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10 minutes with1. New data sets

2. New features

3. New publications

4. Other newshttp://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

http://cancerimagingarchive.net

John FreymannInformatics 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