The Cancer Imaging Archive (TCIA) is a service which de-identifies and hosts a large archive of medical images of cancer accessible for public download. - Update on new data sets - New features - New publications - Other news

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10 minutes with
1. New data sets
2. New features
3. New publications
4. Other news
http://cancerimagingarchive.net

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New data sets

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New Data: SPIE-AAPM ProstateX Challenge
PROSTATEx Classification Challenge
• Release date of training set cases with truth: 21 Nov 2016
• Release date of test set cases without truth: 12 Dec 2016
• Submission date for participants’ test set classification output: 15 Jan 2017
• Challenge results released to participants: 20 Jan 2017
• SPIE Medical Imaging Symposium: 13-16 Feb 2017
142 participants registered already!

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New Data: Prostate Fused-MRI-Pathology

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New features

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New Features: NBIA 6.3 Simple Search Filters

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New Features: NBIA 6.3 Less clicks to search/filter
TCIA Data Portal

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New Features: NBIA 6.3 Additional API features
 Alpha implementation of REST API for “restricted” collections
• Looking for potential testers and use cases
• https://wiki.nci.nih.gov/display/NBIA/NBIA+REST+API+User's+Guide

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New Features: TCIA Data Analysis Centers (DACs)
 DACs are tools or websites which
provide advanced capabilities for
downloading, visualizing, or
analyzing TCIA data
 DACs are not funded by TCIA, but serve
as a construct to enable the research
community to build upon TCIA’s existing
infrastructure (e.g. through ITCR grant
applications)
 TCIA maintains a registry of DACs
to make them discoverable to users
under the “Data Access” menu
 DACs may provide access to TCIA data
using the API or by mirroring their own
local copy of TCIA data

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New Features: BitTorious DAC
 BitTorious is a holistic solution to
collaborative, private data transfer for
organizations needing to share epic
payloads across the Internet in a cost-
scalable, manageable, automated, and
easy-to-use platform.
 Select TCIA collections are being
mirrored on BitTorious at
https://tcia.bittorious.com/.
• 4d-Lung
• Breast-Diagnosis
• Breast-MRI-NACT-Pilot
• TCGA-GBM

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New publications

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1. Ahmadvand P, Duggan N, Bénard F, Hamarneh G, editors. Tumor Lesion Segmentation from 3D PET Using
a Machine Learning Driven Active Surface. International Workshop on Machine Learning in Medical Imaging;
2016: Springer.
2. Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P. Radiomic features from the peritumoral brain
parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term
survival in glioblastoma multiforme: Preliminary findings. European Radiology. 2016:1-10.
3. Jeanquartier F, Jean-Quartier C, Schreck T, Cemernek D, Holzinger A, editors. Integrating Open Data on
Cancer in Support to Tumor Growth Analysis. International Conference on Information Technology in Bio-
and Medical Informatics; 2016: Springer.
4. Song SE, Bae MS, Chang JM, Cho N, Ryu HS, Moon WK. MR and mammographic imaging features of HER2-
positive breast cancers according to hormone receptor status: a retrospective comparative study.
Acta Radiologica. 016:0284185116673119.
5. Chaddad A, Desrosiers C, Toews M, editors. Radiomic analysis of multi-contrast brain MRI for the
prediction of survival in patients with glioblastoma multiforme. Engineering in Medicine and Biology
Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016.
6. Kotrotsou A, Zinn PO, Colen RR. Radiomics in Brain Tumors: An Emerging Technique for
Characterization of Tumor Environment. Magnetic Resonance Imaging Clinics of North America.
2016;24(4):719-29.
7. Zheng C, Wang X, Feng D, editors. Topology guided demons registration with local rigidity preservation.
Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference; 2016:
IEEE.

