Click here to browse the RSNA 2019 Program
TCIA-Initiated
- All Day | AI050 | AI Community, Learning Center
- Crowds Cure Cancer: Help Annotate Data from the Cancer Imaging Archive
- Attendees at this year's RSNA meeting are encouraged to participate in an exciting new activity that will provide valuable data to cancer researchers working in deep learning, radiomics and radiogenomics. This kiosk offers radiologist attendees an opportunity to participate in a 'crowd-sourcing' experiment to accelerate quantitative imaging research. Images are provided by The National Cancer Institute's Cancer Imaging Archive (http://www.cancerimagingarchive.net/), which is a massive public-access resource of cancer radiology images linked to genetic/proteomic, pathology images and clinical data. Many of these cases lack the tumor-location labels needed by computer scientists to jump-start their work on machine learning and quantitative imaging radiomics. Participants will be asked to spend a few minutes anonymously reviewing cases and visually marking their tumor locations. Upon completion, they will receive a ribbon to add to their RSNA badge acknowledging their participation. The data resulting from this process will be openly shared on TCIA with the radiology and computer science communities to accelerate cancer research.
- Crowds Cure Cancer: Help Annotate Data from the Cancer Imaging Archive
- Sunday 4:00-5:30 PM | RCC13 | Room:
Creating publicly-accessible radiology imaging resources for Machine Learning and AI
- To comprehend the challenges in constructing publicly available radiology image datasets.
- Monday 8:30-10:00 AM | RCA21 | Room:
An Introduction to Using the NIH/NCI's Cancer Imaging Archive (TCIA) (Hands-on)
- Access to large, high quality data is essential for researchers to understand disease and precision medicine pathways, especially in cancer. However HIPAA constraints make sharing medical images outside an individual institution a complex process. The NCI's Cancer Imaging Archive (TCIA) addresses this challenge by providing hosting and de-identification services which take the burden of data sharing off researchers. TCIA now contains over 80 unique data collections of more than 30 million images. Recognizing that images alone are not enough to conduct meaningful research, most collections are linked to rich supporting data including patient outcomes, treatment information, genomic / proteomic analyses, and expert image analyses (segmentations, annotations, and radiomic / radiogenomic features). This hands-on session will teach the skills needed to fully access TCIA's existing data as well as learn how to submit new data for potential inclusion in TCIA.
- Monday 10:30-12:00 PM | RCC22 | Room:
Novel Discoveries Using the NCI's Cancer Imaging Archive (TCIA) Public Data Sets
- This didactic session will highlight popular data sets and major projects utilizing TCIA with presentations from leading researchers and data contributors. Attendees will hear presentations about the following projects and data sets: • The Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network • Cancer Proteomics Tumor Analysis Consortium (CPTAC) • Crowds Cure Cancer • Quantitative Imaging Network (QIN) Prostate MRI • Quantitative Image Informatics for Cancer Research (QIICR) • Digital Database for Screening Mammography • Head and Neck Squamous Cell Carcinoma (HNSCC) • 4D-Lung.
- Wednesday 4:30-6:00 PM | RCC45 | Room:
Imaging in Proteogenomics Research
- Learn how medical studies are aggregated within the VA domain for the purpose of conducting medical research, i.e., proteogenomic.
- Thursday 8:30-10:00 AM | RC625 | Room:
Radiomics: Informatics Tools and Databases
- 1) Understand the role of challenges in facilitating reproducible radiomics research. 2) Learn about past challenges and lessons learned. 3) Learn about best practices based on experiences from multisite challenges. 4) Review the meaning and importance of interoperability for quantitative image analysis tools. 5) Review specific use cases motivating interoperable communication of the analysis results. 6) Learn about the tools that support interoperable communication of the analysis results using the DICOM standard. 7) Understand the importance of open science methods to facilitate reproducible radiomics research. 8) Become familiar with publicly available sites where you can download existing radiomic data sets, request to upload new radiomic/radiogenomic data sets, and manage your research projects, and learn about data citations and new data-centric journals which help enable researchers to receive academic credit for releasing well-annotated data sets to the public.
