RSNA 2018

The following events will be presented at the 104th Annual Meeting of the Radiological Society of North America (RSNA 2018)

The 3D Slicer Open­Source Software Platform for Translational Research in Quantitative Imaging - RSNA 2018 QIRR Exhibit

3D Slicer is an open­-source software platform for medical image analysis and 3D visualization used in clinical research worldwide. The application offers easy access to the latest advances in post­processing tools developed in academic research through 127 modules and 93 extensions. These tools include segmentation, registration, automated measurements, DICOM interoperability and structured reporting as well as other utilities such as tool tracking and real­time data fusion for image­guided therapy. Freely available for download on Windows, Mac or Linux and distributed under a BSD license, 3D Slicer allows an easy integration of advanced quantitative image analysis tools in the imaging workflow. The latest version of the software 3D Slicer version 4 has been downloaded over a quarter million times worldwide. 3D Slicer is supported by a multi­institution effort of several NIH­-funded consortia, which include the Neuroimage Analysis Center (NAC), the National Center for Image Guided Therapy (NCIGT), and the Quantitative Image Informatics for Cancer Research (QIICR). The development of 3D Slicer ­ including its numerous modules, extensions, datasets, issues reports, and suggestions ­ is made possible by contributors around the world. Over the past 14 years, more than 3,500 clinicians and scientists have attended 3D Slicer training workshops.

Teaching Faculty

• Sonia Pujol, PhD, Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston MA.
• Steve Pieper, PhD, Isomics Inc.
• Andrey Fedorov, PhD, Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston MA.
• Ron Kikinis, MD, Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston MA.

Logistics

• Date: Sunday November 25 -Friday November 30, 8:00am-5:00pm
• Location: Quantitative Imaging Reading Room (QIRR), Lakeside Learning Center, McCormick Conference Center, Chicago, IL

DICOM4QI demonstration and connectathon: Structured communication of quantitative image analysis results using the DICOM standard

Accurate and unambiguous communication of derived image-related information is critical for the emerging applications of quantitative imaging (QI), such as disease burden assessment, evaluation of treatment response and image-guided therapy. Digital Imaging and Communications in Medicine (DICOM) is the standard supported ubiquitously by commercial imaging devices. DICOM defines both communication interfaces and data formats for images and image-related information (including measurements, regions of interest (ROIs) and segmentations).

While a variety of DICOM object classes exist for describing derived image-related information, thus far they have found very limited acceptance both in the academic community and among the manufacturers of radiology workstations implementing QI analysis methods. As a result, longitudinal tracking, comparison of methods, and secondary analyses are challenging, while applying QI for analyzing patient image data is difficult or impossible to perform across different platforms.

DICOM4QI is a collaborative demonstration and connectathon with the goal of increasing adoption of DICOM in communicating QI analysis results in a structured and interoperable manner.

Handouts:

• Fedorov A, Clunie D, Ulrich E, Bauer C, Wahle A, Brown B, Onken M, Riesmeier J, Pieper S, Kikinis R, Buatti J, Beichel RR. (2016) DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research. PeerJ 4:e2057 https://doi.org/10.7717/peerj.2057

• Herz C, Fillion-Robin J-C, Onken M, Riesmeier J, Lasso A, Pinter C, Fichtinger G, Pieper S, Clunie D, Kikinis R, Fedorov A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. Cancer Research. 2017;77(21):e87–e90 http://cancerres.aacrjournals.org/content/77/21/e87.

See details on the DICOM4QI web site: https://dicom4qi.readthedocs.io/

Logistics

• Date: Sunday November 26 -Friday December 1, 8:00am-5:00pm
• Meet-the-expert session: Monday November 27 to Wednesday November 29, 12:15pm-1:15pm
• Location: Lakeside Learning Center, McCormick Conference Center, Chicago, IL

