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.
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.
Analysis Results Directory
A listing of published analysis results data sets based upon TCIA-hosted data (sorted from newest to oldest):
- Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images
- Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting
- SDTM datasets of clinical data and measurements for selected cancer collections to TCIA
- Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images
- Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection
- Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection
- ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection
- Long and Short Survival in Adenocarcinoma Lung CTs
- Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group
- Data from Head and Neck Cancer CT Atlas
- DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets
- Standardized representation of the TCIA LIDC-IDRI annotations using DICOM
- TCGA Breast Phenotype Research Group Data sets
- QIN multi-site collection of Lung CT data with Nodule Segmentations
- Image Data Used in the Simulations of "The Role of Image Compression Standards in Medical Imaging: Current Status and Future Trends"
- Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting
- NSCLC Radiogenomics: Initial Stanford Study of 26 Cases
- NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures
- Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases
- Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features
- 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
- Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group
- Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
- Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
- MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
- Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach