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title | Data Access |
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| Data AccessChoosing the Download option will provide you with a file to launch the TCIA Download Manager to download the entire collection. If you want to browse or filter the data to select only specific scans/studies please use the Search By Collection option. Data Type | Download all or Query/Filter |
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Images (DICOM, | 175GB3GB) | | Tissue Slide Images (web) | | Clinical Data (TXT) | | Biomedical Data (TXT) | | Genomics (web) | |
Click the Versions tab for more info about data releases. Third Party Analyses of this DatasetTCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: |
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title | Detailed Description |
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| Detailed Description | |
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Modalities | CT, PT, NM | Number of | PatientsParticipants | 69 | Number of Studies | 152 | Number of Series | 624 | Number of Images | 48,931 | Images Size (GB) | 18.3 |
GDC Data Portal - Clinical and Genomic DataThe GDC Data Portal has extensive clinical and genomic data, which can be matched to the patient identifiers on the images here in TCIA. Below is a snapshot of clinical data extracted on 1/5/2016. Explanations of the clinical data can be found on the Biospecimen Core Resource Clinical Data Forms linked below: A Note about TCIA and TCGA Subject Identifiers and Dates NOTE: On 12/3/12 TCIA staff were alerted to the fact that the middle 2 digits in the patient identifiers of images were not consistent with those on the TCGA Data Portal (e.g. patient TCGA-93-Z011 should have been TCGA-17-Z011). This was corrected and the data was re-posted on 12/5/12 so that all subjects now have 17 digits as intended. Subject Identifiers: a subject with radiology images stored in TCIA is identified with a Patient ID that is identical to the Patient ID of the same subject with demographic, clinical, pathological, and/or genomic data stored in TCGA. For each TCGA case, the baseline TCGA imaging studies found on TCIA are pre-surgical. Dates: TCIA and TCGA handle dates differently, and there are no immediate plans to reconcile: - TCIA Dates: dates (be they birth dates, imaging study dates, etc.) in the Digital Imaging and Communications in Medicine (DICOM) headers of TCIA radiology images have been offset by a random number of days. The offset is a number of days between 3 and 10 years prior to the real date that is consistent for each TCIA image-submitting site and collection, but that varies among sites and among collections from the same site. Thus, the number of days between a subject’s longitudinal imaging studies are accurately preserved when more than one study has been archived while still meeting HIPAA requirements.
- TCGA Dates: the patient demographic and clinical event dates are all the number of days from the index date, which is the actual date of pathologic diagnosis. So all the dates in the data are relative negative or positive integers, except for the “days_to_pathologic_diagnosis” value, which is 0 – the index date. The years of birth and diagnosis are maintained in the distributed clinical data file. The NCI retains a copy of the data with complete dates, but those data are not made available.With regard to other TCGA dates, if a date comes from a HIPAA “covered entity’s” medical record, it is turned into the relative day count from the index date. Dates like the date TCGA received the specimen or when the TCGA case report form was filled out are not such covered dates, and they will appear as real dates (month, day, and year).
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy TCGA collections have special publication embargoes which must be followed in addition to our normal data usage policies. See the TCGA section within TCIA's Data Usage Policies and Restrictions for additional details. After the publication embargo period ends these collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work: Public collection license |
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| "The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/." |
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| Albertina, B., Watson, M., Holback, C., Jarosz, R., Kirk, S., Lee, Y., … Lemmerman, J. (2016). Radiology Data from The Cancer Genome Atlas Lung Adenocarcinoma [TCGA-LUAD] collection. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.JGNIHEP5 |
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| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper) |
Other Publications Using This DataTCIA maintains a list of publications which leverage our data. - Choi, Hongyoon and Kwon Joong Na. "Integrative Analysis of Imaging and Transcriptomic Data of the Immune Landscape Associated with Tumor Metabolism in Lung Adenocarcinoma: Clinical and Prognostic Implications." THERANOSTICS, vol. 8, no. 7, 2018, pp. 1956-1965, doi:10.7150/thno.23767.
