![7
Data Collection Center:
Publishing data in addition to manuscripts
Data citations for both primary and analysis data to enable reproducible research
Analysis Dataset Citation (derived image features)
Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L,
Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat
P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J,
Flanders A, Brat DJ. (2014). MR Imaging Predictors of Molecular Profile and Survival: Multi-
institutional Study of the TCGA Glioblastoma Data Set. The Cancer Imaging Archive.
http://doi.org/10.7937/K9/TCIA.2014.4HTXYRCN
Publication Citation (cites specific data used)
MR imaging predictors of molecular profile and survival: multi-
institutional study of the TCGA glioblastoma data set. Radiology.
2013 May;267(2):560-9. doi: 10.1148/radiol.13120118. Epub
2013 Feb 7. PubMed PMID: 23392431; PubMed Central PMCID:
PMC3632807.
Primary Data Citation (TCIA images used for study)
Scarpace, L., Mikkelsen, T., Cha, soonmee, Rao, S., Tekchandani, S.,
Gutman, D., … Pierce, L. J. (2016). Radiology Data from The Cancer
Genome Atlas Glioblastoma Multiforme [TCGA-GBM] collection. The
Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9](https://image.slidesharecdn.com/tciaforqin-2018-05-15-190325195755/85/A-practical-guide-to-using-The-Cancer-Imaging-Archive-for-QIN-Challenges-and-Collaborative-Projects-7-320.jpg)
A practical guide to using The Cancer Imaging Archive for QIN Challenges and Collaborative Projects
- 1. http://cancerimagingarchive.net Justin Kirby – justin.kirby@nih.gov Frederick National Laboratory for Cancer Research Leidos Biomedical Research, Inc. Support to: Cancer Imaging Program/DCTD/NCI A Practical Guide to Using TCIA for QIN Challenges & Collaborative Projects QIN F2F 2018
- 3. 3 The Cancer Imaging Archive: Brief intro • 84 data sets consisting of over 41,000 subjects available for download • Covers most modalities (CT/MR/PET/RT) • Wide variety of cancers + phantoms • Patient populations vary from a handful to >26,000 (NLST) • Many have associated meta-data Demographics/outcomes/therapy Pathology imaging Radiologist expert and automated computational analyses (segmentations, features) • ‘Omics ties to GDC/TCGA, CPTAC, and GEO http://www.cancerimagingarchive.net
- 4. 4 Organization of TCIA ecosystem The Cancer Imaging Archive Data Collection Center •Tools and staffing to support data collection, curation, and de- identification Data Access •Browse (home page) •Filter/Search (Data Portal) •REST API •Analysis Data Data Analysis Centers •3rd party web sites or tools which connect to TCIA’s API or mirror its data
- 5. 5 TCIA services (not just software) Relieves PI of majority of data sharing burden/risks • Data hosting with >99% uptime • De-identification using pre-configured RSNA’s Clinical Trials Processor (CTP) and DICOM PS 3.15 Annex E standards • Multi-tiered QC process inspects both DICOM headers and pixels for PHI and integrity of data set Phone/email support available for end users and submitters Extensive documentation throughout the site Exposure to a large community of researchers • Increase visibility of your work, get more citations!
