MEDICINE 2020: PRECISION PRACTICE WITH BIG DATA

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1 MEDICINE 2020: PRECISION PRACTICE WITH BIG DATA Daniel L. Rubin, MD, MS Assistant Professor of Radiology and of Medicine (Biomedical Informatics) Department of Radiology Stanford University Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Precision Practice with Big Data Big Data Biological databases Medical record databases Imaging databases Knowledegbases Precision practice Learning from data (data mining, discovery) Knowledge delivery ( just in time information) Decision support (computerized reasoning) Medical practice is all about integration Clinical data History Physical exam Laboratory results Pathology Radiology Special tests Serology, ECG, EEG, special assays, molecular/genetic tests Big Data can be overwhelming; variation in practice Biomedical informatics can help! Biomedical informatics research areas Machine learning Text interpretation Knowledge engineering Model Development Biomedical Knowledge Knowledge Base Information Retrieval Knowledge Acquisition Diagnosis Biomedical Research Planning & Data Analysis Inferencing System Treatment Planning Biomedical Data Data Acquisition Data Base Human Interface Real-time acquisition Imaging Speech/language/text Specialized input devices Teaching Image Generation Credit: Edward Shortliffe Copyright 2012 Daniel L. Rubin 1

2 Course goals Show how medical practice and research are being transformed by large amounts of data (clinical, molecular, imaging) Show how computer methods can enable precision care Help physicians recognize the best therapy Get the knowledge they need when they need it Discover new knowledge and challenge established dogma Broaden clinical decision making beyond just published knowledge and physician experience Course goals Some major topics illustrated Disease sub typing/patient profiling Data mining Predicting treatment response Personalized treatment Course administration Location: LKSC, Room 101 (except OCT 3; lecture in LKSC 130) Time: Wednesdays 12:15 1:05pm, lunch will be provided and served at 12:00pm. Videos: Recordings will be posted after each lecture Course administration Units: 1 unit TA: Shanshan Tuo Requirements: Weekly attendance If miss a session, view recorded seminar and complete a short written assignment. The assignment will be posted shortly after lecture and due prior to the next scheduled talk. Submit to with BMI 205 at the beginning of the subject line. Course website Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Copyright 2012 Daniel L. Rubin 2

3 What is Big Data? Data that is too big to be handled by one computer Data coming in big: Volume: terabytes and petabytes Velocity: moving at high speeds and accruing rapidly Variety: structured, semi structured and unstructured data Big Data is big outside medicine IBM s Watson beat best Jeopardy! champions Consumed all of Wikipedia as knowledge source Big Data is big in finance Big Data and consumer profiling Take the customers web browsing & online banking activity dark red hair blond B A eyes blue brown A B Modeling Honest find the customers whose behavior indicates a sales lead and distribute the leads to bankers in the branch Peter Heffring, founder of Ceres Marketing Systems Classifier Crooked Tridas Vickie Mike Wally Waldo Barney 33 Big Data opportunities in Healthcare Evolution of medical records to electronic form creates opportunities for new applications: Learning health systems: Patient data is aggregated and analyzed to provide individual and population health care Participatory health care: Engagement of patients in their care; social media Outreach and decision support: Matching patients to treatments; personalized treatment Big Data opportunities in medicine are going viral Copyright 2012 Daniel L. Rubin 3

4 Health care is becoming a killer app for Big Data Mining genetic data for prediction Patientslikeme.com Collect medical data on many patients Show patterns to help patient decision making Big Data includes images! Volume and Velocity: Clinical practice: Thousands per imaging study in clinical medicine Research: Hundreds per experiment, and hundreds of experiments per day Variety: Modalities: Radiology, Pathology, Ophthalmology, Dermatology, microscopy, molecular imaging Image formats: DICOM, Analyze, jpg, NIFTI, Image metadata: Patient identifiers, dates, acquisition parameters measurements, quantitative and semantic image features What might we do with Big Data in Imaging? Search it for just in time knowledge Mine it for image search or to find similar images Analyze it to create decision support applications Pushing knowledge to support interpretation Pancreatic cystic lesion with small cysts, fibrous septa likely um Content based image retrieval Given an image, find images with similar lesions Look at patients diagnoses for decision support cyst cyst cyst Copyright Daniel Rubin 2011 Imaging features Disease information DDx Recommended next steps metastasis hamangioma Copyright 2012 Daniel L. Rubin 4

