1. ASNR 55th Annual Meeting, Long Beach, April 22-27, 2017
eEdE-212
Texture Analysis: An Objective Tissue Characterization Method in Neuroradiology
MW Wagner ¹,2, AS Becker 2, A Boss 2, TA Huisman ¹, MC Wurnig 2, A Poretti ¹
¹ Section of Pediatric Neuroradiology, Division of Pediatric Radiology, Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
2 Institute of Radiology, University Hospital Zurich, Zurich, Switzerland
ASNR 55th Annual Meeting, Long Beach, April 22-27, 2017

2. Disclosure We have nothing to disclose
No relevant financial relations interfering with our presentation
No reference of any unlabeled or unapproved use of drugs
Some data discussed in this presentation have been acquired outside of the brain (so far texture analysis has been applied more in other organs like liver and kidneys)

3. Outline Challenges Solutions Future potential
Wording / Theory / Software / Pitfalls
Solutions
Robust features / Nomograms
Future potential
Impact on clinical decision making
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4. Wording
Big data
Definition: extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations
Omics data
Definition: information provided by the data in huge volumes without prior hypothesis is complementary to conventional approaches (Genomics, Proteomics, Lipidomics, Connectomics, Radiomics, …)
Hawkins et al; Nat Rev Genet Jul;11(7): Cavanillas et al; New Horizons for a Data-Driven Economy – Springer 2016.
© MWW

5. Radiomics – what does it mean?
Converts medical images (US, CT, MRI) into abstract features using data- characterization algorithms
Tools: histogram analysis / 2D+3D shape analysis / texture analysis, etc.
Texture analysis – features / parameters of images based on matrices: GLCM, GLRLM, etc.
Aerts et al; Nat Commun. 2014 Jun 3; 5: 4006.
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6. Texture analysis – overview
Matrix of neighbor pairs with same gray level in a given distance + direction
ROI covering the lesion of interest
Arbitrarily selected and arranged pixels with different gray levels numbered from 1 - 4
Frequency distribution of all gray levels within ROI (bin width ≠ 4)
Matrix of runs of same gray level with same gray level in
a given distance + direction (run lengths plotted on x-axis)
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7. Texture analysis – histogram
1st order features
Maximum observation
75th percentile
Percentiles
Median
25th percentile
Minimum observation
Skewness
Kurtosis
negative
positive
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8. Texture analysis – GLCM
GLCM, gray level co-occurrence matrix
Each ROI pixel used as a “reference” pixel once
Comparison of reference pixel and “neighbor” pixel in a given distance & direction  0/45/90/135°
GLCM increased by 1 in respective column/row with each new reference/neighbour pixel pair
Example above: two pairs of 1:2 (in a distance of 1)  2 added to the matrix accordingly
© MWW

9. Texture analysis – GLCM features
Contrast
Measures contrast or local intensity variation
Energy
Measure of uniformity of the co-occurrence matrix - higher with greater uniformity
Homogeneity
Increases with less contrast and more homogeneous images
© MWW

10. Texture analysis – GLRLM
GLRLM, gray level run-length matrix
Quantification of runs of the same gray level in given direction  0/45/90/135°
Different to GLCM: GLRLM with run lengths plotted on the x-axis
Example above: one run of three 2’s and a 1 added to the matrix, accordingly
© MWW

11. Texture analysis – GLRLM features
Short-run emphasis (SRE): Measures the distribution of short runs, ↑ for fine texture
Long-run emphasis (LRE): Measures the distribution of long runs / coarse scale texture parameter
Gray-level non-uniformity (GLN): Measures the similarity of gray level values / ↓ GLN if gray levels similar
HGRE, LGRE, SRHGE, LRHGE, etc…
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12. What software do we need?
SAME SAME -
BUT DIFFERENT ?
Correlational studies
between the different software types are largely missing!
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13. Pitfalls: Acquisition parameters
Texture features: dependent on factors unrelated to the pathology of the lesion
CT/MRI parameters (mAs, kV, FOV, field strength, matrix size, SNR, contrast phase)
Example: different contrast phase: native vs arterial vs portal venous phase
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14. Pitfalls: Object motion
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15. Pitfalls: Post-processing algorithms
Re-quantization: how many gray levels necessary in image of interest?
Do we loose crucial information by using only 6 bit images?
Or:
Do deeper images provide essential information?
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16. 2D vs 3D calculation 2D 3D Single slice with greatest lesion diameter
Less time consuming
Does not account for entire lesion of interest? 3D
3D shape analysis possible
Additional information
So far no studies with benefit for 3D
Copyright National University of Singapore
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17. Solutions Robust features: insensitive to various pitfalls
Validation, reproducibility, observer variability
Correlation matrix revealing redundancy of features
Causal connection of feature and pathology?
Statistical approach: Random forest / Decision trees
Copyright B. Bradford
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18. Solutions: Nomogram 1st step: Create ground truth 2nd step: Comparison
A nomogram to delineate boundaries of normal texture feature values for gray matter / white matter (age dependency…) / basal ganglia, etc.
2nd step: Comparison
A nomogram of clearly pathologic changes to proof feasibility
3rd step: Fun part
Detect changes not visible by eye-balling
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19. Solutions: Example Nomogram for likelihood of kidney tumors
Karlo et al; Eur J Cancer May;59:57-64.
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20. Future impact Radiomics features as biomarkers to
Differentiate benign and malignant lesions  reduce unnecessary biopsies
Predict overall survival in cancer
Predict response to treatment (RTx/CTx)
Generate alternatives or adjuncts for TNM-system
Association with underlying gene-expression pattern toward a precise individual diagnosis
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21. Take home Texture analysis
Is an objective evaluation tool in radiology
Depicts changes in tissues not visible by eye- balling
Makes use of pixel / voxel composition
Has the potential to enhance diagnostic accuracy of any abnormality depicted in studies
Contributes to a personalized medicine
Is not standardized - so far…
© MWW