Introduction to CTIIP
Project 1
Problem Statement
Approach
Project 2
Problem Statement
Approach
Project 3
Problem Statement
Approach
Put together a primer, examples of data, use cases, how to carry out an integrative query, so that it is understandable.
Introduction
Each domain is at a different step in maturity. proprietary data formats
Pathology problems:1. proprietary data formats that cannot be displayed and manipulated in the same tools. Solution is to integrate caMicroscope with OpenSlide (allows us to read prop. formats without converting images). Makes a large number of image formats accessible. 2. no standard for markups and annotations. So we're creating microAIM.
Co-clinical and animal model images: most imaging machines for animal model imaging do not follow the DICOM standard. We developed a supplement to the DICOM standard to accommodate small animal imaging (standard out for balloting). We want to include co-clinical/animal model data in the integrative queries. For this new standard to be used, equipment manufacturers would need to incorporate this standard when they develop machines/software.
Challenges: read one-page document. We want to use pathology images in the challenges. The tool used to display the markup and annotations (for the pathology images) is caMicroscope. There will be a challenge in which animal model data will be used. Give people images they have never seen before and develop algorithms (like to circle all the nuclei). Ground truth decided by a pathologist and a radiologist. The algorithm that comes closest to ground truth is the winner.
Three separate sections with problem/solution for each aim. Status of the solution.
Imaging-based cancer research is in the beginning phase of an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to prognosis or treatment across three complementary research domains: in vivo clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of image data. In this project, we propose to develop and deploy software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
Informatics have to let us communicate. Need to be able to compare the data between the omics.
The overarching goal of this project is to establish an informatics infrastructure that demonstrates the benefit and feasibility of data interoperability across the three domains: Genomics, Diagnostic Imaging, and Digital Pathology. The intent is to identify and address the interoperability needs to support specific research objectives, with the goal of demonstrating the need to scale up. The scope is limited to pilot data sets, and the intent is only to demonstrate the infrastructure. Creation of more robust tools that leverage the interoperability and infrastructure created in this project would be supported through extramural support after the benefit of scaling up has been demonstrated.
CBIIT has worked extensively for several years in the area of data standards for both clinical research and healthcare, working with the community and Standards Development Organizations (SDOs), such as the Clinical Data Interchange Standards Consortium (CDISC), Health Level 7 (HL7) and the International Organization for Standardization (ISO). From that work, EVS and caDSR is harmonized with the BRIDG, SDTM, and HL7 RIM models. Standardized Case Report Forms (CRFs), including those for imaging, have also been created. This work provides the bioinformatics foundation for semantic interoperability in digital pathology and co-clinical trials integrated with clinical and patient demographic data and data contained in TCIA / TCGA.
Three complementary projects were proposed and approved.
- Digital Pathology; Integration of TCIA and TCGA - The first project will address interoperability for digital pathology data, improve integration and analytic capabilities between TCIA and TCGA, and raise the level of interoperability to create the foundation required for pilot demonstration projects in each of the targeted research domains: clinical imaging, pre-clinical imaging, and digital pathology imaging.
- DICOM Standards for Small Animal Imaging; Use of Informatics for Co-clinical Trials – The second project will address the need for standards in pre-clinical imaging, and test the informatics created in project 1 for decision support in co-clinical trials.
- Pilot Challenges: The third project will also leverage the work done in project 1 to further enhance the informatics and infrastructure in several “Pilot Challenges.” These challenges will be designed to develop knowledge extraction tools and compare decision support systems for the three research domains, which will now be represented as a set of integrated data from TCIA and TCGA. The intent is not to specifically implement a rigorous “Grand Challenge”, but rather to develop “Pilot Challenge “projects. These would utilize limited data sets for proof-of-concept, and test the informatics infrastructure needed for such “Grand Challenges” that would be scaled up and supported by extramural initiatives later in 2014 and beyond.
Abstract
Problem Definition
Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
How do we better treat our patients?
High-Level Solution
Solution Details
Business Benefits
Summary
Call-to-Action
Conclusion
The National Cancer Institute Clinical and Translational Imaging Informatics Project (NCI CTIIP) seeks to increase the understanding of genetic mutations to better diagnose and treat patients with cancer. Its component projects support research goals from the domains of genomics, diagnostic imaging, and digital pathology. These research goals include:
- Creating an open-source digital pathology image server that can host and serve digital pathology images for any of the major vendors without recoding, facilitating the integration of pathology data with radiographic, genomic, and proteomic data.
- Establishing an informatics and IT infrastructure to implement pilot challenges for clinical and pre-clinical studies that integrate the genomics, diagnostic imaging, and digital pathology domains.
- DICOM Working Group 30?
- Developing DICOM standards for small animal imaging and identify co-clinical datasets to test the integration of TCIA and TCGA for this data.
NCIP created in 2013 as a part of CBIIT.
Imaging Informatics Working Group created across NCI. Explore needs for research in in vivo, pathology, and omics. Radio-patho genomics for omics.
Need to generate proper therapy for a patient. Look at in vivo imaging, radiology and pathology, run a gene panel to look for abnormal. Look at co-clinical trials (model of a tumor in a mouse that is similar to a human. Experiment therapies on mice.) Run an integrative query to develop a sophisticated diagnosis. Search big data.
Visual pathology integrative queries–Ashish at Emory. Imaging consistent with ground truth.
Three pilot challenges–pathology, radiology, co-clinical.
Medical Image Computational and computer-assisted Intervention: MICCAI
Interventions in tumors, cardiology, etc that are image-based
Mass General will guide the pilots
Ground truth: find the compatibility of the informatics that we need to run pilots. Take images out of TCIA, CGA, clinical data and compare them.
