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To enhance the value of TCIA collections we encourage the research community to publish their analyses of existing TCIA image collections. Examples of this kind of data includes radiologist or pathologist annotations, image classifications, segmentations, radiomics features, or derived/reprocessed images. Similar to submitting new image collections, these data are reviewed by the TCIA Advisory Group for relevance and curated using our normal processes to assure data are de-identified. However, TCIA does not certify the quality of the analyses themselves (e.g. accuracy of segmentation on a given scan). Researchers should always carefully review the data and any related publications before deciding whether these analyses could be useful in their work.
Note: Column headers can be clicked to sort the table.
Title | Cancer Type | Location | Subjects | Collections Analyzed | Analysis Artifacts on TCIA | Updated |
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DICOM SR of clinical data and measurement for breast cancer collections to TCIA | Breast | Breast | 474 | TCGA-BRCA, BREAST-DIAGNOSIS, ISPY1, Breast-MRI-NACT-Pilot | DICOM SR descriptions of patient characteristics, histopathology, receptor status and clinical findings including measurements. | 2020-05-28 |
DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets | Glioblatoma, Low Grade Glioma | Brain | 167 | TCGA-GBM, TCGA-LGG | Tumor segmentations | 2020-04-30 |
Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis | Ovarian | Ovary | 20 | TCGA-OV | Radiologist assessments of image features, proteogenomic features | 2020-04-15 |
Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines | Lung | Lung | 402 | NSCLC-Radiomics | Thoracic segmentations, pleural effusion segmentations, image features | 2020-04-08 |
Standardized representation of the TCIA LIDC-IDRI annotations using DICOM | Lung | Chest | 1,010 | LIDC-IDRI | Tumor segmentations, image features | 2020-03-26 |
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | Lung, Head-Neck | Lung, Head-Neck | 701 | NSCLC-Radiomics, NSCLC-Radiomics-Genomics, Head-Neck-Radiomics-HN1, NSCLC-Radiomics-Interobserver1, RIDER Lung CT | Tumor segmentations and radiomic features | 2020-03-23 |
Lung | Chest | 31 | RIDER Lung CT | Tumor segmentations | 2020-02-13 | |
Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images | Various (14 collections) | Various (14 collections) | Various (14 TCGA collections) | Nuclei segmentations | 2020-02-08 | |
Head and Neck Squamous Cell Carcinoma | Head-Neck | 215 | HNSCC | Radiation Therapy Structures | 2019-07-11 | |
SDTM datasets of clinical data and measurements for selected cancer collections to TCIA | Breast, Glioblastoma | Breast, Brain | 516 | ISPY1, BREAST-DIAGNOSIS, Breast-MRI-NACT-Pilot, TCGA-BRCA, Ivy GAP | Standardized (SDTM format) conversions of clinical and image analysis data | 2019-06-21 |
Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting | Various (13 collections) | Various (13 collections) | 324 | Various (13 collections) | Lesion measurements | 2019-05-30 |
QIN multi-site collection of Lung CT data with Nodule Segmentations | Lung | Chest | 31 | Lung Phantom, LIDC-IDRI, QIN LUNG CT, RIDER Lung CT | Tumor segmentations | 2018-12-18 |
Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images | Various (13 collections) | Various (13 collections) | 4,759 | Various (13 TCGA collections) | Deep learning based computational stain for staining tumor-infiltrating lymphocytes (TILs) | 2018-12-17 |
Breast | Breast | 84 | TCGA-BRCA | Radiologist assessments of image features, lesion segmentations, radiomic features, and multi-gene assays | 2018-09-04 | |
Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting | Lung Adenocarcinoma, Renal Clear Cell, Liver, Ovarian | Chest, Kidney, Liver, Ovary | 352 | TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV | Lesion measurements | 2018-05-17 |
Lung Adenocarcinoma | Chest | 40 | LungCT-Diagnosis, QIN LUNG CT | Tumor segmentations and radiomic image features | 2017-08-11 | |
Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection | Glioblastoma | Brain | 135 | TCGA-GBM | Tumor segmentations and radiomic image features | 2017-07-17 |
Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection | Low Grade Glioma | Brain | 108 | TCGA-LGG | Tumor segmentations and radiomic image features | 2017-07-17 |
ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection | Low Grade Glioma | Brain | 188 | TCGA-LGG | Radiologist assessments of image features, tumor segmentations | 2017-03-17 |
Colon, Lung, Breast, Glioblastoma | Colon, Chest, Breast, Brain | 40 | CT COLONOGRAPHY, LIDC-IDRI, TCGA-BRCA, TCGA-GBM | N/A | 2016-12-08 | |
Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death | Glioblastoma | Brain | 99 | TCGA-GBM | N/A | 2016-08-02 |