5 Mammography interpretation in terms of BIRADS HTTP request Bayesian Network Decision Support Structured mammography report Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Response to physician Differential diagnosis ranked by disease Decision Major obstacles to Big Data in imaging Fragmentation of data Data stored in silo systems Few standards for data exchange, interoperability Inconsistent terminology Need to integrate with non image data (clinical, molecular, etc.) Unstructured data Images and radiology text Biomedical imaging content is not explicit (making it not computer interpretable) Images contain semantic and quantitative information Image Semantics Things radiologists say about images Anatomy, image observations, regions of interest, etc. Image meaning Image Quantitation Measurable aspects of images Length, area, volume, mean/sd of pixels, etc. Image biomarkers These results conveyed as image markups and text reports, both of which are unstructured Images and image reports are unstructured IMAGES REPORTS Solutions to structuring image data Computer methods to extract the image contents (collectively called image metadata) Semantic information (type of image, clinical context, visual observations) Quantitative information (imaging parameters, measurements, image processing outputs) Store/transmit image metadata using standard terminologies in a standardized format (AIM) Software architecture to link extracted image features to the images Copyright 2012 Daniel L. Rubin 5

6 Structuring image data Structured image data = semantics + quantitation (= image phenotype ) Approach: Semantic annotation Semantic annotation of images CAVITARY MASS From controlled terminology: How to record these statements in machineaccessible format? Finding: mass Location: Lung, LUL Length: 2.3cm Width: 1.2cm Margins: spiculated Cavitary: Y Calcified: N Spatial relationships: Abuts pleural surface invades aorta Annotation and Image Markup (AIM) Markup ONTOLOGY BASED ANNOTATION ANDIMAGE MARKUP (AIM) Rubin DL, et. al: Medical Imaging on the Semantic Web: Annotation and Image Markup, AAAI Image Irregular mass in the right lobe of the liver, likely a metastasis. Annotation Markup: Graphical symbols associated with an image and optionally with one or more annotations of that same image Annotation: Explanatory or descriptive information, generated by humans or machines, directly related to the content of a referenced image AIM Information Model (v 3.0) Finding DICOM Equipment User Image Annotation Web Image References Person Annotation Of Annotation Calculation Results Annotation Role & References Inferences Text Geometric Shapes (2D and 3D) AIM semantic annotations are structured The Pixel at the tip of the arrow [coordinates (x,y)] Terminology Server There is a hypodense mass measuring 4.5 x 3.5 cm in the right lobe of the liver, likely a metastasis. in this Image [DICOM: ] represents an Hypodense Mass [RID243, RID118] [2D measurement ] 4.5 x 3.5 cm in the Right Lobe Text [SNOMED:A ] Report of the Liver [SNOMED:A ] Likely [RID:392] a Metastasis [SNOMED:A ] Semantic Annotation in AIM Copyright 2012 Daniel L. Rubin 6

7 Describing multiple lesions AIM is in XML How to create this complex structure in a user-friendly annotation tool? epad (electronic Physician Annotation Device) epad Rich Web Application Rich Web application for image viewing and semantic annotation Entire application runs in the Web browser Collects semantic and quantitative data as user views/annotates images Template (e CRF) for collecting semantic features Tools for making measurements Plug ins architecture for quantitative features Saves all image metadata in AIM Saved in XML database; Linked to DICOM images ROI Template Values Automated lesion summary by querying image annotations! epad Automated Lesion Response Graph Copyright 2012 Daniel L. Rubin 7

