To enhance the value of TCIA collections for future research we encourage the research community to publish analysis datasets to augment our primary datasets. Potential data types of interest include analysis results such as tumor segmentations, radiomics features, derived/reprocessed images, and radiologist assessments.
Submitting a request to publish analysis results
In order to publish analysis results you must first identify the subset of TCIA data that you analyzed. This is done by creating a shared list using TCIA’s Data Portal (see “Creating a Shared List“). Once a shared list is created you can send a request to publish your dataset to TCIA’s help desk providing the following information:
- Shared List Name – The name of the TCIA shared list that identifies the data you have analyzed. (required – See Creating a Shared List for assistance)
- Title – The title of your dataset.
- Authors – The names of the authors who helped generate the dataset in the order you would like them to appear in the citation.
- Abstract – A brief abstract of the data. It should include how you selected the image data, how any analyses 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 process your request your dataset will be published in our Analysis Results directory and will be assigned a unique/persistent digital object identifier (DOI). This DOI can be used to cite your dataset and also provides a web link to easily direct people to your data.
Analysis Results Directory
An alphabetical listing of published results data sets based upon TCIA-hosted data:
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
- Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death
- Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features
- Image Data Used in the Simulations of "The Role of Image Compression Standards in Medical Imaging: Current Status and Future Trends"
- MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
- NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures
- Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor
- QIN multi-site collection of Lung CT data with Nodule Segmentations
- Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
- Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group
- 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
- Spatial Habitat Features derived from Multiparametric Magnetic Resonance Imaging data from Glioblastoma Multiforme cases
- TCGA Breast Phentotype Research Group Data sets