# Presentation: 826584

## Program Origins: 

- caBIG and caIMAGE              -   the ‘pre-quel’   -

- Imaging WorkspaceCarl JaffeNCI Cancer Imaging Program

## Slide 2

## Slide 3

## Slide 4

## Slide 5

##  Gap analysis: imaging’s roles in detection -> characterization -> monitoring

- Lowering barriers to CAD development for quantitative therapy monitoring
- Validating and testing software algorithms
- Data transparency and data sharing
- Changing the culture/community of clinical trials to meet needs of CTWG
- Empowering individual ‘end-users’
- Integrating imaging into bio-informatics

## Phase I

- Develop workspace infrastructure starting with RIDER
- 1st focus: hardware, storage capacity, query structure and GUI, performance, load responsiveness, log files,
- Begin DICOM indexing structure
- Enterprise Vocabulary Services (EVS) and Common Data Elements (CDE)
- Technical  infrastructure – contract software developers & helpdesk
- Open source image transfer technology – MIRC (RSNA)
- HIPAA compliant de-identification & anonymization

## First issues being addressed by RIDER
Harmonization of imaging protocols
Firewalls
Privacy (HIPAA, deidentification / anonymization)
Access
CaBIG Compatibility (linkage to CA research communities and data)
Issues yet to be addressed in RIDER
Database design consensus
Metadata selection
Quality review process
Curation 
Pilot evaluation of software tools by researchers on RIDER data subsets

- Harmonization of imaging protocols
- Firewalls
- Privacy (HIPAA, deidentification / anonymization)
- Access
- CaBIG Compatibility (linkage to CA research communities and data)
- Issues yet to be addressed in RIDER
- Database design consensus
- Metadata selection
- Quality review process
- Curation
- Pilot evaluation of software tools by researchers on RIDER data subsets

_**R**_EFERENCE  _**I**_MAGE  _**D**_ATABASE  to  _**E**_VALUATE  _**R**_ESPONSE to Drug Therapy in Lung Cancer

## First efforts

## Another example

## Cancer Imaging Program (CIP) rationale for a new initiative

- Rests on a ‘gap’ analysis
- What’s the role for government ?
- Would it happen without intervention ?
- What’s the vision ?
- will it lead to ‘disruptive’ innovations ?
- does it have traction across disciplines ?
- Who are the stake-holders ?
- How can they be symbiotically engaged ?

## Example of stake-holder communities 

- Imaging CAD community
- Attracting new non-medical participants
- Imaging clinical trialists in
- Academia and Contract Research Organizations (CROs)
- SPORES
- Cancer Centers
- Device Manufacturers, PhRMA, FDA
- Imaging Response Assessment Teams (IRATs)
- 8 centers coordinated by – AACI
- ...etc

## preliminary ‘Phase II’ discussions to date

- Distributable thin-client viewer
- Mark-up and annotation – structured and distributable
- Bi-directional transfer of both images and mark-up (possibly locked)
- Clinical  metadata –what’s relevant; how connected
- Unique, robust, identification despite asynchronous data submission
- Audit trails and secure transfers for active trials & regulatory submission
- Rendering and visualization
- Revisit some aspects of "Phase I"
- Automatic methods of QA
- Enabling experimentation
- Design a ‘validation’ environment
- Riffing on image technology with a biomedical focus

**Notes:**
Markup more than GSPS, stnrd around XML

## Slide 14

## Just a few topics of interest

- CAD development for quantitating time change needs to be accelerated and is a critical issue for PhRMA and FDA
- Data sharing is not common
- Maintaining permanent archives is burdensome to investigators
- Distributed mark-up processes don’t exist

## Where are we now ?Existing image networks for clinical trials

| Group |  | Trnsfr software | Proprietary? |  | Review |
| --- | --- | --- | --- | --- | --- |
| QARC |  | Dicommunicator |  |  | Images local |
| ACRIN  |  | Preview 32 |  |  | Images local & pushed |
| COG-Phase I  |  | GRID |  |  | ? |
| PBTC  |  | ? ftp |  |  | Local |
| CALG-B OSU  |  | ? |  |  | ? Local |
| ATC  |  | ? |  |  | N/A |
|  |  |  |  |  |  |
| caBIG 'Ideal' |  | RSNA-MIRC | open-source |  | distributed image and markup |

## MIRC

**Field Centers**
**at Remote**
**Clinical Sites**

***DICOMHTTP***
***HTTPS***

**Submission**

**MIRC Storage Service**

**MIRC DICOM Service**

**MIRC Database Service**

**Firewall**

**MIRC**

**I3 DB**
**(Annotations:**
**Image, Clinical,**
**Markup, etc.)**

**Hibernate**** ****(I3 ORM)**

**I3 Object API**
**caCORE SDK**

**Data Manager**

** **
**Curation**

**Data Manager**

**CSM API**
** **

**Processors**
**Query, Result set, User Registration, Lookup Manager, CSM, Export**

**Data Transfer**
**Object (DTO)**

**Java Server**
**Faces (JSF)**

***Cancer Centers***

**Researchers**

***Academia***

***Industry***

**Hibernate**** ****(CSM)**

**Hibernate**** ****(I3 ORM)**

***Data***
***Access***
***Layer***

***HTTP***
***FTP***

**MDB**
**Image zipping**

**File**
**System**
**(DICOM, XML, DOC)**

**JBoss**

**Tomcat**

**Retrieval**

## Why is imaging in clinical trials so important ? 

- Why is imaging in clinical trials so important ?

## Slide 19

## FDA reform plans

## The Value of Image Data

- Reliable image data could lead to:
- Smaller clinical trials with fewer patients
- Earlier go/no decisions on compounds
- Faster regulatory approval
- Shorter time to market

## Cost to conduct a study*

| Therapy |  | Sample Size |  |  |  |  | Trial Cost* |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | Superiority |  | Non-inferiority  |  |  | Superior |  |  | Non-inferior  |
| SURVIVAL STUDY
CPT-11/5-FU/LV  Oxali/5-FU/LV |  | 2200 |  | 5700 |  |  | $88 M |  | $228 M |  |
| TTP STUDY
CPT-11/5-FU/LV  Oxali/5-FU/LV |  | 400 |  | 750 |  |  | $16 M |  | $30 M |  |
| 
*This example from TP Therapeutics on colon CA, assumes a costs of  $40,000/patient |  |  |  |  |  |  |  |  |  |  |

- Comparison Between **SURVIVAL** & **TTP** Studies

**Notes:**
Based on fairly standard expenses of $40,000 per patient, total costs for future survival studies could reach stratospheric heights. 

## Imaging in Clinical Trials - the context

- NCI spends ~ $750 M per year on cancer therapy research (mostly drugs)
- PhRMA spends far more
- But only 2% of  cancer patients are on formal clinical trials
- isn’t all cancer therapy experimental since few cancer therapies are ‘truly curative’ ?
- Imaging is not a key element in trials
- It’s usually ‘correlative’ or a 2ndary end-point

## The current model isn’t working

- Quantitative tools are deficient or lacking
- Image acquisition protocols are diverse, ad hoc and often subtly violated
- Central reads are rare and expensive
- Images are poorly connected to clinical data & outcomes
- Access to primary images is limited or restricted
- The categorical bins of CR,PR, SD, PD are taken as truth
- High image quality in past clinical trials is a myth
- Most trial images are ‘digitized’ rather than ‘digital’

**T 0**
**120 KV**
**Film @ low  W&L**
**7 mm cuts**

**T 1**
**140 KV**
**late i.v. con**
**digital @ **
**higher W&L**

**T 2**
**140 KV**
**pre i.v. bolus**

**T 3**
**120 KV**

## Problem summary

- Local (site) reads are unreliable in clinical trial circumstance (30% disagreement)
- Site Q/A may not be protocol compliant and fully reliable
- Prior to reproducible CAD-enhanced reading [so-called] ‘central’ reads are necessary
- There is no economic model that lends itself to convenient, low-cost cooperative reading of image-monitored therapies

## Cancer Imaging Program (CIP) objectives 

- Provide technical infrastructure to lower the barriers to Clinical Trial participation
- Enlarge the CAD community by providing ‘validated’ image datasets for training and testing image-quantitative algorithms
- Direct resources to enable PhRMA and FDA to use imaging more effectively and reproducibly for drug development and approval

## Imaging technology- starting points -

- HIPAA-compliant image transfer mechanisms (e.g. MIRC)
- Indexed image databases open & accessible to CAD developers
- Distributable image mark-up tools, coordinated with the DICOM images
- A standard mark-up file format (possibly XML)
- Unique identifier structure for images and their metadata permitting asynchronous distributed submissions
- Connectivity software for linking clinical metadata to specific images

## Imaging technology- in more detail -

- Distributed analytic viewing and mark-up to obviate ‘central read’ geographic limits
- Harmonization tools to overcome technical differences of scanners (e.g. PET, MDCT, etc)
- Image databases securely accessible/auditable for special NCI CTEP (and maybe FDA) reviews

## Imaging objectives- in more detail cont’d -

- Vocabularies and common data elements directly harmonized with caBIG and NCI processes
- NLP and structured image reporting
- Methods to provide CDE tools that allow distributed, asynchronous community participation
- Meaningful connectivity between imaging, tissue specimens and genetic data as a caBIG compliant process
- Any other implementable and achievable project devised by the WorkSpace