8 Software framework for quantitative imaging assessment of tumor burden BIMM: Searchable database of AIM and linked images Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Opportunities for Big Data in Imaging For discovery: e.g., by finding the best image biomarkers for treatment response To deliver knowledge radiologists need just in time For decision support: e.g., by finding cases of similar appearing abnormalities Discovering alternative imaging biomarkers from historical data Comparing alternative imaging biomarkers RECIST linear measurements Baseline AIM Store T1 T2 Novel Quantitative Biomarkers Prior linear measurements as seed to automatic segmentation and volume estimation Cross-sectional area shows response earlier than RECIST better quantitative imaging biomarker? Copyright 2012 Daniel L. Rubin 8

9 Mining for discovery in imaging Big Data Exploration of alternative imaging biomarkers Drug is effective in about half of patients Drug is effective in MOST patients DISEASE Exploratory data mining for discovery e.g., which image biomarker is best in cancer? WHO & Tumor PET SUV Disease RECIST Volume Mean 25-75% max NHL Panc CA Br CA GIST XX XX XX DCE-MRI Ktrans RKtrans Upstroke XX XX XX IMAGE BIOMARKER DI-WI Opportunities for Big Data in Imaging For discovery: e.g., by finding the best image biomarkers for treatment response To deliver knowledge radiologists need just in time For decision support: e.g., by finding cases of similar appearing abnormalities Push enabled indexing text via RadLex annotations Copyright Daniel Rubin RadLex indexing -Anatomy - Disease - Image finding - Modality Pushing knowledge to support interpretation Pancreatic cystic lesion with small cysts, fibrous septa likely um Decision support on best imaging tests Published guidelines on appropriateness Semantic annotation of guideline knowledge Human readable, machine processable Medical systems can access the knowledge for decision support Human-readable Machine-processible AG Authoring AG Customization Copyright Daniel Rubin 2011 Imaging features Disease information DDx AG in Published Format Human/Computer Sharing/Distribution Order Entry Decision Support Copyright 2012 Daniel L. Rubin 9

10 ACR AC in Published (Human Readable) Format Semantic annotation of knowledge Approach: Semantic Wiki Semantic Wiki = A wiki with capability for semantic tagging Wiki provides human readable, intuitive interface Semantic tags make the human readable knowledge content machine processible Implementation: Semantic MediaWiki (http://semantic mediawiki.org/) An extension to the wiki used to host Wikipedia Original (human readable) format of guideline are preserved Semantic entities and relations were identified and tagged to enable machine processing Radiology Procedures Human Readable Knowledge Capture Clinical Indication Machine Processible View of AG Clinical Indication Entity Radiology Procedure Entity Appropriateness Ratings Appropriateness Value Wiki-based automated decision support application shows the user the potential radiology exams that may be ordered in the given clinical context, with the appropriateness rating ( Rating ) and relative radiation level ( RRL ) Web Based Decision Support System Consumes Semantic Wiki content Clinical indication Radiology procedures Appropriateness ratings Input: A clinical case scenarios Output: List of radiology procedures, ordered by appropriateness rating Radiology Procedures Listing ordered by appropriateness rating Decision Support Output Clinical Indication Appropriateness Ratings Copyright 2012 Daniel L. Rubin 10

11 Opportunities for Big Data in Imaging For discovery: e.g., by finding the best image biomarkers for treatment response To deliver knowledge radiologists need just in time For decision support: e.g., by finding cases of similar appearing abnormalities Decision Support Content based image retrieval Statistical modeling (e.g., Bayesian networks) Retrieval of Similar Appearing Lesions (Content based image retrieval) Given an image, find images with similar lesions Approach: use machine- and radiologist features cyst metastasis cyst cyst hamangioma Building a CBIR resource during routine clinical work Semantic image annotation during radiology workflow Capture image features Quantitative features Semantic features Store and query image metadata for CBIRbased decision support Structured image data ( image phenotype ) represented as feature vector Example Query Quantitative Features f i f j f k Texture f q f r f s Shape f x f y f z Boundary Semantic Features Query Image (cyst) 11 Most Similar Imaging phenotype = quantitative + semantic features 12 Least Similar Imaging Phenotype f 1 f 2 f 3 f 4 f 5 f 6 f 7 f 8 f 9 f 10 f 11 f 12 f 13 Texture Shape Boundary Semantic A (B): A = rank B = computed similarity Copyright 2012 Daniel L. Rubin 11

