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.
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|
|Lung, Head-Neck||Lung, Head-Neck||701||Various (5 collections)||Tumor segmentations and radiomic imaging features||2020-03-23|
|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|
|Renal Clear Cell Carcinoma||Kidney||103||TCGA-KIRC||Radiologist assessments of imaging features||2015-05-28|
Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset
|Prostate||Prostate||80||Prostate structure segmentations||2015-08-20|
|Head and Neck Squamous Cell Carcinoma||Head-Neck||215||HNSCC||Radiation Therapy Structures||2019-07-11|
|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|
|Lung||Chest||31||Lung Phantom, LIDC-IDRI, QIN LUNG CT, RIDER Lung CT||Tumor segmentations||2018-12-18|
|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|
|Lung||Chest||1,010||LIDC-IDRI||Tumor segmentations, image features||2020-03-26|
|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|
|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|
|Glioblastoma||Brain||135||TCGA-GBM||Tumor segmentations and radiomic imaging features||2017-07-17|
|Low Grade Glioma||Brain||108||TCGA-LGG||Tumor segmentations and radiomic imaging features||2017-07-17|
|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|
|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|