1. In Silico Brain Tumor Research Center Emory University, Atlanta, GA Classification of Brain Tumor Regions S. Cholleti, *L. Cooper, J. Kong, C. Chisolm, D. Brat, D. Gutman, T. Pan, A. Sharma, C. Moreno, T. Kurc, J. Saltz

2. In Silico Brain Tumor Research Datasets: histologyneuroimaging clincal\pathology Integrated Analysis molecular In Silico Research Centers of Excellence

3. Morphometry of the Gliomas Oligodendroglioma Astrocytoma Nuclear Morphology: Vessel Morphology: Necrosis:

4. Morphometric Analysis PAIS Database Parallel Matlab Scientific Queries ? (90+ Million Nuclei)

5. Morphological Correlates of Genomic Analysis Nuclear Characterization Region Filtering Nuclear Classification Nuclear priors Class Summary Statistics ? Proneural Neural Classical Mesenchymal (Neoplastic Oligodendroglia, Neoplastic Astrocytes, Reactive Endothelial,...)

6. Morphological Correlates of Genomic Analysis Nuclear Characterization Tissue Classification Nuclear Classification Nuclear Priors Class Summary Statistics ? Proneural Neural Classical Mesenchymal (Neoplastic Oligodendroglia, Neoplastic Astrocytes, Reactive Endothelial,...)

7. Region Classification Classify regions as normal or tumor –exclude nuclei in normal tissue regions –conditional probabilities for nuclear classification texton approach –Multiple layers of classification add robustness –Combines supervised and unsupervised classifiers References –Malik, J., Belongie, S., Shi, J., and Leung, T. 1999. Textons, contours and regions: Cue integration in image segmentation. In Proceedings IEEE 7th International Conference on Computer Vision, Corfu, Greece, pp. 918–925. –O. Tuzel, L. Yang, P. Meer, and D. J. Foran. Classification of hematologic malignancies using texton signatures. Pattern Anal. Appl., 10(4):277-290,2007. –M. Varma and A. Zisserman. Texture classification: Are filter banks necessary? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 691-698, 2003.

8. Tissue Classifier: Training Train Region Classifier SVM For each training region: Extract “Textures” Training Regions Texton Library For each class (texture classification): Region “Textures” Texton Histogram

9. Tissue Classifier: Testing Texton Library SVM Region “Textures” Texton Histogram Test Region Region Classification

10. Dataset Human Annotated regions –18 whole-slide images –Normal, GBM (IV), Astrocytoma (II & III), Oligodendroglioma (II & III), Oligoastrocytoma (II & III) Region type# Normal45 Astrocytoma20 Oligodendroglioma54 Oligoastrocytoma29 Glioblastoma18 Total166

11. Experiment and Results ExperimentClassification accuracy (%) Normal vs Tumor98 Oligodendroglioma vs Oligoastrocytoma86 Oligodendroglioma vs Astrocytoma92 Olgiodendroglioma vs Glioblastoma91 Oligoastrocytoma vs Astrocytoma80 Oligoastrocytoma vs Gligoblastoma76 Astrocytoma vs Glioblastoma70 30 x 2 cross-validation Randomly pick 50% data for training and 50% for testing. Classification accuracy: Average(correct regions / total regions)

12. Extension: Region Masking

13. Questions