1. Daniel Thomas Ginat, MD, MS Department of Radiology eEdE-114

2. Disclosures None

3. Introduction and Purpose Texture analysis is a form of quantitative imaging that provides insights into the nature of tumors that are not necessarily apparent upon visual inspection, but may provide added value in terms of better describing the biological attributes of tumors and impact upon patient management. The goal of this educational exhibit is to provide an overview of texture analysis potential applications related to characterization of head and neck tumors. Applications in this realm are mostly still investigational and cutting-edge.

4. Texture Analysis Definition and Role in Medical Imaging Texture analysis describes a wide range of techniques that enable quantification of the gray- level patterns, pixel interrelationships, and the spectral properties of an image. By examining the nature of gray-scale patterns in medical images, a subset of texture features, or “texture signature,” can be extracted that characterizes various lesions. Textural features may complement the macrotexture information already apparent to radiologists.

5. Pre-Processing Segmentation is performed to split the image into useful components. Regions of interest (ROIs) are selected manually or using software segmentation techniques to select the lesion or normal tissues for normalization/harmonization, if needed. Next a binary mask is produced to illustrate the region of the gray level image that will be input for subsequent texture analysis.

6. Constructing ROIs Representative CT shows a right tonsil tumor Manually constructed ROI on CT image Manually constructed binary mask

7. Main Steps in Texture Analysis Feature estimation Classification Evaluation Decision trees k-nearest neighbor Support vector machines Neural network techniques Syntactic Statistical Spectral Feature selection Manual Co-occurrence matrix Principal components analysis Step-wise discriminant Linear discriminant analysis Cross-validation K-means Hierarchical clustering

8. Texture Analysis Techniques Syntactic texture analysis: identifies fundamental or “primitive” elements of the image, which are then linked through syntax. Statistical analysis: first-order features refer to gradient parameters that characterize local gray-level differences, while second-order features are extracted from grey scale coincidence matrices, which are constructed by systematically considering the relationship between pixel pairs and tabulating the frequency of various gray level combinations within an image or within a region of interest. Spectral analysis: exploits wavelet functions to evaluate spatial frequencies at multiple scales, but is computationally intensive.

9. Histogram use in Texture Analysis The histogram of intensity levels is a concise and simple summary of the statistical information contained in the image. Calculation of the histogram involves single pixels in an image. Thus, the histogram contains the first-order statistical information about the image, but is not spatially-dependent.

10. 1D and 2D Statistical Texture Analysis Parameters (and many more!)

11. What the Equations Mean Mean: average of all numbers in a data set Variance: measures how far each number in the set is from the mean Skewness: measure of the asymmetry of the probability distribution of a real-valued random variable about its mean Energy: provides the sum of squared elements in the gray-level co- occurrence matrix. Also known as uniformity or the angular second moment Entropy: measures the disorder in a system Contrast: measures the local variations in the gray-level co-occurrence matrix Correlation: measure the strength of association between two variables Angular second moment: the square of energy Homogeneity: measures the closeness of the distribution of elements in the gray-level co-occurrence matrix to the gray-level co-occurrence matrix diagonal Kurtosis: measure of whether the data are heavy-tailed or light-tailed relative to a normal distribution

12. Spatially-Dependent Texture Features Matrices used to compute the grey-level run length features are based on the length and quantity of runs (contiguous pixels with matching intensity values). Short-run emphasis (SRE) feature indicates whether the image has a majority of short runs, while the long-run emphasis (LRE) feature indicates a majority of long runs. 110 101 111 Horizontal Runs Run Length 23 Grey Level 000 111 Example binary grey-scale image and gray-level run-length matrix features

13. Feature Selection and Extraction It is important to reduce the number of features before classification so that the statistical model will better reflect the noise or random error than the underlying data Manual selection of a subset of features based on previous work most relevant to the hypothesis being tested. Scatterplots and linear regression can be used to evaluate correlations between pairs of textural features and identify potentially redundant features To compute a characteristic of a digital image able to numerically describe its texture properties; First-order histogram based features, Co-occurrence matrix based features, Multiscale features.