12 Decision Support Content based image retrieval Statistical modeling (e.g., Bayesian networks) Bayesian Modeling for Mammography Diagnosis Mass Stability Mass Margins Mass Density Mass Shape Mass Size Breast Density Skin Lesion Tubular Density Architectural Distortion Mass P/A/O LN Asymmetric Density Ca ++ Lucent Centered Disease Milk of Calcium Ca ++ Dermal Benign/ Malignant/ Pre-malig. Ca ++ Round Ca ++ Dystrophic Ca ++ Popcorn Ca ++ Fine/ Linear Ca ++ Eggshell Ca ++ Pleomorphic Ca ++ Punctate Ca ++ Amorphous Ca ++ Rod-like Report Driven Decision Support Bayesian Network on server Mammography interpretation in terms of BIRADS HTTP request Ontology-supported structured radiology report Structured mammography report Outline Course introduction Background: Big Data Challenges to Big Data in imaging and solutions Opportunities for Big Data in imaging Conclusion: Thinking Big about future directions Response to physician Differential diagnosis ranked by disease Decision High Throughput Biology High Throughput Radiology? Annotate data with ontologies Link diverse data resources Quantitation & semantic annotation Link diverse imaging/ non-imaging resources Exploratory data mining Biological validation Image mining and modeling Clinical/research confirmation Copyright 2012 Daniel L. Rubin 12

13 Image phenotype molecular signatures Anatomic Anatome Imaging Functional Physiome Imaging Metabolome New frontier of Big Data in imaging: Predictive Radiomics Mineable Genomic info Molecular Imaging Proteome Transcriptome Mineable Image feature set Which Drug to use? Genome Courtesy Bob Gilles, Moffitt Hospital Courtesy Bob Gilles, Moffitt Hospital Image Workstations of Today Image Workstations of Tomorrow Find images by patient; no intelligence Image quantitation + Semantic image analysis Decision-supported PACS Disease p Ductal carcinoma.65 Fibrocystic change.12 Scar.11 Focal fibrosis.09 Fibroadenoma.05 Patient Response to treatment: Sum of Maximum Lesion Diameters (cm) /19/00 9/20/00 3/4/01 1/31/02 4/3/02 7/31/02 1/31/03 6/22/03 9/25/03 Summary Images are an important component of Big Data The key to using images in Big Data era is leveraging image metadata Semantic features Quantitative image biomarkers Semantic annotation is key to structuring image data, text, and knowledge The future is mining large collections of image metadata to improve precision of practice Discovery of new imaging biomarkers Defining disease subtypes, predicting response CBIR, decision support Our Group Students, Post docs, Residents, Staff, and Collaborators Luis de Sisternes Dan Golden Jiajing Xu Francisco Gimenez Tiffany Ting Liu Rebecca Sawyer Vanessa Sochat Selen Bozkurt Mustafa Safdari Witi Sachchamarga Hakan Bulu Lior Weizman Aaron Abajian Craig Giacomini Irene Liu Christina Hung William Du Alan Snyder Debra Willrett Jafi Lipson Hayit Greenspan Mina Ghaly Sandy Napel Chris Beaulieu Dana Yeo Funding Support Bao Do NCI QIN U01CA NCI cabig In-vivo Imaging Workspace Siemens AG Medical Solutions GE Medical Systems NIBIB-RSNA RadLex Development grant American College of Radiology Imaging Network (ACRIN) Copyright 2012 Daniel L. Rubin 13

14 Thank you. Contact info: Copyright 2012 Daniel L. Rubin 14

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