The Quantitative Imaging Network The National Cancer Institute Then: 1939 And Now: 2016
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 The Cancer Imaging Program The Quantitative Imaging Network 
The National Cancer Institute Then: 1939 And Now: 2016 
Annual Meeting April 10 – 11, 2017 Abstracts The 2017 Quantitative Imaging Network Annual Meeting Shady Grove Facility NCI Joseph F. Fraumeni Jr. Lecture Room TE 406/408/410 Agenda and Abstracts April 10 – 11, 2017 The Quantitative Imaging Network Annual Meeting April 10 – 11, 2017 Agenda Location: Joseph F. Fraumeni Jr. Lecture Hall (TE 406), Shady Grove Facility DAY 1: Monday April 10, 2017 8:00 Introduction and Welcome Paula Jacobs, Ph.D. Associate Director, DCTD
Robert Nordstrom, Ph.D. Branch Chief, DCTD
8:15 Summary of the 2016 QIN-NCTN Planning Meeting Larry Schwartz, MD Chair, Prof. of Radiology Columbia University With Q/A
Hui-Kuo Shu, MD, Ph.D. - Insights into clinical validation Emory University John Buatti, MD. - Potential Impact of Proven QIN tools Chair, Dept. of Rad Oncology University of Iowa
Bruce Cheson, MD Georgetown University
Maryellen Giger, Ph.D. University of Chicago
David Mankoff, MD, Ph.D. ECOG-ACRIN Medical Foundation ************************************************************************** 10:15 BREAK: Poster Session in Seminar Room 110 Tool Demos in Lecture Hall 406 Clinical Trial Design & Dev. Working group – meet on 2nd floor West Wing in Room 910 / 912 ************************************************************************** 11:20 PANEL SESSION
Members: Drs. Schwartz, Kelloff, Shankar, Wahl, Shu, Mankoff, Buatti ************************************************************************** 12:00 Lunch ************************************************************************** 1:00 Presentation of the Young Investigator Award Robert Nordstrom – Dr. Assaf Hoogi, Stanford University Awardee 1:20 CIP involvement in the Interagency Working Group on Medical Imaging Janet Eary, MD. Deputy Associate Director, DCTD Session 2: Pathways to Clinical Utility
1:30 New Funding Opportunity Announcements (PAR-17-128, PAR-17-129) Robert Nordstrom, Ph.D. Branch Chief, DCTD 1:45 Challenges and Collaborative Projects (CCPs): Platforms and resources Keyvan Farahani, Ph.D. Program Director, DCTD 2:00 CCP 1: PET segmentation challenge Reinhard Beichel, Ph.D. Prof. of Electrical and Computer Engineering University of Iowa
2:30 BREAK: Poster Session in Seminar Room 110 Tool Demos in TE 406 PET/CT Working Group meet on 2nd floor East Wing in Room 908 MRI Working Group meet on 2nd floor West Wing in Room 910 / 912 **************************************************************************
Despina Kontos, Ph.D. University of Pennsylvania Zheng Zhang, Ph.D. Brown University
Moderator: Keyvan Farahani Members: ME Giger, W Huang, D Newitt, L Hadjiyski, D Goldgof, R Beichel 4:30 QIN Announcement of 2017 Executive Committee and Working Group Leaders 5:00 Adjourn for the Day
Robert Nordstrom, Ph.D. Branch Chief, DCTD
Bradley J. Erickson, MD, Ph.D. Prof. of Radiology Mayo Clinic, Rochester, MN 8:20 Quantitative Imaging feature pipeline Sandy Napel, Ph.D. Prof. of Radiology Stanford University 8:35 BioInformatics and Data Sharing (BIDS) Working Group Demonstrations of a Quantitative Imaging Pipeline 9:15 Informatics Panel discussion Purpose: Discuss the emerging role of informatics pipelines in QIN translational research Moderator: Keyvan Farahani Members: B Erickson, S Napel, J Kalpathy-Cramer, A Sharma, D Goldgof ************************************************************************** 10:00 Morning break Poster Session in TE410 Tool Demos in TE 406 BIDS Working Group meet on 2nd floor West Wing in Room 910 / 912 ************************************************************************** 10:45 Update on QIN/ITCR activities Juli Klemm, Ph.D. NCI / CBIIT 11:05 Open Session with a few ITCR members (Brief 5 Mins. Presentation for each speaker) Moderator: Yantian Zhang Speakers: Joel Saltz, Hugo Aerts, Andriy Fedorov, Christos Davatzikos, John Flavin, Gordon Harris, Anant Madabhushi 11:45 Open Microphone Discussion 12:00 Lunch Session 4: Joint CBIIT / QIN Collaborations Theme: Meaningful ways to connect clinical data to imaging collections Purpose: Present existing solutions to harmonize disparate clinical data sets
Ed Helton, Ph.D. NCI / CBIIT 1:15 TCIA Update: Support for QIN collaborative projects, challenges and data sharing John Freymann CIP/Frederick National Lab Justin Kirby CIP/Frederick National Lab
Mark Jensen, Ph.D. Leidos / Frederick National Lab
David Clunie, Ph.D. Pixelmed Publishing LLC
Fred Prior, Ph.D. Univ. of Arkansas Ashish Sharma, Ph.D. Emory University
Moderators: Justin Kirby, John Freymann and Edward Helton 4:00 Program wrap-up and closing remarks Table of Contents Presentation Abstracts Section Session 1: Translation of QIN tools into clinical settings Summary of the 2016 QIN-NCTN Planning Meeting 2 Larry Schwartz, MD Columbia University Overview of Pathways to Clinical Trials Project 3 Insights into clinical validation Hui-Kuo Shu, MD, Ph.D. Emory University Potential Impact of Proven QIN tools John Buatti, MD. University of Iowa
Bruce Cheson, MD Georgetown University
Methods and Tools across Modalities and Clinical Tasks Maryellen Giger, Ph.D. University of Chicago ECOG-ACRIN plans for clinical testing of QIN tools 6 David Mankoff, MD, Ph.D. ECOG-ACRIN Medical Foundation Young Investigator presentation: 8 “Automated and adaptive segmentation of diverse cancer lesions for treatment evaluation” Assaf Hoogi, Ph.D. Stanford University CIP involvement in the Interagency Working Group on Medical Imaging Janet Eary, MD. NIH/NCI/DCTD
New Funding Opportunity Announcements: PAR-17-128, PAR-17-129 Robert Nordstrom, Ph.D. NIH/NCI/DCTD
Platforms and resources Keyvan Farahani, Ph.D. NIH/NCI/DCTD PET segmentation challenge 11 Reinhard Beichel, Ph.D. University of Iowa
Despina Kontos, Ph.D. University of Pennsylvania Zheng Zhang, Ph.D. Brown University
QIN applications of Pipelines in Bioinformatics Bradley J. Erickson, MD, Ph.D. Mayo Clinic, Rochester, MN Quantitative Imaging feature pipeline 14 Sandy Napel, Ph.D. Stanford University
Demonstrations of a Quantitative Imaging Pipeline Bradley J. Erickson, MD, Ph.D. Mayo Clinic, Rochester, MN Update on QIN/ITCR activities 16 Juli Klemm, Ph.D. NIH/NCI/CBIIT
NCI CBIIT Update: Ongoing activities of interest to QIN 17 Ed Helton, Ph.D. NCI / CBIIT TCIA Update: Support for QIN collaborative projects, challenges and data sharing 18 John Freymann CIP/Frederick National Lab Justin Kirby CIP/Frederick National Lab The Genomic Data Common (GDC) model for harmonizing diverse clinical data sets 19 Mark Jensen, Ph.D Leidos / Frederick National Lab Assessing gaps in the GDC model to support QIN clinical data sharing 20 David Clunie Pixelmed Publishing LLC Implementing a clinical data sharing strategy for QIN/TCIA Fred Prior, Ph.D. Univ. of Arkansas Ashish Sharma, Ph.D. Emory University Poster Session & Abstracts Poster #
in lung cancer: Dana Farber Cancer Institute
with CT scans: Arkansas State University
H. Lee Moffitt Cancer Center & Research Institute
of lung cancer incidence from size-stratified nodules in The National Lung Screening Trial (NLST): Univ. of South Florida
when using Dose Reduction Methods: UCLA
Sarcoma and HCC as a Model: Columbia University
Segmentation: Univ. of British Columbia
Princess Margaret Cancer Centre, Toronto, Canada
radiomics in Predicting EGFR Mutation Status: Dana Farber Cancer Inst.
