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Pathomics Based Biomarkers, Tools, and Methods
- 1. Pathomics Based Biomarkers, Tools and Methods Joel Saltz Department of Biomedical Informatics Stony Brook University Imaging Community Call October 24, 2016
- 3. Multi-scale Integrative Analysis in Biomedical Informatics • Predict treatment outcome, select, monitor treatments • Reduce inter-observer variability in diagnosis • Computer assisted exploration of new classification schemes • Multi-scale cancer simulations
- 4. Pathomics, Radiomics Identify and segment trillions of objects – nuclei, glands, ducts, nodules, tumor niches … from Pathology, Radiology imaging datasets Extract features from objects and spatio-temporal regions Support queries against ensembles of features extracted from multiple datasets Statistical analyses and machine learning to link Radiology/Pathology features to “omics” and outcome biological phenomena Principle based analyses to bridge spatio-temporal scales – linked Pathology, Radiology studies
- 5. Radiomics Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach Hugo J. W. L. Aerts et. Al. Nature Communications 5, Article number: 4006 doi:10.1038/ncomms5006 Features Patients
- 6. Integrative Morphology/”omics” Quantitative Feature Analysis in Pathology: Emory In Silico Center for Brain Tumor Research (PI = Dan Brat, PD= Joel Saltz) NLM/NCI: Integrative Analysis/Digital Pathology R01LM011119, R01LM009239 (Dual PIs Joel Saltz, David Foran) J Am Med Inform Assoc. 2012 Integrated morphologic analysis for the identification and characterization of disease subtypes. Pathomics Lee Cooper, Jun Kong
- 7. Tools to Analyze Morphology and Spatially Mapped Molecular Data - U24 CA180924 • Specific Aim 1 Analysis pipelines for multi- scale, integrative image analysis. • Specific Aim 2: Database infrastructure to manage and query Pathomics features. • Specific Aim 3: HPC software that targets clusters, cloud computing, and leadership scale systems. • Specific Aim 4: Develop visualization middleware to relate Pathomics feature and image data and to integrate Pathomics image and “omic” data.
- 8. SEER Virtual Tissue Repository • Lynne Penberthy MD, MPH NCI SEER • Ed Helton PhD NCI CBIIT Clinical Imaging Program • Ulrike Wagner CBIIT Clinical Imaging Program • Radim Moravec NCI PhD, NCI SEER • Ashish Sharma PhD Biomedical Informatics Emory • Joel Saltz MD, PhD Biomedical Informatics Stony Brook • Tahsin Kurc PhD Biomedical Informatics Stony Brook Vision – Enable population/epidemiological cancer research that leverages rich cancer phenotype information available from Pathology tissue studies NCIP/Leidos 14X138 and HHSN261200800001E - NCI
- 9. SEER Virtual Tissue Repository • SEER registries are a potential source of information about unusual outcomes and rare cancers • Leverage Pathology labs which store FFPE tumors, slides and digital images • Link to SEER data – track long term outcomes • Accrue linked clinical data, Pathology slides from SEER sites
- 10. SEER VIRTUAL TISSUE REPOSITORY • Create linked collection of de-identified clinical data and whole slide images • Extract features from a sample set of images (pancreas and breast cancer). • Enable search, analysis, epidemiological characterization • Pilot focus on extreme outcome Breast Cancer, Pancreatic Cancer cases • Display images and analyzed features
- 11. Robust Nuclear Segmentation • Robust ensemble algorithm to segment nuclei across tissue types • Optimized algorithm tuning methods • Parameter exploration to optimize quality • Systematic Quality Control pipeline encompassing tissue image quality, human generated ground truth, convolutional neural network critique • Yi Gao, Allen Tannenbaum, Dimitris Samaras, Le Hou, Tahsin Kurc
- 14. Feature Explorer Suite • Explore Relationship Between Imaging Features, Outcome, ”omics” • Explore relationships between features and explore how features relate to images
- 15. Feature Explorer - Integrated Pathomics Features, Outcomes and “omics” – TCGA NSCLC Adeno Carcinoma Patients
- 16. Feature Explorer - Integrated Pathomics Features, Outcomes and “omics” – TCGA NSCLC Adeno Carcinoma Patients
- 17. Collaboration with MGH – Feature Explorer – Radiology Brain MR/Pathology Features
- 18. Collaboration with SBU Radiology – TCGA NSCLC Adeno Carcinoma Integrative Radiology, Pathology, “omics”, outcome Mary Saltz, Mark Schweitzer SBU Radiology
- 19. Pathomics Relationship Between Image and FeaturesLeveraging Visualization to Aid in Feature Management Step 1: Choose a case from the TCGA atlas (case #20) Step 2: Select two features of interest; X axis (area), Y axis (perimeter) Step 3: Zoom in on region of interest Step 4: Pick a specific nucleus of interest. Each dot represents a single nucleus Step 5: Evaluate the features selected in the context of the specific nucleus and where this nucleus is located within the whole slide image The tool provides visual context for feature evaluation. This technique maps both intuitive features (i.e. size, shape, color) and non-intuitive features (i.e. wavelets, texture) to the ground truth of source images through an interactive web-based user interface. Selected nucleus geolocated within whole slide image Detects elongated nucleus Going from the whole slide data set to selected features and back to the image Adding a visual perspective by using a live web-based interactive tool (http://sbu-bmi.github.io/featurescape/u24/Preview.html)
- 21. Select Feature Pair – dots correspond to nuclei
- 22. Subregion selected – form of gating analogous to flow cytometry
- 23. Sample Nuclei from Gated Region
- 24. Gated Nuclei in Context
- 25. Algorithm Comparison, Validation, Uncertainty Quantification • High quality image analysis algorithms are essential to support biomedical research and diagnosis • Validate algorithms with human annotations • Compare and consolidate different algorithm results e.g.: what are the distances and overlap ratios between markup boundaries from two algorithms? Green: algorithm 1 Red: algorithm 2 Cross matching of two spatial data sets MICCAI 2014 Brain Tumor Introduction
- 26. caMicroscope/MongoDB - Multiple Algorithm Comparison; Generate and Curate Pathomics Feature set
- 28. Heatmap – Depicts Agreement Between Algorithms
- 29. 3D Slicer Pathology – Generate High Quality Ground Truth
- 31. Adjust algorithm parameters, manual fine tuning
- 32. Auto-tuning and feature extraction • Goal – correctly segment trillions of objects (nuclei) • Adjust algorithm parameters • Autotuning – finds parameters that best match ground truth in an image patch • Region template runtime support to optimize generation and management of multi-parameter algorithm results • Eliminates redundant computation, manages locality • Active Harmony – Jeff Hollingsworth!! • Collaboration – George Teodoro, Tahsin Kurc
- 35. Performance Optimization 256 nodes of Stampede. Each node of the cluster has a dual socket Intel Xeon E5-2680 processors, an Intel Xeon Phi SE10P co-processor and 32GB RAM.The nodes are inter-connected via Mellanox FDR Infiniband switches.