- 6. 6 Data Collection Center: Publish Your Data Primary Data (radiology, pathology, clinical, etc) Analysis Results (derived from primary data) Image credit: Hugo Aerts
- 7. 7 Data Collection Center: Publishing data in addition to manuscripts Data citations for both primary and analysis data to enable reproducible research Analysis Dataset Citation (derived image features) Gutman DA, Cooper LA, Hwang SN, Holder CA, Gao J, Aurora TD, Dunn WD Jr, Scarpace L, Mikkelsen T, Jain R, Wintermark M, Jilwan M, Raghavan P, Huang E, Clifford RJ, Mongkolwat P, Kleper V, Freymann J, Kirby J, Zinn PO, Moreno CS, Jaffe C, Colen R, Rubin DL, Saltz J, Flanders A, Brat DJ. (2014). MR Imaging Predictors of Molecular Profile and Survival: Multi- institutional Study of the TCGA Glioblastoma Data Set. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2014.4HTXYRCN Publication Citation (cites specific data used) MR imaging predictors of molecular profile and survival: multi- institutional study of the TCGA glioblastoma data set. Radiology. 2013 May;267(2):560-9. doi: 10.1148/radiol.13120118. Epub 2013 Feb 7. PubMed PMID: 23392431; PubMed Central PMCID: PMC3632807. Primary Data Citation (TCIA images used for study) Scarpace, L., Mikkelsen, T., Cha, soonmee, Rao, S., Tekchandani, S., Gutman, D., … Pierce, L. J. (2016). Radiology Data from The Cancer Genome Atlas Glioblastoma Multiforme [TCGA-GBM] collection. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9
- 8. 8 Data Descriptor Journals Journal Recommended Repositories Nature Scientific Data https://www.nature.com/sdata/policies/repositories#imaging Medical Physics http://aapm.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)2473-4209/about/author- guidelines.html (see section 13-Medical Physics Dataset Articles) Elsevier Data in Brief http://www.elsevier.com/authors/author-services/research-data/data-base-linking/supported- data-repositories#Health PLOS ONE http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories Research Data Support https://www.springernature.com/gp/authors/research-data-policy/repositories-bio/12327160 Publish detailed descriptions about how to use your TCIA data to gain academic credit (publication/citations) in addition to the novel scientific findings you might publish in traditional journals.
- 9. 9 A significantly growing community! 38 incoming data sets in varying stages of curation Over 10,000 active users per month • Up from ~3,000/month in 2015 Downloads of 40-50TB per month • Up from ~2TB/month in 2015 613 publications based on TCIA data • 134 new publications in 2017
- 10. 10 Researchers want to share data – 38 data set queue Community Proposed Data Sets GBM-DSC-MRI-DRO ASCC TNM Consensus Colorectal Liver Metastases QIN-BREAST-02 MyelomaTT3a Low Dose CT Liver Metastases Lung Fused-CT-Pathology HNSCC Oropharyngeal Radiomics OPC-Radiomics Oropharynx Phantoms HNSCC 3D CT RT MSK Pancreatic Cancer Repository Program Data Sets Notes TCGA 2 collection//sites CPTAC 9 cancer types, 14 sites Exceptional Responders 24 of 58 subjects remaining Immunotherapy 2 cancer types PDX mouse Not started NCTN integration RTOG 0617 pilot in process QIN ECOG-ACRIN 10 trials
- 12. 12 QIN Use Cases Collaborative research projects Challenge competitions Data sharing requirements (NIH guidelines, publication guidelines)