Jasharee doing MICCAI Challenge in Munich. Segmentation of nuclear imaging in pathology. Combined radiology and pathology classification.
Want to be able to say that these informatics allow us to compare the pathology, rad, co-clinical findings.
Document the approach, technology, application to do a MICCAI challenge the way Jaysharee does it. See their order of march.
Ashish has the conceptual approach for an integrative query system. Learn his order of march.
Need to explain how the challenge management system and integrative query system play together in a scientific scenario.
three tocs: one for challenge steps, one for int query sys. how well does it integrate; what are the common–how do we annotate the tumor in MedICI such that it is compatible with the annotations in the components of the integrative query system. What relationships can we find in the informatics in the animal and patient findings.
Describe each section separately and then see if we can merge the two to answer the scientific question.
Imaging-based cancer research is in the beginning phase of an integrative-biology revolution. It is now feasible to extract large sets of quantitative image features relevant to prognosis or treatment across three complementary research domains: in vivo clinical imaging, pre-clinical imaging, and digital pathology. These high-dimensional image feature sets can be used to infer clinical phenotypes or correlate with gene–protein signatures. This type of analysis, however, requires large volumes of image data. In this project, we propose to develop and deploy software that supports a comprehensive and reusable exploration and fusion of imaging, clinical, and molecular data. Within these three research domains, only one, clinical imaging, has made some progress in terms of establishing a framework and standards for informatics solutions. For pre-clinical imaging and digital pathology, there are no such standards that allow for the seamless viewing, integration, and analysis of disparate data sets to produce integrated views of the data, quantitative analysis, data integration, and research or clinical decision support systems.
This research theme has been systematically explored by the NCI Imaging Informatics Working Group, as a joint effort by NCI extramural staff and CBIIT staff. In addition, these informatics needs were well articulated at the Imaging Informatics Working Group Workshop held on March 5th, 2013 and April 14th, 2015 that were in conjunction with the Quantitative Imaging Network (QIN) annual meetings. At these workshops, QIN members leveraged their experience to explore novel informatics solutions for pre-clinical imaging, co-clinical trials, and digital pathology, and validated the needs met by the projects.
Some of the relevant, high-level takeaways from the workshop can be summarized as follows:
Standards
- The lack of standards in pre-clinical and pathology prevents the ability to share and leverage data across studies and institutions.
- There are differences between the domains, and therefore there should be careful consideration of where there are commonalities in semantic interoperability, and where there is not.
Community Engagement & Needs Identification
- Identify cross-NCI needs and gaps, and work with projects that represent both internal and broader community needs.
- Engaging the broader community is important, in order to gain consensus on needs and gaps. Working with professional societies is helpful in this regard.
- Standards Development Organizations (SDOs) may also be helpful partners.
- Keep the initial group working on a standards project small; use the wider community to validate and credential what is developed.
Incentivizing Adoption
- NCI should incorporate data sharing requirements into grants.
- NCI could create and fund projects that can only be successful if standards are utilized / developed (i.e., “pose questions that can only be answered through increased standardization”).
Strategy
Challenge Management System, MedICI
Jaysharee's program: Medical Imaging Challenge Infrastructure: MedICI
- Based on open-source CodaLab
- ePAD (created by Daniel Rubin's group at Stanford): tool for annotating images, creates AIM images
- caMicroscope
http://miccai.cloudapp.net:8000/competitions/28
- Competition #1: MICCAI challenge has a training phase where they train their algorithms. A test phase where they run their algorithms on images they have never seen before. They are compared to the ground truth that is determined beforehand. caMicroscope is used to see what is there before and to visualize the results. Overlap/completeness match determines the winner.
- Competition #2: They are given slides.
From PPT: Use titles of slides
Setting up a competition by an organizer. Organizer creates competition bundle.
Can go to cancerimagingarchive.net and create shared lists. Shared lists are pulled into CodaLab. That is how they get the test and training data.
Next is to create ground truth.
Regions of interest in a tumor for annotations are necrosis, adema, and active cancer. Radiologists create the ground truth.
Once participants upload their results, they can see them in ePad.
Integrative Query System
Look at Ulli's PPT
Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
Integrative Queries
Programmatic Access to Data to TCGA-related image data.
Extend software to support data mashups between image-derived information from TCIA and clinical and molecular metadata from TCGA.
What the data is used for
Relate data from TCIA, caMicroscope, animal model
genomics, animal
how do we make a decision on a firm diagnosis?
Get queries and relate it to the human data and vice versa
System should integrate clinical data (from TCGA), preclinical data (comes from UC Davis)
Use case: Breast cancer has biomarkers (progesterone status, etc.). One question to ask is "if the estrogen status is negative in humans, what does the pathology look like?" Then compare this to mice. Is the model we have a good model for the human condition?
If you treat a mouse model that has an ER negative status with a certain drug, what is the outcome? Then see this in humans.
We are setting up the data structure so when that is done, we'll be able to see what use cases are possible.
To make data comparable, we must collect it in a structured fashion. Common Data Elements for TCGA.
We are pulling data out of caDSR (ER negative and positive, other common data elements) and we are asking Bob Cardiff's team to ask the same questions so that we can compare human and mouse data.
We are exploring the standardization of informatics. Use all the tools we have to create standard informatics to compare patient to animal data. We are using the available standards: DICOM, AIM, micro AIM. Fundamental to integrative queries.
If you did an integrative query, how would you do it? Data calls to do different integrative queries. How would you use sufficient standard data. Come out with information that will allow you to make a decision. Pilot challenges to compare the decision support systems for three domains.
We need a clear explanation of how to do this.
Data mashups that allow us to
Explain our complicated project in a simple manner so they understand why we are doing and what we are doing.
