Standardize and Optimize. Trials and Drug Development
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1 Informatics Infrastructure to Standardize and Optimize Quantitative Imaging in Clinical Trials and Drug Development Daniel L. Rubin, MD, MS Assistant Professor of Radiology Member, Stanford Cancer Center Chair, ACRIN Informatics Committee Department of Radiology Stanford University
2 Acknowledgements Collaborators and Funding Mia Levy Edward Graves Cesar Rodriguez George Fisher Sandy Napel Andrew Evens Annotation and Image Markup David Channin, Pattanasak Mongkolwat Funding Support NCI cabig In-vivo Imaging Workspace NCI QIN U01CA GE Medical Systems
3 Outline 1. Challenges in clinical cancer research 2. Informatics opportunities and approach 3. Planned deliverables and future work
4 Outline 1. Challenges in clinical cancer research 2. Informatics opportunities and approach 3. Planned deliverables and future work
5 Clinical cancer research goals Evaluate cancer response to new treatments with great sensitivity so benefits of advances are not overlooked Leverage new technologies Molecular medicine is producing new treatments Can exploit quantitative image information ( biomarkers ) about tumor burden Can determine better secondary endpoints based on quantitative imaging biomarkers Can develop/validate better, more sensitive criteria for individual & cohort response
6 Challenges Poor reproducibility of measurements on images Lack of coordination and effective communication between oncologists and radiologists and local vs. central sites in making quantitative imaging assessments Little integration of multiple quantitative measures of tumor burden that, taken together, are more informative than individual indicators Lack of tools for recording quantitative image metadata to enable data sharing and data mining
7 Oncologist Response Assessment & 1.0 cm 1.2 cm Oncologist reviews radiology report & images Defines certain lesions as measurable disease for tracking Applies criteria to assess treatment response Manual, labor-intensive, error-prone
8 RECIST Flowsheet Lesion ID Location/Description Baseline Follow-up 1 Right upper lung nodule 2.5 cm 1.2 cm 2 Liver nodule - segment cm 1.4 cm 3 Liver nodule - segment cm 1.0 cm Sum Longest Diameters 6.5 cm 3.6 cm Response Rate -44% Response Category Partial Response
9 Information Reported by Radiologist Markup Regions of Interest (ROI) Text Report Lesion Location (anatomic region; image number) Lesion Dimension(s) Impression of disease status (not machine-accessible) Usually unaware of lesion being tracked and measurement criteria
10 Challenges in recording, coordinating, and communicating quantitative imaging information in cancer research
11 Need standardization in imaging for clinical trials To control variability and inconsistency in Methods of acquisition Analysis of images Interpretation of images To improve data quality To streamline conduct and reduce cost of trials To identify earlier whether drugs are effective in individual patients and cohort studies
12 Outline 1. Challenges in clinical cancer research 2. Informatics opportunities and approach 3. Planned deliverables and future work
13 Our goals Informatics platform to streamline and improve quality of data collection/analysis from imaging in clinical research Reproducible measurement of tumor burden and cancer treatment response Coordination and effective communication between oncologists and radiologists and local/central study sites Integration of multiple quantitative measures of tumor burden Comparing quantitative imaging biomarkers Pooling/analyzing aggregate quantitative imaging data
14 Our technological approach 1. Ontologies for standard descriptors of data 2. Image metadata schemas to capture semantic image content 3. Image warehouses integrated with clinical data compliant with standards for data sharing 4. Tools to analyze quantitative imaging data and provide decision support for assessing cancer treatment response.
15 1. Ontologies Provide standard names for the key entities in cancer imaging domain Diseases Anatomy Imaging findings and measures Imaging procedures Resolve synonyms to preferred terms Several for cancer research (RadLex, NCIt, SNOMED)
16 Image Semantics: Image meaning There is a hypodense mass measuring 4.5 x 3.5 cm in the right lobe of the liver, likely a metastasis. Radiology Image Organ = liver Location = right lobe Measurement = 4.5 x 3.5 cm Diagnosis = metastasis Probability = likely Radiology Report
17 2. Image metadata schemas: AIM Annotation and Image Markup standard to make image contents computable Reader records image observations via annotation tool Enables high-volume analysis of image observations and quantitative image biomarkers
18 ipad (imaging Physician Annotation Device) Plug-in to OsiriX open source workstation OsiriX provides Tools for visualizing and annotating images Plug-ins for image analysis ipad provides Template for collecting AIM-compliant annotations Features for identifying and tracking lesions Automated assessment of treatment response
19 ipad architecture Translator ipad Tempate Exporter Database GUI: plug-in to OsiriX platform ( Template: Structured data entry; Enforces annotation requirements Translator: Image annotations AIM Exporter: Transmits AIM XML to local database or federated storage (cagrid) Database: Saves/queries AIM annotations
20 3. Image warehouse Biomedical metadata manager (BIMM) Resource for recording and storing quantitative image data compliant with cabig standards (AIM) Enables query/analysis of image data
21 4. Tools for decision support and treatment response ipad automatically processes image annotations and evaluates response criteria Can provide decision support and alerts
22 Outline 1. Challenges in clinical cancer research 2. Informatics opportunities and approach 3. Planned deliverables and future work
23 Planned deliverables 1. Tools to measure lesions on images comprehensively and reproducibly 2. Tools to estimate tumor burden according to imaging biomarkers 3. Resource for recording and storing quantitative image data compliant with cabig standards 4. Tools for mining the image data for decision support in clinical trials and research
24 Software framework for quantitative imaging assessment of tumor burden
25 1. Measuring lesions reproducibly: Automated lesion segmentation Automated segmentation Manual segmentation
26 2. Tools to estimate tumor burden: Image Reporting Objective image assessments at each time point Alerts to missing data; required assessments
27 3. Recording and sharing quantitative image data Link quantitative and semantic data to images Sharing on cagrid Input to decision support tools and reporting applications
28 Sum of Maxim Lesion 4. Tools for decision support: Patient response Sum of Maximum Lesion Diameters Over Time /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 Study Date RECIST Score: Tx Remission PD Stable Remission PD Regression Automated lesion tracking Classification of lesions (measurable/non-measurable) Calculation of quantitative imaging biomarkers Temporal analysis of biomarkers response assessment
29 Decision support: Cohort response Automated summary of cohort response data
30 DISEASE Exploratory data mining for discovery e.g., which image biomarker is best in cancer? IMAGE BIOMARKER WHO & Tumor PET SUV Disease RECIST Volume Mean 25-75% max DCE-MRI Ktrans RKtrans Upstroke DI-WI NHL XX Panc CA XX Br CA XX XX XX GIST XX
31 Evaluation studies Evaluation of infrastructure in mock clinical trial Evaluation in two active clinical trials Completeness of information on tumor burden Reproducibility of measurement of tumor burden Tool usability study Assessment of treatment response in cohort studies
32 What we hope to gain Accommodate all quantitative imaging metadata into our infrastructure Determine value of full spectrum of quantitative imaging biomarkers of cancer Widespread adoption of image annotation tools for collecting structured image metadata Demonstrate value of pooled quantitative imaging data for discovery and decision support
33 Thank you. Contact info:
34 Software framework for quantitative imaging assessment of tumor burden
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