- Thursday 4:30-6:00 PM | RCC55 | Room:
Deep Learning: Applying Machine Learning to Multi-Disciplinary Precision Medicine Data Sets
- This didactic session will provide clinician researchers with examples of ongoing machine learning research in imaging combined with clinical and 'omics data sets, along with examples of where to find and how to link existing cancer image archive cases to other public-access stored databases that contain same-patient demographics, genetics, proteomic, and pathology images. Many of these disparate data types may be presently unfamiliar to imagers - such as mass spectroscopy data that arises from cellular proteomic analysis that propel the need for urgently forming new cross-disciplinary research teams. These datasets, often stored separately by different professional specialty teams, constitute critical complementary elements ultimately needed for reliable Machine Learning. This session pivots out from the clinical images available in the NCI Cancer Imaging Archive (TCIA) collections that acts as the point of origin for linking same-patient demographics, pathology, proteomics, and genetic data so that machine learning efforts can be more scientifically robust.
Community Sessions
Do you have a TCIA-related presentation at RSNA that's not listed below? Contact the helpdesk to request it be added!
- All Day | QRR015 | QIRR, Learning Center
The Quantitative Image Feature Pipeline (QIFP): Automated Computation of Quantitative Image Features and Construction of Predictive Models
- Quantitative image features computed from medical images (i.e., radiomics [1]) can be useful components of biomarkers of diseases including cancer that can be used for treatment selection, assessing response to treatment, and for predicting clinical outcome. As the field evolves, it is still important to discover the best quantitative imaging features for use in associative and predictive models for each cancer type and imaging modality to predict response to existing and new therapeutics, to identify cancer subtypes, and to correlate with cancer genomics. Challenges to progress include the dearth of shared software algorithms, architectures, and tools required to compute, compare, evaluate, and disseminate these quantitative imaging features to researchers and, eventually, to use them for clinical trials and patient management. Our project tackles these challenges with the Stanford Quantitative Imaging Feature Pipeline (QIFP)*, an open source and server-based software system, that gives researchers capabilities for characterizing images of tumors and surrounding tissues. These features can be passed to resident machine learning algorithms to build predictive models, which in turn can be used in multi-center clinical trials with eventual translation to clinical care. The QIFP also allows researchers to add their own algorithms, written in any language for any platform and deployed in Docker containers, for computing novel quantitative image features and for building predictive models for their own studies, and for the benefit of the community. In this way, the QIFP facilitates assessment of the incremental value of new vs. existing feature sets and machine learning algorithms for the development and qualification of imaging biomarkers. *funded by NIH U01 CA187947
- Quantitative image features computed from medical images (i.e., radiomics [1]) can be useful components of biomarkers of diseases including cancer that can be used for treatment selection, assessing response to treatment, and for predicting clinical outcome. As the field evolves, it is still important to discover the best quantitative imaging features for use in associative and predictive models for each cancer type and imaging modality to predict response to existing and new therapeutics, to identify cancer subtypes, and to correlate with cancer genomics. Challenges to progress include the dearth of shared software algorithms, architectures, and tools required to compute, compare, evaluate, and disseminate these quantitative imaging features to researchers and, eventually, to use them for clinical trials and patient management. Our project tackles these challenges with the Stanford Quantitative Imaging Feature Pipeline (QIFP)*, an open source and server-based software system, that gives researchers capabilities for characterizing images of tumors and surrounding tissues. These features can be passed to resident machine learning algorithms to build predictive models, which in turn can be used in multi-center clinical trials with eventual translation to clinical care. The QIFP also allows researchers to add their own algorithms, written in any language for any platform and deployed in Docker containers, for computing novel quantitative image features and for building predictive models for their own studies, and for the benefit of the community. In this way, the QIFP facilitates assessment of the incremental value of new vs. existing feature sets and machine learning algorithms for the development and qualification of imaging biomarkers. *funded by NIH U01 CA187947