- 13. 13 Summary of QIN Use: 15 QIN Data Sets from 11 out of 19 active sites Collection Cancer Type Modalities Subjects Access Updated QIN Use Case Brain-Tumor-Progression Brain Cancer MR 20 Limited 2018/01/31 Collaborative project QIN LUNG CT Non-small Cell Lung Cancer CT 47 Public 2017/07/31 Lung Seg Challenge ACRIN FLT Breast Breast Cancer PET, CT, OT 83 Limited 2017/12/11 TBD Breast-MRI-NACT-Pilot Breast Cancer MR, SEG 64 Public 2016/01/26 BMMR Challenge ISPY1 Breast Cancer MR, SEG 222 Public 2016/08/31 BMMR Challenge Lung Phantom Lung Phantom CT 1 Public 2014/06/19 Lung Seg Challenge QIN-BRAIN-DSC-MRI Low & High Grade Glioma MR 49 Limited 2015/08/28 DSC MRI Challenge QIN-Breast Breast Cancer MR, PT, CT 67 Limited 2015/09/04 General data sharing QIN Breast DCE-MRI Breast Cancer MR, KO 10 Public 2014/07/31 DCE Challenge QIN GBM DCE-MRI Glioblastoma Multiforme MR 10 Limited 2015/08/14 TBD QIN GBM Treatment Response Glioblastoma Multiforme MR 54 Limited 2015/08/12 TBD QIN-HeadNeck Head and Neck Carcinomas PT, CT, SR, SEG, RWV 156 Public 2014/08/26 Publications, ITCR use, FDG PET Seg Challenge QIN PET Phantom PET Phantom PT 2 Public 2014/09/04 FDG PET Seg Challenge QIN Prostate Prostate Cancer MR 22 Limited 2014/07/02 Collaborative project, AIF challenge QIN-SARCOMA Sarcomas MR 15 Limited 2014/09/05 AIF Challenge
- 14. 14 Suggestions for the coming year Re-use of existing data has been limited Capabilities to archive results data underutilized thus far (limited as they may be) More lead time required due to increased demand Continued efforts to share diverse/rich data sets are critical While painful compared to “quick and dirty” solutions, standardization is worthwhile when planning CCP’s and data storage
- 15. 15 Coming soon… Challenge competitions Software Updates New QIN CCP’s More data
- 16. Advancing TCGA-GBM and TCGA-LGG with expert labels & radiomic features Rich datasets without segmentation labels • Essential for quantitative studies. • Enabling radiogenomic analyses. www.braintumorsegmentation.org Highly competitive challenge utilizing TCIA data ranked 1st at BraTS’15 Bakas et al., “GLISTRboost”, Springer LNCS 2016 Automated method Combining Biophysical Tumor Growth Modeling with Machine Learning for Glioma Segmentation Input: TCIA data Segmentations: using GLISTRboost Manual segmentations approved by expert neuro- radiologist Publicly available contribution towards repeatable and reproducible studies, by: - enabling direct utilization of the TCGA/TCIA glioma collections - allowing to fully exploit their potential in clinical and computational studies 1. Panel of >500 imaging features, extracted from the manual segmentations2.
- 17. 17 New Data Portal interface
- 19. 19 Clinical trials data: ECOG-ACRIN / QIN 2 completed trials • 6657/ISPY1 (public) • 6688/FLT-Breast (restricted to QIN use until 12/18/18). 2 trials in progress • 6684/brain FMISO expected to be completed next month • 6668/NSCLC FDG-PET has been transferred from ACRIN, now starting curation at TCIA How can QIN best leverage this data in clinically meaningful CCP’s? • Schedule presentations about these data to Executive Committee or Working Groups as they come online? • Identify logical follow up questions to ask based on trial publications? • Sequester portions of trial data for validation sets? • Alter the priority list as new needs arise?
- 20. 20 Clinical trials data: NCTN Data Archive Integration Pilot project underway to connect RTOG 0617 data in TCIA to NCTN Data Archive clinical data
- 21. 21 Clinical Proteomics Tumor Analysis Consortium (CPTAC) Collection Cancer Type Modalities Subjects Location Updated CPTAC-CCRCC Clear Cell Carcinoma CT 18 Kidney 2018/04/26 CPTAC-GBM Glioblastoma Multiforme CT, MR 24 Brain 2018/04/26 CPTAC-LSCC Squamous Cell Carcinoma CT,CR, DX 3 Lung 2018/04/26 CPTAC-LUAD Adenocarcinoma CT, MR, PT 10 Lung 2018/04/26 CPTAC-PDA Ductal Adenocarcinoma CT, MR, DX, CR 43 Pancreas 2018/04/26 CPTAC-UCEC Corpus Endometrial Carcinoma CT,MR,PT 31 Uterus 2018/04/26 CPTAC-HNSCC Head and Neck Cancer CT 5 Head-Neck 2018/04/26 CPTAC-CM Cutaneous Melanoma MR 1 Brain 2018/02/13 Precision medicine data • Proteomics • Radiology • Pathology • Clinical • Genomics Similar to TCGA, but prospective • Newer scanners • Same variability in acquisitions