- Dara, S et al. "Feature Extraction in Medical Images by Using Deep Learning Approach." International Journal of Pure and Applied Mathematics, vol. 120, no. 6, 2018, pp. 305-312, https://acadpubl.eu/hub/2018-120-6/1/20.pdf
- Livieris, Ioannis et al. "Detecting Lung Abnormalities from X-Rays Using an Improved Ssl Algorithm." Electronic Notes in Theoretical Computer Science, vol. 343, 2019, pp. 19-33, doi:10.1016/j.entcs.2019.04.008.
- Livieris, Ioannis et al. "A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays." Algorithms, vol. 12, no. 3, 2019, p. 64, doi:10.3390/a12030064.
- Matsuyama, Eri and Du-Yih Tsai. "Automated Classification of Lung Diseases in Computed Tomography Images Using a Wavelet Based Convolutional Neural Network." Journal of Biomedical Science and Engineering, vol. 11, no. 10, 2018, p. 263, doi:10.4236/jbise.2018.1110022.
- Meldo, Anna et al. "Database Acquisition for the Lung Cancer Computer Aided Diagnostic Systems." 25th conference of FRUCT(Finnish-Russian University Cooperation in Telecommunications) Association, Nov 5-8 2019 2019. https://fruct.org/publications/fruct25/files/Mel.pdf.
- Pathak, Yadunath et al. "An Efficient Low-Dose Ct Reconstruction Technique Using Partial Derivatives Based Guided Image Filter." Multimedia Tools and Applications, 2018, pp. 1-20, doi:10.1007/s11042-018-6840-5.
- Singh, Apurva et al. "A Novel Imaging-Genomic Approach to Predict Outcomes of Radiation Therapy." Department of Electrical and Computer Engineering, vol. MS, George Washington University, 2019. general editor, Murray Loew.
- Toğaçar, Mesut et al. "Detection of Lung Cancer on Chest Ct Images Using Minimum Redundancy Maximum Relevance Feature Selection Method with Convolutional Neural Networks." Biocybernetics and Biomedical Engineering, 2019, doi:10.1016/j.bbe.2019.11.004.
- Wong, Jordan et al. "Comparing Deep Learning-Based Auto-Segmentation of Organs at Risk and Clinical Target Volumes to Expert Inter-Observer Variability in Radiotherapy Planning." Radiother Oncol, vol. 144, 2019, pp. 152-158, doi:10.1016/j.radonc.2019.10.019.
- Yu, Zexi. "Co-Segmentation Methods for Improving Tumor Target Delineation in Pet-Ct Images." Electrical and Computer Engineering, vol. Master of Science (M.Sc.), University of Saskatchewan, 2016, p. 119. general editor, Francis; Babyn Bui, Paul, (Link to Thesis)
. At this time we are not aware of any manuscripts based on this data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
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| Version 2 (Current)4 (Current): Updated 2020/05/29 Data Type | Download all or Query/Filter |
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Images (DICOM, 18.3GB) | | Tissue Slide Images (web) | | Clinical Data (TXT) | | Biomedical Data (TXT) | | Genomics (web) | | Updated clinical data link with latest spreadsheets from GDC. Added new biomedical spreadsheets from GDC. Version 3: Updated 2017/01/30 Data Type | Download all or Query/Filter |
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Images (DICOM, 18.3GB) | | Clinical Data (TXT) | | Genomics (web) | | Images for 5 new subjects added. Version 2: Updated 2016/01/05 Data Type | Download all or Query/Filter |
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Images (DICOM, 9.4GB) | | Clinical Data (TXT) | | Genomics (web) | | Extracted latest release of clinical data (TXT) from the GDC Data Portal. Version 1: Updated 2014/03/28 Data Type | Download all or Query/Filter |
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Images (DICOM, 9.4GB) | | Clinical Data (TXT) | | Genomics (web) | | |
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