Rembrandt – Vasari Project research enabled by caBIG - NCIA

1. Rembrandt – Vasari Projectresearch enabled by caBIG - NCIA Eliot Siegel

2. Origins • Neuroimaging is used as a biomarker in diagnosis and therapeutic response in cerebral neoplasia clinical trials. • As yet, no consistent criteria in use (e.g. RTOG). • Response criteria (MacDonald 1987) does not fully incorporate feature-rich capabilities of MRI. • Relative absence of controlled vocabularies used in neuro oncology. • Need for a vetted subjective imaging feature set that can be used by imagers to classify primary cerebral neoplasia. • e.g.: BI-RADS for Mammography.

3. Concept • If consistent methodology can be devised for subjective classification of glioma imaging features MRI could play a more central role in: • Tumor classification & behavior. • Prognosis • Therapeutic response.

4. Plan to Create Controlled Vocabulary for Primary Brain Tumors • Needs analysis from domain experts. • Prepare a “straw-man” imaging feature set for human gliomas based on standard MR imaging. • Vet feature set with domain experts. • Validate feature set using a large collection of human gliomas • Gain endorsement from organizations which could contribute resources to the project (e.g. ASNR).

5. Initial Project Goals • By mid-2007: • Affirmation from domain experts that this project would enhance the field. • Review relevant literature in this area. • Interest by related organizations in this work. • Focus: • Identify collaborators (AEF, DR, JS) • Identify funding mechanism. • Create consortium of imaging collections using NCIA

6. Fundamental Assumptions for Cerebral Neoplasia • Biologic behavior of human gliomas can vary even within the same histologic subtype. • MR imaging features of similar histologic subtypes of human gliomas can vary substantially. • Genomics of similar histologic subtypes of gliomas are variable. • Are the imaging features a better marker for biologic behavior than histology?

7. Classify Imaging Features of Entire Tumor and Resected Specimen Record features of the entire tumor at baseline. Distinguish features that comprise tissue in resected specimen.

8. Comparative Microarray & MR Feature Analysis • MRI Featureset • Infiltration • Enhancement • Nodularity • Necrosis • Edema • nCET • Diffusion • Hemorrhage X = ?

9. Acronyms • REMBRANDT (Repository of Molecular Brain Neoplasia Data) • an NCI CCR / NABTC national initiative 2003 • key NCI CCR personnel: Howard Fine, J.C.Zenklusen • Fresh frozen surgical tissue NCI-central lab analyzed for genetics and proteomics ( > 480 cases  1000 ) • VASARI (Visually AcesSAble Rembrandt Images) • A post-facto opportunistic assembly of clinical images accompanying Rembrandt cases

10. Vasari Design • TJUH contributed > 50 tissue cases to Rembrandt for which the genetic analysis are data accessible • first target: oligodendrogliomas since survival can be prolonged and may have sub-populations • Concentrate first on visual classifiers of MR images. • Analyze strength of correlation before attempting more complex quantitative approaches • Devise unique classifier set with 30 features and pilot test • Build score sheets electronically linked to TJU PACS for expedited data management with 3 expert neuroradiologists

11. MRI Feature Development • Core subjective feature set adapted from Pope et al. AJNR 2005, modified to 30 features. • Feature set vetted by local neuroradiologists and domain experts. • Finalize data-form following test assessment by evaluators.

12. Feature Set – Controlled Vocabulary • 30 features clustered by categories. • Lesion Location • Morphology of Lesion Substance • Morphology of Lesion Margin • Alterations in Vicinity of Lesion • Extent of Resection • Goal is capture imaging features of entire tumor and imaging features of resection specimen.