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Other news 1. RSNA

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TCIA-Sponsored Sessions
Imaging Integration with Cancer
Genomics/Proteomics: Methodologies Leveraging the
Cancer Imaging Archive
The Cancer Imaging Archive: Using 'Big Data' for the
study of Cancer Radiomics, Proteomics, Genetics and
Pathology (Hands-on)

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User Community Sessions
Posters/Exhibits
The Quantitative Image Feature Pipeline (QIFP) for Discovery, Validation, and
Translation of Cancer Imaging Biomarkers
Thursday 12:15-12:45 PM | IN251-SD-THA2 | IN Community, Learning Center
Station #2
Reproducibility of CT Texture Parameters by Leveraging Publically Available Patient
Imaging Datasets
Thursday 12:15-12:45 PM | IN255-SD-THA6 | IN Community, Learning Center
Station #6
Interoperable Communication of Quantitative Image Analysis Results Using the
DICOM Standard
All Day | QRR003 | QIRR, Learning Center
Early Implementation of Radiomics into Clinical Use: How Radiomic Data Can
Change Clinical Care of Patients
All Day | IN109-ED-X
Radiogenomic Analysis of The Cancer Genome Atlas (TCGA)/The Cancer
Imaging Archive (TCIA) Head and Neck Squamous Cell Cancer (HNSCC)
Cohort: Correlations between Genomic Features and Quantitative Imaging
Features
Monday 10:30-10:40 AM | SSC08-01 | Room: S402AB
Radiogenomics Mapping of Non-small Cell Lung Cancer Shows Strong
Correlations between Semantic Image Features and Metagenes
Monday 11:20-11:30 AM | SSC08-06 | Room: S402AB
Targeting Glucose Metabolism in Brain Tumor Initiating Cells: An Novel
Therapeutic Approach for Radiosensitization
Monday 11:50-12:00 PM | MSRO25-09 | Room: S103CD
Practical Radiogenomics: Lessons Learned from the Cancer Genome Atlas
Tuesday 9:40-10:10 AM | RC305-06 | Room: S102AB
Comparison of Novel Multi-level Otsu and Conventional PET Segmentation
Methods for Measuring FDG Metabolic Tumor Volume in Patients with Soft
Tissue Sarcoma
Tuesday 11:20-11:30 AM | RC311-12 | Room: S505AB

16. http://cancerimagingarchive.net
John Freymann
Informatics Manager, Applied/Developmental Research Directorate
Frederick National Laboratory for Cancer Research
Leidos Biomedical Research, Inc.
Support to: Cancer Imaging Program/DCTD/NCI
Imaging Integration with Cancer Genomics/Proteomics:
Methodologies Leveraging The Cancer Imaging Archive
RSNA 2016, Thursday 8:30-10:00 AM | RCC51 | Room: S501ABC

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Welcome
• This meeting is a continuation of NCI-
AAPM off-site imaging genomics meetings
2011-2015.
• NCI Cancer Imaging
Program
• Paula Jacobs PhD
• Consultant
• C. Carl Jaffe MD
• FNLCR Informatics
• John Freymann
• Justin Kirby
• Brenda Fevrier-
Sullivan
• UAMS
• PI – Fred Prior
• ..And countless
voluntary researchers
from around the globe

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Premise of the TCIA-TCGA project:
 Imaging can improve the
pace and accuracy of
genomic discoveries:
Temporal context
Spatial context
Additional biomarkers
Non invasive alternatives
Genomic Features
Clinical
Features
Pathology Features
Imaging
Features
Needed: Big Data and Open Science

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Prerequisite 3: Open Science
• Voluntary, Opt-in, Multi-
institutional Groups from data-
supply sites
• Multi-disciplinary:
• Radiology, Oncology Informatics
Statistics Genomics
• Incentives:
• First opportunity to publish
• Chance to become a thought leader

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10 minute presentations
1. Qualitative Process
2. Quantitative Analysis
3. Statistics
4. Proteomics
5. Deep Learning
•Juan Ibarra,
MD
Baylor College of
Medicine
TCGA Imaging Bladder Research
Group Progress Update
•Sandy
Napel, PhD
Stanford
University
Stanford’s Quantitative Image
Feature Pipeline for Radiomics
Research and Translation
•Erich Huang,
PhD
National
Cancer
Institute
Statistical Methodology for
Analyzing TCIA Imaging Data
•Evis Sala,
MD, PhD
Memorial Sloan
Kettering
Ovarian cancer: the role of
imaging in interrogating tumor
biology and genomics/proteomics
•Maryellen
Giger, PhD
University of
Chicago
Imaging-Genomics Research in
Breast Cancer: Past & Future
Analyses

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Using Big Data for the study of Cancer Radiomics,
Proteomics, Genetics, and Pathology (HandsOn
Intro – overview of scope and intent of archive
Publishing data
Browsing for data
What kinds of 'omics/path data do we have?
Searching/filtering data
Other ways to access the data
Q&A