Participants/Organizers

Andriy Fedorov, PhD, Boston, MA (Presenter) Research funded, Siemens AG David A. Clunie, MBBS, Bangor, PA (Abstract Co-Author) Owner, PixelMed Publishing LLC; Consultant, Carestream Health, Inc; Consultant, CureMetrix, Inc; Consultant, MDDX Research & Informatics; Consultant, General Electric Company; Consultant, Healthcare Tech Solutions; ; Christian Herz, Boston, MA (Abstract Co-Author) Nothing to Disclose Philippe Michelon, Montpellier, France (Abstract Co-Author) Employee, Intrasense SAS Mani Benjamin, Foster City, CA (Abstract Co-Author) Employee, TeraRecon, Inc Joost Van Griethuysen, Amsterdam, Netherlands (Abstract Co-Author) Nothing to Disclose Martin Vallieres, PHD, Montreal, QC (Abstract Co-Author) Nothing to Disclose Isaiah Norton, Boston, MA (Abstract Co-Author) Nothing to Disclose Lauren J. O'Donnell, PhD, Boston, MA (Abstract Co-Author) Nothing to Disclose Michael Onken, Oldenburg, Germany (Abstract Co-Author) CEO, Open Connections GmbH Joerg Riesmeier, Oldenburg, Germany (Abstract Co-Author) Consultant, J. Riesmeier Mathieu Hatt, PHD, Nantes Cedex 1, France (Abstract Co-Author) Nothing to Disclose Erik Ziegler, MSc,PhD, Boston, MA (Abstract Co-Author) Nothing to Disclose Gordon J. Harris, PhD, Boston, MA (Abstract Co-Author) Medical Advisory Board, Fovia, Inc; Member, IQ Medical Imaging LLC; Member, Precision Imaging Metrics, LLC; ; Emel Alkim, Stanford, CA (Abstract Co-Author) Nothing to Disclose Daniel L. Rubin, MD, MS, Stanford, CA (Abstract Co-Author) Nothing to Disclose Alex Zwanenburg, PhD, Maastricht, Netherlands (Abstract Co-Author) Nothing to Disclose Paul Wighton, PhD, Chelsea, MA (Abstract Co-Author) Employee, CorticoMetrics LLC Lee Tirrell, MS, Cambridge, MA (Abstract Co-Author) Employee, CorticoMetrics LLC Marco Nolden, Heidelberg, Germany (Abstract Co-Author) Nothing to Disclose Hans Meine, Bremen, Germany (Abstract Co-Author) Nothing to Disclose Peter Oppermann, Bremen, Germany (Abstract Co-Author) Employee, Fraunhofer MEVIS Jayashree Kalpathy-Cramer, MS, PhD, Charlestown, MA (Abstract Co-Author) Consultant, Infotech Software Solution Justin Kirby, Rockville, MD (Abstract Co-Author) Nothing to Disclose James L. Reuss, PhD, Elm Grove, WI (Abstract Co-Author) Employee, Prism Clinical Imaging, Inc Stockholder, Prism Clinical Imaging, Inc Michael Bowen, Elm Grove, WI (Abstract Co-Author) Employee, Prism Medical Alberto Traverso, Maastricht, Netherlands (Abstract Co-Author) Nothing to Disclose Leonard Wee, Maastricht, Netherlands (Abstract Co-Author) Nothing to Disclose Andre Dekker, PHD, Maastricht, Netherlands (Abstract Co-Author) Nothing to Disclose Hugo Aerts, PhD, Boston, MA (Abstract Co-Author) Stockholder, Sphera Inc Steve D. Pieper, PhD, Cambridge, MA (Abstract Co-Author) CEO, Isomics, Inc ; Employee, Isomics, Inc ; Owner, Isomics, Inc ; Research collaboration, Siemens AG ; Research collaboration, Novartis AG; Consultant, MeBio ; Research collaboration, Boston Scientific Corporation; Consultant, Boston Scientific Corporation Ron Kikinis, MD, Boston, MA (Abstract Co-Author) Nothing to Disclose

Novel Discoveries Using the NCI's Cancer Imaging Archive (TCIA) Public Data Sets

Learning objectives: 1) Critically appraise The Cancer Imaging Archive (TCIA)/MD Anderson Cancer Center Head and Neck Squamous Cell Carcinoma (HNSCC) data set. 2) Identify solutions to challenges in sharing and curating RT DICOM data collections. 3) Describe novel discoveries made using the HNSCC data set. 4) Apply TCIA data sets to derive imaging-based predictors of oncologic outcome. 5) Recommend innovative research approaches using extant and future TCIA collections.

Abstract: 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

Teaching Faculty

Janet F. Eary, MD, Bethesda, MD (Moderator) Nothing to Disclose Evis Sala, MD, PhD, Cambridge, United Kingdom (Presenter) Nothing to Disclose Andriy Fedorov, PhD, Boston, MA (Presenter) Research funded, Siemens AG Jayashree Kalpathy-Cramer, MS, PhD, Charlestown, MA (Presenter) Consultant, Infotech Software Solution Daniel L. Rubin, MD, MS, Stanford, CA (Presenter) Nothing to Disclose Aaron J. Grossberg, MD, PhD, Portland, OR (Presenter) Nothing to Disclose Jeffrey F. Williamson, PhD, St. Louis , MO (Presenter) Nothing to Disclose John B. Freymann, BS, Rockville, MD (Presenter) Nothing to Disclose

Logistics

• Date: Thursday, November 29, 10:30am-12:00pm | RCC52
• Location: S501ABC