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- 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.
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