnostic imaging/pathology
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Male
Middle Aged
*Software

Figure

• 

  Fig. 1 MR images of example TCGA GBM case (TCGA-06-0213, 55-year-old female patient).Tumor segmentation was performed semi-automatically with TumorPrism3D.A. T1W post-contrast image. B. Segmented ROIs for enhancement (red) and necrosis (green) components. C. FLAIR image. D. Segmented ROI for non-enhancing T2 high signal intensity component (blue). FLAIR = fluid-attenuated inversion recovery, GBM = glioblastoma multiforme, ROI = region of interest, TCGA = The Cancer Genome Atlas, T1W = T1-weighted

• 

  Fig. 2 Overall procedure of this study.Contrast enhanced-T1W and T2 FLAIR images were registered, followed by tumor segmentation by two different raters using two different semi-automated software tools. Subsequently, total of 180 imaging features were extracted from segmented ROIs and used for evaluating feature quality. FLAIR = fluid-attenuated inversion recovery, GBM = glioblastoma multiforme, ROIs = regions of interest, TCIA = The Cancer Imaging Archive, T1W = T1-weighted

• 

  Fig. 3 Tumor segmentation procedures.A. TumorPrism3D. B. 3D Slicer. ROI = region of interest

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  Fig. 4 Example of segmentation results with two semi-automated software tools.Contrast-enhanced, necrotic, and non-enhancing T2 high signal intensity components are indicated by red, green, and blue color, respectively.A. Represents case in which similar segmentation results were produced. B. Represents case in which difference was observed in segmentation results. FLAIR = fluid-attenuated inversion recovery

• 

  Fig. 5 Waterfall diagram of normalized dynamic range for three feature groups extracted from segmented tumor volumes with TumorPrism3D.A. 1st order statistic feature. B. Morphometric feature. C. Texture features. CE = contrast-enhancing, NC = necrotic, NH = non-enhancing T2 high signal intensity

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  Fig. 6 Consensus maps of feature clusters.A. TumorPrism3D. B. 3D Slicer.

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  Fig. 7 Proportion of each feature group in 5 clusters.

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  Fig. 8 Proportion of features related to each tumor component in 5 clusters.

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