14. Feature Selection Choosing most effective features for class distinction. Strongly skewed features are rejected, while strongly correlated features are rejected. The most desirable line of approach is to pay a lot of attention in choosing image features so that the classes are linearly separable. Scatterplots and linear regression can be used to evaluate correlations between pairs of textural features and, therefore, identify potentially redundant features.

15. Feature Classification Involves partitioning the feature space according to tissue class or diagnostic category. Classification is typically accomplished using a decision or discriminant function, which is used to determine what variables distinguish between two or more groups. For example, linear discriminant analysis expresses one dependent variable as a linear combination of other features or measurements and is a component of pattern recognition and machine learning.

16. Feature Evaluation The accuracy or success of our feature classification strategy is generally evaluated by cross-validation: – dividing the data into training and testing subsets – performing the classification on the training set – validating the results of the classification on the testing set – re-partition the original cases and repeat the procedure Logistic regression can also be used by assigning the most discriminating features as predictors and either tissue class or diagnosis as the outcome measure. The classification accuracy is then calculated by measuring the area under the ROC curve. K-means or hierarchical clustering are suitable when there is no prior knowledge of how the feature space is organized.

17. Potential Clinical Applications Objective strategy for lesion segmentation and characterization, particularly in cases in which the lesions are inseparable on the basis of conventional CT and MRI. Differentiating benign versus malignant tumors, types of malignant tumors, and prognosis. Texture may assist in monitoring disease progression or evaluation of emerging therapies.

18. Benign versus Malignant Tumors 2D and 3D texture-based analysis have shown promise in differentiating between benign and malignant tumors, such as cysts, glomus tumors, and schwannomas, versus lymphoma, and adenocarcinoma, but the number of correctly classified data vectors available so far is insufficient to implement this technique in the routine evaluation of MRI. Images should originate from one scanner with an identical protocol. Pleomorphic adenoma ADC map Lymphoma ADC map

19. Determining Human Papillomavirus (HPV) Status in Oropharyngeal Squamous Cell Carcinoma HPV-positive oropharyngeal squamous cell carcinomas have a different prognosis and behavior pattern, including fewer secondary malignancies compared with HPV-negative oropharyngeal squamous cell carcinomas and an overall more favorable prognosis. The histogram feature median and entropy are different among HPV- positive and HPV-negative oropharyngeal squamous cell carcinomas, reflecting differences in the internal consistency of the tumor types. HPV- HPV+

20. Predicting Treatment Response Selected texture features for response to induction chemotherapy versus no response. Initial analysis performed on oropharyngeal squamous cell carcinomas revealed that certain computer-extracted tumor texture features show trends that correlate with response to induction chemotherapy in oropharyngeal squamous cell carcinomas. No response Response

21. Selected References Tourassi GD. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology 213:317-320, 1999. Dang M, Lysack JT, Wu T, Matthews TW, Chandarana SP, Brockton NT, et al. MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma. Am J Neuroradiol. 36(1):166–70, 2015. Fujita A, Buch K, Li B, Kawashima Y, Qureshi MM, Sakai O. Difference between HPV- positive and HPV-negative non-oropharyngeal head and neck cancer: texture analysis features on CT. J Comput Assist Tomogr. 2015 Oct 13. Mattfeldt T, Fleischer F. Characterization of squamous cell carcinomas of the head and neck using methods of spatial statistics. J Microsc. 256(1):46-60, 2014. Fruehwald-Pallamar J, et al. Texture-based analysis of 100 MR examinations of head and neck tumors - Is it possible to discriminate between benign and malignant masses in a multicenter trial? Rofo. 2015 Sep 30. Hoeben BA, et al. Systematic analysis of 18F-FDG PET and metabolism, proliferation and hypoxia markers for classification of head and neck tumors. BMC Cancer. 14:130, 2014. Buch K, et al. Using Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinomas on CT. Am J Neuroradiol. 36(7):1343-1348, 2015. Lee J. Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma. AJNR Am J Neuroradiol. 2016 Jan;37(1):37-43. Kassner A, Thornhill RE. Texture analysis: a review of neurologic MR imaging applications. AJNR Am J Neuroradiol. 2010 May;31(5):809-16.

22. Acknowledgements Maryellen Giger, PhD James Melotek, MD Hui Li, PhD Kayla Mendel Tamari Miller Anup Alexander, MD