- 36. Classification • Automated or semi-automated identification of tissue or cell type • Variety of machine learning and deep learning methods • Classification of Neuroblastoma • Classification of Gliomas • Quantification of lymphocyte infiltration
- 37. Classification and Characterization of Heterogeneity Gurcan, Shamada, Kong, Saltz Hiro Shimada, Metin Gurcan, Jun Kong, Lee Cooper Joel Saltz BISTI/NIBIB Center for Grid Enabled Image Analysis - P20 EB000591, PI Saltz lassification and Characterization of Heterogeneity
- 38. Neuroblastoma Classification FH: favorable histology UH: unfavorable histology CANCER 2003; 98:2274-81 <5 yr Schwannian Development ≥50% Grossly visible Nodule(s) absent present Microscopic Neuroblastic foci absent present Ganglioneuroma (Schwannian stroma-dominant) Maturing subtype Mature subtype Ganglioneuroblastoma, Intermixed (Schwannian stroma-rich) FH FH Ganglioneuroblastoma, Nodular (composite, Schwannian stroma-rich/ stroma-dominant and stroma-poor) UH/FH* Variant forms* None to <50% Neuroblastoma (Schwannian stroma-poor) Poorly differentiated subtype Undifferentiated subtype Differentiating subtype Any age UH ≥200/5,000 cells Mitotic & karyorrhectic cells 100-200/5,000 cells <100/5,000 cells Any age ≥1.5 yr <1.5 yr UH UH FH ≥200/5,000 cells 100-200/5,000 cells <100/5,000 cells Any age UH ≥1.5 yr <1.5 yr ≥5 yr UH FH UH FH
- 39. Multi-Scale Machine Learning Based Shimada Classification System • Background Identification • Image Decomposition (Multi-resolution levels) • Image Segmentation (EMLDA) • Feature Construction (2nd order statistics, Tonal Features) • Feature Extraction (LDA) + Classification (Bayesian) • Multi-resolution Layer Controller (Confidence Region) No Yes Image Tile Initialization I = L Background? Label Create Image I(L) Segmentation Feature Construction Feature Extraction Classification Segmentation Feature Construction Feature Extraction Classifier Training Down-sampling Training Tiles Within Confidence Region ? I = I -1 I > 1? Yes Yes No No TRAINING TESTING
- 41. Brain Tumor Classification – CVPR 2016
- 42. Combining Information from Patches
- 43. Brain Tumor Classification Results Le Hou, Dimitris Samaras, Tahsin Kurc, Yi Gao, Liz Vanner, James Davis, Joel Saltz
- 44. Tumor Infiltrating Lymphocyte quantification • Convolutional neural network to classify lymphocyte infiltration in tissue patches • Convolutional neural network and random forest to classify individual segmented nuclei • Extensive collection of ground truth • Joint work with Emory and TCGA PanCanAtlas Immune group Unsupervised Autoencoder – 100 feature dimensions
- 45. Lymphocyte identification Lymphocytes Infiltration No Lymphocyte Infiltration
- 46. Receiver Operating Characteristic – Area Under Curve – 95%
- 47. Lymphocyte Classification Heat Map Trained with 22.2K image patches Pathologist corrects and edits
- 48. Good Bad Test as Good 2916 33 Test as Bad 28 2094 Machine Learning and Quality Critiquing
- 49. Dissemination • Containers • Cloud • TCIA • HPC via NSF and DOE • TCGA – PanCanAtlas – Lymphocyte characterization • Integrated Features/NLP joint with TIES
- 50. ITCR Team Stony Brook University Joel Saltz Tahsin Kurc Yi Gao Allen Tannenbaum Erich Bremer Jonas Almeida Alina Jasniewski Fusheng Wang Tammy DiPrima Andrew White Le Hou Furqan Baig Mary Saltz Emory University Ashish Sharma Adam Marcus Oak Ridge National Laboratory Scott Klasky Dave Pugmire Jeremy Logan Yale University Michael Krauthammer Harvard University Rick Cummings
- 51. Funding – Thanks! • This work was supported in part by U24CA180924- 01, NCIP/Leidos 14X138 and HHSN261200800001E from the NCI; R01LM011119-01 and R01LM009239 from the NLM • This research used resources provided by the National Science Foundation XSEDE Science Gateways program under grant TG-ASC130023 and the Keeneland Computing Facility at the Georgia Institute of Technology, which is supported by the NSF under Contract OCI-0910735.
- 52. Thanks!