High-Grade Serous Ovarian Cancer | Ovary | 93 | TCGA-OV | Radiologist assessments of image features, genomic subtypes | 2016-08-02 | |
Glioblastoma | Brain | 74 | TCGA-GBM | N/A | 2015-08-20 | |
NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures | Prostate | Prostate | 80 | Prostate structure segmentations | 2015-08-20 | |
Renal Clear Cell Carcinoma | Kidney | 103 | TCGA-KIRC | Radiologist assessments of image features | 2015-05-28 | |
Lung | Chest | 102 | LIDC-IDRI | N/A | 2015-02-24 | |
Glioblastoma | Brain | 75 | TCGA-GBM | Radiologist assessments of image features | 2014-11-12 | |
Breast | Breast | 48 | TCGA-BRCA | N/A | 2014-11-12 | |
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features | Glioblastoma | Brain | 55 | TCGA-GBM | Tumor segmentations | 2014-11-05 |
Glioblastoma | Brain | 45 | TCGA-GBM | Radiologist assessments of image features and hemodynamic parameters | 2014-07-24 | |
Lung | Chest | 26 | NSCLC Radiogenomics | N/A | 2013-03-01 |
Persistent Identifiers to Subsets of TCIA Data
TCIA DOIs are references to data within the TCIA repository, and are not references to specific publications. Specific publications may refer to TCIA DOIs to identify the data used in doing research, but a TCIA DOI is not the DOI for that publication. In fact many publications might reference a particular TCIA DOI.
To facilitate data sharing, many publications encourage authors to include data citations to the data that the authors used in creating the results described in their scholarly papers. In addition, new journals are now available for describing data collections outright (e.g., Nature Scientific Data). As a service to the community, TCIA now has the ability to create persistent identifiers linked to subsets of data held within TCIA that authors may use as data citations in their scholarly papers.
TCIA uses the DataCite system to manage these references. DataCite leverages the Digital Object Identifier (DOI) infrastructure, which is widely used in citing scholarly articles. TCIA users may request that a DOI be created for subsets of data stored within TCIA. A DOI request related to data that is NOT managed by TCIA will NOT be granted. All information related to a TCIA DOI must be persistently managed by TCIA.
To request a DOI for a subset of data, a registered TCIA user must first identify the subset of data that will be referenced by the DOI. The best way for a user to identify this subset of data is to create a shared list using TCIA's web application (see "Creating a Shared List"). By definition, only publicly available data may be included in the shared list. Creating a DOI to private data is not permitted. Currently DOIs created by TCIA may only reference static (unchanging) subsets of data. In other words, if someone changes the content of the shared list, this will not be reflected in data returned by existing DOIs created from that shared list.
Once a user has created a shared list, they will send a request for the creation of a DOI to TCIA's help desk providing the following information:
- Requestor - The name and e-mail address of the person with whom TCIA staff will work to create the DOI (defaults to the user submitting the request to the help desk).
- Shared List Name - The name of the TCIA shared list that identifies the publicly available data that will be the subject of the DOI. (required - See Creating a Shared List for assistance)
- Title - A name of your choosing that will be assigned to the TCIA data referenced by the DOI (similar to the title of a paper) (defaults to the shared list name).
- Authors - The names of the authors who helped generate the data set. This may be people who helped collect or curate the image data or who helped generate any analyses that is being shared with your DOI.
- Abstract - A brief abstract of the data subset (required). The abstract should be something about how you selected the image data, how any analyses that are part of the DOI were generated/collected, and what the potential value of this data is for other TCIA users.
- Special Instructions - Any guidance about the timing of when we publish the DOI (e.g. it should not be listed until a related manuscript is published) or other questions/concerns.
Once we validate your request for a TCIA data DOI, we will create a DOI landing page for the citation (within the TCIA public wiki space) and associate a DOI with that landing page. The landing page will include a link that allows readers to directly download the subset of data cited. The help desk will then inform you (as the requestor) via return e-mail when the new DOI is ready.
DOI Directory
An alphabetical listing of available DOIs created for TCIA-hosted data:
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