13. Examples Non-standardized Features • Infiltration • Margination • Edema • Non-enhancing tumor. • Enhancement • Irregular • Nodular • Indistinct • Infiltrative • Necrosis • Physiologic • Diffusion

14. Well marginated Non-enhancing

15. Infiltrative & Necrotic Type

16. Nodular Predominantly Non-enhancing

17. Leveraged Opportunity • Both projects require: • Development of a controlled vocabulary which reliably records all aspects of imaging features. • Review of a large clinical image dataset with complete clinical, histologic, treatment and survival data for validation.

18. Research plan • Objective • brain tumor genetics/proteomics  image phenome • Process • ad hoc network-based, geo-distributed workgroup • Adam Flanders – Neuroradiologist MR expert (TJU, Philadelphia) • Daniel Rubin – Radiologist / ontologist informatician (Stanford CA) • Lori Dodd – Biostatician, imaging / genetics expertise (NCI Bethesda) • Literature search on glioma genetic expression associated with MR image uncovered a pub with 23 visual features, 3 of which correlated with survival.

19. Initial Vasari Plan • Three components: • Data collection • Imaging through NCIA • Tissue repository and analysis through Rembrandt. • Database linkage • Create mechanism to link imaging features on NCIA to genomic data on Rembrandt. • Analysis • perform comparative analysis image features and genomic expression on patient subset. • multi-variate analysis of imaging features relative to gene expression

20. Overview of Data Recording Prototype • overview of prototype data entry for independent review of MRI data using existing TJUH PACS. • custom application created using Stentor Philips API to facilitate review of MR data using PACS interface. • resides as application layer on top of clinical PACS. • Interacts with PACS database and custom research database. • application built using combination of Javascript and ASP with MS Access serving as data repository. • database contains two relational tables: • A table which holds information about the MR studies. • A table which contains interpretation data from each reader.

21. Application Schema PACS Filesystem & DBase VASARI Webserver& DBase • Software resides as an application layer over conventional PACS software. • Application communicates to the PACS client workstation and back office through the API. • Application also communicates with research database through a webserver.

22. iSite PACS

23. iSite PACS Query • User is brought to standard clinical query page. • Investigators authorized to participate in VASARI have a new worklist selection displayed in their clinical folder list. • Selecting VASARI takes user to the research worklist.

24. iSite PACS List and Load Study • User accesses custom list of TJUH study patients identified by ID number, GMDI number and dates of the two key studies: pre-operative (baseline) and first post-operative study. • User selects exam from list by clicking “Load Study” link.

25. iSite PACS review images • The two key exams (baseline & first post-operative exam) are automatically loaded into the PACS review palette for the investigator. • The two key exams are also annotated with a red “V” icon to distinguish the exams from others that appear on the timeline.

26. iSite PACS Enter MRI feature data • VASARI data entry form window automatically loads in foreground along with associated MRI studies. • data form is used by each reviewer to enter responses for the 30 MRI features. • Each feature is listed on a separate row with a brief description. • All responses are made through the use of pull-down menus.

27. Study Display with Evaluation Form • Study display with evaluation form in the foreground.

28. Database Extract • Data is stored by a unique entry ID and exam number. • Keyed by radiologist identifier and exam number. • Thirty MRI features stored f1 - f30. • Data can be exported in multiple formats and joined to demographic table to extract GMDI number etc.

29. Other Details • Application was designed to work on ubiquitous Enterprise PC systems instead of dedicated clinical PACS in order to minimize barriers to participation. • Application is hidden from other users. • Feature variables are stored as ordinal, categorical or boolean values. • Editing of data submission is not permitted. • Previously reviewed studies automatically disappear from the VASARI worklist and cannot be re-evaluated.

30. Long Term Goal VASARI • Accrue clinical MR imagesets from other Rembrandt contributors. • Perform large scale evaluation and analysis based on lessons learned in TJUH pilot. • Build cooperative library of brain tumor genomic data in Rembrandt linked to key images in NCIA subsets.

31. Summary • Value added of rich accessible data repositories like NCIA and Rembrandt. • Interdisciplinary synergy and varied research perspectives. • Model for other collaborations.