## What else is wrong with the status quo?variables impacting clinical trials

- Imaging _instrument_ variations
- At start time of trial
- Over accrual period
- _Acquisition_ variations
- Local preferences and practices
- Cost, patient and radiation constraints*
- Sampling interval variations
- _Reader_ variation
- Run-in training, learning curves, ROC

## For Imaging to become a biomarker- the essentials -

- Uniformity of instrumentation
- Uniformity of protocol-specified acquisition
- Independent quality control
- Reliability/independence of reader interpretation
- Provenance, auditability and storage accessibility

## Uniform Protocols for Imaging Clinical Trials

- Multi-societal, industry and gov’t developing agreements on imaging methods for clinical trials – **ACR coordinated**

## Visualization vs. Quantitationdeveloping & testing tools for (semi-)automated segmentation

## Endpoints for determining clinical benefit (validation)

- Time To Progress of disease (TTP)
- Progression Free Survival (PFS)
- Response Rate (RR)
- 5 year disease free survival
- 3 year disease free survival
- Cause-specific mortality

## RECIST vs. Volume

**Diameter=17.7 mm**

**Diameter=17.1 mm**

**RECIST vs. Volume**

- Dr Larry Schwartz, MSK

**Volume=**_**886**_** mm****3**

**Volume=**_**525**_** mm****3**

## Sounds good, so why not calculate volumes?

- No fully automatic, objective methods
- Semi-automatic methods are time-consuming, labor-intensive, and/or not user-friendly
- And importantly:   _not  validated against clinical outcome_
- How much must it improve upon the linear RECIST measurement to be useful, given the extra effort ?

## Example: you still need observer domain knowledge !

**Baseline**

**13 wks  (– 7 %)**

**27 wks   (PR – 43 %)**

*metastatic renal cell carcinoma*

**Notes:**
Rx with epothilone a microtubular apparatus stabilizer

## Another example problem:how is inhomogeneity addressed?

- Another example problem:how is inhomogeneity addressed?

## 

****

- Local read

- Network read

- Network read with CAD

## 

****

- Local read  - quantitation: simple

- Network read - quantitation: simple with consensus

- Network read with CAD - quantitation: complex

## Measurement reproducibility

- 

****

- Local read

- Central read

- Central read with CAD

**Measurement**** ****reproducibility**

## Leveraging Resources

**Clinical Data Collection:** Costs covered by ongoing clinical investigations
**CaBIG **“*Imaging Workspace*”: Web-accessible public resource
**Database Resource: **Engage medical and non-medical community in CAD development.
**Standardized Methods for Software Evaluation:** Improved clinical trial results
**Public Private Partnerships: **Cancer centers, other academia, device and drug industry, CROs, FDA, NIST and FNIH

## The Grand Challenge: Meta Data Analysis: Genomics and Imaging

**Clinical Decision Making**
**Understand Sources of uncertainty.**

**The Grand Challenge: Meta Data Analysis: Genomics and Imaging**

## caBIG Imaging Workspace: 

**Near Term Goals**
**Clinical Trial Image Archives**
**Standards for Clinical Trial Image Data Collection, Transmission and Archiving**
**Open-source Software and Non-proprietary Work-station Solutions for Data Sharing**
**Software Tools **
**Public Annotated Databases**
**Reference Data Sets with ‘Truth’ (e.g. outcome)**
**Longer Term Goals**
**Artificial Intelligence (AI) Enhanced Data Mining**
**Meta Data Analysis for Clinical Decision Support**

## NCI: Research OpportunitiesPAR: Academic-Industry Partnerships

- R01 Goal:
- Accelerate translational research to encourage delivery of technologies and methods for clinical trials.
-  Partnerships
- Industry Device and/ or Pharma
- (FDA, NIST).
- Research emphasis:
- Imaging/Drug Response.
- Multi-Modality Imaging Platforms
- Harmonization of data collection
- Open Source platforms and tools
- Database development and validation tools

**Notes:**
PAR RO1 with an emphasis on development of validation methods for imaging technology. For informatics this could be support for database development, open source tools, open source platforms, and process to accelerate a technology to phase 0.phase 1 trial. Expect to emphasize the use of the imaging workspace but NOT a requirement. Self organized consortiums will be encouraged to develop a consensus on validation methods so that standardized methods may later be implemented 

## Path Forward

- Think different
- Take advantage of a _NATIONAL_ workspace
- Don’t emulate existing services
- Funding is tight for simply a network version of the status quo
- It’s easier to find funding for a visionary goal with a working community that supports it
- Think BIG
- Think caBIG
- Be part of it - and stay committed