- All Day | QRR014 | QIRR, Learning Center
ePAD 2018: Expanded Platform to Support Using New Quantitative Imaging Biomarkers in the Clinical Research Workflow
- Quantitative imaging ('radiomics') is an emerging field that holds promise for making radiology image interpretation more objective and reproducible, with the potential of better characterizing and diagnosing lesions. As the number of quantitative imaging algorithms explodes, however, there is a pressing need for integrating these algorithms into image interpretation workflows in reading rooms of the future. The electronic Physician Annotation Device (ePAD; http://epad.stanford.edu) [1] is an open source tool that enables radiomics algorithms to be deployed in research workflows such as clinical trials. ePAD captures image annotations from radiologists as they view images and executes radiomics feature algorithms, storing all the results in the Annotation and Image Markup (AIM) [2] format, enabling interoperability of annotations. ePAD has a modular design, and is extensible for adding new tools and quantitative imaging applications that the community is developing. In the past year we have made substantial new developments and enhancements in the ePAD platform that are helping to bring new quantitative imaging methods to the reading room of the future: (1) integration of ePAD into the Quantitative Imaging Feature Pipeline (QIFP) [3], an open source and server-based software system for creating and executing image feature pipelines, (2) recent harmonization of AIM with DICOM, with support for the new DICOM-SR/AIM object, permitting interoperability with vendor platforms and support for new radiomics advancements, (3) expansion of ePAD plugins to incorporate new radiomics image feature algorithms, and (4) new applications that leverage radiomics features for decision support. The ePAD interface has been enhanced with a new Javascript user interface to fit seamlessly into the radiologist research workflow and produces structured reports of lesion features to improve clinical decision making. ePAD is being used internationally with over 300 users who created 21,000+ image annotations, as well in national research projects of The Cancer Genome Atlas.
- All Day | QRR020 | QIRR, Learning Center
Cancer Imaging Phenomics Toolkit (CaPTk): A Software Platform Leveraging Quantitative Radio(geno)mic Analytics for Computational Oncology
- Computational research has provided the scientific community with sophisticated algorithms towards gaining a comprehensive understanding of fundamental oncologic mechanisms, while providing substantive insight into the biological basis of disease susceptibility and treatment response, as well as potentially leading to the identification of new therapeutic targets. Rapid deployment and translation of such algorithms via an integrative and easy-to-use platform is required to maximize their benefit in clinical practice. CaPTk1 is a platform that makes this translation possible, thereby enabling clinical researchers to conduct quantitative analyses without requiring a substantial computational background. It can thus be seamlessly integrated into the typical quantification and analysis workflow of a radiologist, emphasizing its clinical potential. CaPTk is a growing software platform focusing on image analysis and machine learning tools for brain, breast and lung cancer, based on a two-tier functionality: Extraction of diverse and complementary features (e.g. textural, morphologic, kinetic) from multimodal imaging. Integration of the extracted features, via multivariate machine learning, into non-invasive diagnostic, prognostic and predictive models.
- Friday 8:30-10:00 AM | RC825 | Room:
Radiomics: From Image to Radiomics
- 1) Learn about the role of image annotations in radiology and their relevance to enabling interoperability and for communicating results and value for machine learning and decision support. 2) Become acquainted with important standards and tools that support the creation, management, and use of image annotations. 3) See case examples of image annotations in practice to enable developing applications that help the practice of radiology. 4) Understand the categories of, and the specific radiomic image features that can be computed from images. 5) Understand the effect and implications of image acquisition and reconstruction on radiomic image features. 6) Learn about workflows that drive the creation of predictive models from radiomic image features. 7) Understand the methods for and the potential value of correlating radiological images with genomic data for research and clinical care. 8) Learn how to access genomic and imaging data from databases such as The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. 9) Learn about methods and tools for annotating regions within images and link them with semantic and computational features.10) Learn about methods and tools for analyzing molecular data, generating molecular features and associating them with imaging features. 11) Learn how deep learning can revolutionize interpretation of medical images.