Univ. of Washington at Seattle
MRI for the management of GBM patients: Emory Univ./Johns Hopkins
Treatment Effect & Fractional Tumor Burden: Medical College of Wisconsin Poster #
Imaging following Radiosurgery for Metastatic Brain Cancer: Princess Margaret Cancer Centre, UHN, Toronto, Canada
MRI Images using Convolutional Neural Networks (CNNs): Mayo Clinic, Rochester, Minnesota
using machine learning: Mayo Clinic, Rochester, Minnesota
Head and Neck Cancer: Univ. of Michigan (team#3)
learning for imaging-genomic association discovery studies and precision medicine: Univ. of Chicago, Illinois
Diagnosis and Response to Therapy: Univ. of Chicago, Illinois Quantitative Imaging for Assessing Breast Cancer Response to Treatment 57 NCI Quantitative Imaging Network: UCSF (Abstract only)
Cancer Therapy: Oregon Health & Science Univ., Portland, Oregon
multi-parametric magnetic resonance imaging: Mount Sinai, NY, NY
by using CDSS-S and CDSS-T decision support systems: Univ. of Michigan (team#2)
Univ. of Michigan (team#1) Poster #
Oropharyngeal Cancers: Case Western Reserve Univ.
Univ. of Arkansas at Little Rock
The University of Iowa
Treatment Response: Stanford Univ.
Quantitative Cancer Imaging in Clinical Trials: ECOG-ACRIN, Philadelphia, PA Working Groups Posters
A Preliminary Report of the Cooperative Efforts of the NCI’s QIN PET-CT Subgroup
DWI bias correction
Apparent Diffusion Coefficient (ADC) Mapping and Parametric ADC Map DICOM implementation Demonstrations Station #
in cancer therapy trials: University of Iowa
QIN Tools involved in Prospective Clinical Trials 115 Appendix 1: Report on the December, 2016 QIN-NCTN Planning Meeting 119 Appendix 2: Agenda for the National Photonics Initiative Workshop: 143 Strategies for Improving Early Detection of Cancer and Response to Therapies through Imaging Technologies 
QIN and JMI to honor Dr. Larry Clarke in an upcoming special issue The special issue will focus on quantitative imaging methods and translational developments. 
The call for papers will be posted online by March 1st and will be open to submissions immediately. In addition, JMI invites papers based on presentations from the QIN annual meeting in April 2017. Questions about the journal can be sent to JMI@spie.org. Check the journal website http://spie.org/JMICalls for details. Dr. Larry Clarke’s QIN Young Investigator Award Recipient: Dr. Assaf Hoogi, Stanford University Assaf Hoogi is currently a Postdoc fellow in the Quantitative Imaging Lab, school of Medicine at Stanford. Assaf received his MSc and PhD degrees in biomedical engineering from the Technion (Israel) in 2008 and 2013 respectively with specialization in image processing and computer vision. His research interests are in the areas of image understanding and characterization. In particular, Assaf focuses on developing new methods for adaptive lesion segmentation, disease prediction and treatment evaluation by exploring new directions of machine learning and computer vision techniques such as deep learning, sparse coding, deformable models, variational approaches and other cutting edge techniques. 
During his PhD research, Assaf developed a semi-automatic algorithm to quantify intra-plaque neovasculature for evaluating plaque vulnerability, and assessed the risk of stroke by using Contrast Enhanced Ultrasound images of Carotid artery plaques. Assaf detected, segmented and tracked contrast spots over time on ultrasound images using segmentation and multidimensional dynamic programming techniques. Classification of contrast paths into blood vessels and artifacts was performed according to spatial criteria. His PhD was awarded among the five novel researches in the Technion during 2013. During his PhD, Assaf was also an intern fellow in the image processing group in Thorax center, Rotterdam, Netherlands. During his postdoc fellowship, under the supervision of prof. Daniel Rubin, Assaf focuses on developing an adaptive segmentation framework for automated lesion annotation. An adaptive framework that can handle substantial diversity of image characteristics that will be highly desirable, and will supply far more general, fast, accurate and robust segmentation solution, than any ad-hoc designed platforms that are currently available. Such a technique requires a minimal user interaction and will be used as a tool to assess treatment efficacy. Except for lesion segmentation, Assaf also deals with disease prediction and survival analysis by using text and image longitudinal data. Assaf published his research in leading journals and conferences, including the MedIA, IEEE-TMI, MICCAI and NIPS. Yüklə 4,95 Mb. Dostları ilə paylaş:
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