QIN: Overview of Scientific Challenges

Transcription

1 QIN: Overview of Scientific Challenges Goal: Robust methods for Imaging (QI) as a Biomarker for Response to Therapy RIDER: Public Resource to promote QI Methods and Standards QIN: Quantitative Imaging Network NBIIT: Critical Role of cabig Infrastructure. QIN: Technical resource for ACRIN-clinical trials.

2 RIDER: NCI Web-Based Public Resources ( ) Evaluation of Responses to Cancer Therapies Mission and Goals Eight Contracts: Scope Quantitative Imaging (QI): Retrospective data collection Implementation Model: Phantom Data collection Patient retrospective data Measurement variance-bias Compare relative performance Clinical decision tools Commercial Platform Example: PET CT Phantom Design Simulated CT, PET CT, DCE MRI and DI MRI Longitudinal Repeat Studies CT, PET CT, DCE DI MRI DWI MRI: Phantom Platform Harmonization Study DWI MRI PET CT Stakeholders and Agencies ACIN, RSNA, SNM, AAPM,. NCI cabig, NIH NIBIB, FDA, NIST. Trans Oncology 2009 : Results

3 RIDER-ACRIN Core: PET CT Phantoms Modified ACR phantom with 68 Ge cylinder inside 22 cm 10 cm Goals: Adoption by ACRIN CORE, QIN, FDA and RSNA QIBA

4 RIDER-ACRIN: Single-site repeat PET/CT scans Long Term Goal ( ): Model variance to minimize platform dependence in collaboration ACRIN Core and QIN. Plots of recovery coefficient (RC) = measured in ROI/true A B C Absolute recovery coefficients from 3D-OSEM reconstructions using 7, 10, and 13 mm smoothing. SUV variation across sites-platforms-time: range 14-40% Maximum ROI recovery coefficients versus sphere diameter for the same phantom repositioned and imaged 20 times using PET/CTs from three vendors Doot et al. 2010

5 RIDER ACRIN DWI: Minimize Platform Dependence (GE, Siemens, Philips) Diffusion Coefficient of 0 o C Measured at 4 b values Within 5% 30 + Sites US and UK 1.5T 3T GE 1.5T 3T Philips 1.5T 3T Siemens

6 Planned DWI Workshop Goals: Jan 2011 Evaluate the status of minimize platform dependence of DW MRI using Phantoms Evaluate the current literature and status of clinical measurements as a biomarker Develop a plans for a targeted clinical trial to be implemented by ACRIN Explore methods for further improvement to be addressed by QIN.

7 QIN IT: cabig Imaging Product Line Visualization Supports GRID Computing Archive The National Biomedical Imaging Repository (NBIA): searchable repository of in vivo cancer images Transfer extensible Imaging Platform (XIP): an easily extensible open source platform that provides development and implementation support to allow multiple algorithms to run on proprietary or generic workstations. Adjudication Imaging Core Middleware is a set of tools, libraries and applications designed to create a bridge between the unique medical model of DICOM, cagrid and other non-specific radiology software and systems. Standards Baseline +20 weeks Max Diameter 32.6mm Volume 9.48cm3 55% increase Algorithm Validation Tools (AVT): allow determination of the consistency of any measurement method for detecting change. AVT can also serve to do an analysis of variability of multiple readers of imaging studies and/or variability of multiple algorithms UPICT Annotations and Imaging Markup Developer (AIM): XML standard for medical image annotation and markup

8 QIN: Stanford: Ontology-Image Annotation ipad: Imag data ( image phenotype ) represented as feature vector

9 QIN: Uni of Iowa: Architecture

10 GRID Computing: Federated Databases: NCI Workshop Nov 2010 Svcs Svcs Quantitative Clinical Decision Clinical IMAG Apps Workspace Making NCI Research Networks QIN, NTR NBIA v4.4 AIM v2.0/1.7 External Apps AVT Middleware VP, AIME XIP/XIP Host IMAG Workstation Remote Benchmarking 1. QI Imaging Software Tools 2. Correlative Imaging-Genomics NCI Imaging Workspace

11 Example of a Small Company: Definiens Turning Images into Knowledge NCI QIN Collaborator: Moffitt and Stanford High Content Screening Small Animal Imaging Molecular Pathology Medical Imaging Tools: Scalable knowledge based clustering methods across resolution scales from the cellular to the organ level.

12 Example: QIN Investigators: Moffitt-USF Automatic Batch Processing of Volume Image of the Brain Critical for NCI TCGA initiative for image correlation studies Image 1: T1 MRI weighted image Image 2: Operator independent Knowledge Based Fuzzy Clustering Image 3-5: Other operator dependent reference methods.

13 QIN Summary Of Challenges QIN will serve as a technical resource for ACRIN and other clinical trial groups to address new challenges: Platform independent methods for data collection Advanced QI methods for data collection New phantom Q/C methods Optimized and validated tools for data analysis and modeling, that are ideally operator independent IT and informatics resources to permit quantitative correlation with genomics and other laboratory methods

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