- 22. 22 Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) network 6. Sequencing 7. Proteomics
- 23. 23 Pilot accomplishments with VA and DOD Boston VA MAVERIC program • Now have VA-approved SOPs for image transfer • Proof of principle: 36 imaging studies on 7 patients hosted • Genomic data submitted to GDC Walter Reed GYN-COE Imaging for APOLLO 2 • SOPs developed for images from three sites • 250 studies on 90 patients transferred, not yet public • Facilitating image feature extraction analysis by experts
- 24. APOLLO-5 Year 1 Projected Cases Multiple Cancer Types and Imaging Modalities Cancer Type Low Estimate High Estimate GYN 300 400 Breast 100 200 Prostate 50 100 Colon/GI 50 100 ENT/Thyroid 50 100 Kidney 25 50 Lung 25 50 Brain 10 20 Sarcomas 10 20 Lymphoid 10 20 TOTAL 630 1060 • Imaging will be from multiple imaging modalities • Most cases will have multiple image sets & time points
- 25. 25 Crowds Cure Cancer – BIDS WG pipeline project Preliminary pipeline stages • Ingest tumor location seed points from TCGA subjects • Generate 3d segmentations • Compute radiomic features • Predict outcomes Access the data • Visit our Analysis Results page • Direct link: https://doi.org/10.7937/K9/TCIA.201 8.OW73VLO2
- 26. 26 PET-CT Image Feature Standardization Extending/improving IBSI guidelines Utilizing TCIA data such as CC-Radiomics-Phantom
- 27. 27 Stay tuned!
- 28. 28 Acknowledgements Funding: NCI Cancer Imaging Program Frederick National Laboratory for Cancer Research • John Freymann, Justin Kirby, Brenda Fevrier-Sullivan, Luis Cordeiro, Craig Hill Consultant - Carl Jaffe University of Arkansas Medical School • Fred Prior, Kirk Smith, Lawrence Tarbox, Bill Bennett, Tracy Nolan, Dwayne Dobbins Emory University • Ashish Sharma
Editor's Notes
- #8: Provide DOIs to collections and meta-collections (article’s analysis) Publication can refer to the specific data sets used via the DOIs in the data citations Currently working with NLM, collaborating with Nature Scientific Data and other publications
- #24: The VA team through its MAVERIC (Massachusetts Veterans Epidemiology Research and Information Center) and RePOP projects have worked extensively to develop an internal SOP to collect and centralize the imaging data of patients participating in APOLLO, pulling imaging data on request from its 21 regional service network facilities, or VISNs. The Gyno. Center of Excellence (GYN-COE) developed its imaging SOP in the context of supporting the APOLLO 2 project; as such the emphasis was on collecting the data from multiple sites, configuring and testing the data de-identification and submission systems, and collaborating with TCIA on the quality control steps, staging the images for feature extraction and returning quantitative imaging measures back to the APOLLO data systems. This is ongoing with the feature extractions, the imaging data are all in TCIA
- #25: Murtha Cancer Center Biobank (MCCB) Sites: WRNMMC, Ft. Bragg, Portsmouth, Keesler, San Diego, Madigan, Fort Belvoir, San Antonio, William Beaumont El Paso, Anne Arundel Medical Center estimated to contribute 200-300 cancers of all types / year VA Palo Alto initially estimated to contribute 50 lung and GI cancer cases / year Clinical Breast Care Project (CBCP) estimated to provide 100-200 breast cancers / year Center for Prostate Disease Research (CPDR) estimated to contribute 50-100 prostate cancers / year University of Virginia (UVA) estimated to provide 25-50 lung cancer cases / year Gynecologic Cancer Center of Excellence (GYN-COE) Tissue and Data Acquisition Network (TDAN) Sites: Inova, Duke, Roswell Park with negotiations underway for the Universities of Hawaii and Oklahoma estimated to contribute 300-400 GYN cancers /year Priorities: 1. Active Duty 2. Minorities 3. High priority aggressive cancers 4. High priority events (e.g. metastasis, recurrence, resistance)