To enhance the value of TCIA collections for future research we encourage the research community to publish analysis datasets to augment existing TCIA image collections. Potential data types of interest include analysis results such as 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.
If your analysis results include voxel-based segmentations, parametric maps (e.g., maps of DCE or DWI MRI model parameter fits), or measurements derived from the segmented regions (e.g., radiomics features), we strongly encourage you to consider using the dcmqi library to convert your dataset into standard DICOM representation. We will work with you as part of the submission process to help you use this and other tools to prepare your submission in a format suitable for archival and reuse.
Submitting a request to publish analysis results
Requests to share analysis results on TCIA can be submitted by filling out this application form. Proposals will be reviewed on a monthly basis by the TCIA Advisory Group. If accepted, your proposal will be prioritized and assigned to one of our curation teams who will assist you through the submission process. Your data will be published with a citation and corresponding digital object identifier (DOI) which can be cited in publications. To help other users find your dataset on TCIA entries will be added on the Collection pages of any TCIA dataset your analyses utilized, and also to our Analysis Results directory below.
Note: If your analysis results include voxel-based segmentations, parametric maps (e.g., maps of DCE or DWI MRI model parameter fits), or measurements derived from the segmented regions (e.g., radiomics features), we strongly encourage you to consider using the dcmqi library to convert your dataset into standard DICOM representation. We will work with you as part of the submission process to help you use this and other tools to prepare your submission in a format suitable for archival and reuse.
Access Analysis Results Data
Note: Column headers can be clicked to sort the table.
| Title | Cancer Type | Location | Subjects | Collections Analyzed | Analysis Artifacts on TCIA | Updated |
|---|---|---|---|---|---|---|
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach | Lung, Head-Neck | Lung, Head-Neck | 701 | Various (5 collections) | Tumor segmentations and radiomic imaging features | 2020-03-23 |
Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death | Glioblastoma | Brain | 99 | TCGA-GBM | N/A | 2016-08-02 |
| Glioblastoma | Brain | 75 | TCGA-GBM | Radiologist assessments of imaging features | 2014-11-12 | |
| Glioblastoma | Brain | 45 | TCGA-GBM | Radiologist assessments of imaging features and hemodynamic parameters | 2014-07-24 | |
| Breast | Breast | 48 | TCGA-BRCA | N/A | 2014-11-12 | |
| Renal Clear Cell Carcinoma | Kidney | 103 | TCGA-KIRC | Radiologist assessments of imaging features | 2015-05-28 | |
| Lung | Chest | 102 | LIDC-IDRI | N/A | 2015-02-24 | |
Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features | Glioblastoma | Brain | 55 | TCGA-GBM | Tumor segmentations | 2014-11-05 |
| 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 | |
| Lung | Chest | 26 | NSCLC Radiogenomics | N/A | 2013-03-01 | |
| Head and Neck Squamous Cell Carcinoma | Head-Neck | 215 | HNSCC | Radiation Therapy Structures | 2019-07-11 | |
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 |
| Colon, Lung, Breast, Glioblastoma | Colon, Chest, Breast, Brain | 40 | CT COLONOGRAPHY, LIDC-IDRI, TCGA-BRCA, TCGA-GBM | N/A | 2016-12-08 | |
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 |
ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection | Low Grade Glioma | Brain | 188 | TCGA-LGG | Radiologist assessments of imaging features, tumor segmentations | 2017-03-17 |
| Breast | Breast | 84 | TCGA-BRCA | Radiologist assessments of imaging features, lesion segmentations, radiomic features, and multi-gene assays | 2018-09-04 | |
Standardized representation of the TCIA LIDC-IDRI annotations using DICOM | Lung | Chest | 1,010 | LIDC-IDRI | Tumor segmentations, image features | 2020-03-26 |
DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets | Glioblatoma, Low Grade Glioma | Brain | 243 | TCGA-GBM, TCGA-LGG | Tumor segmentations | 2018-11-20 |
| High-Grade Serous Ovarian Cancer | Ovary | 93 | TCGA-OV | Radiologist assessments of imaging features, genomic subtypes | 2016-08-02 | |
| Lung Adenocarcinoma | Chest | 40 | LungCT-Diagnosis, QIN LUNG CT | Tumor segmentations and radiomic imaging features | 2017-08-11 | |
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 |
Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection | Glioblastoma | Brain | 135 | TCGA-GBM | Tumor segmentations and radiomic imaging 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 imaging features | 2017-07-17 |
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 |
| 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 |
Analysis Results Directory
A listing of published analysis results data sets based upon TCIA-hosted data (sorted from newest to oldest):