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PROSTATE-MRI | PROSTATE-MRI
DOI: 10.7937/K9/TCIA.2016.6046GUDv | Data Citation Required | Image Collection
| Location | Species | Subjects | Data Types | Cancer Types | Size | Status | Updated |
|---|---|---|---|---|---|---|---|
| Prostate | Human | 26 | MR, Histopathology | Prostate Cancer | Public, Complete | 2011/06/30 |
Summary
This collection of prostate Magnetic Resonance Images (MRIs) was obtained with an endorectal and phased array surface coil at 3T (Philips Achieva). Each patient had biopsy confirmation of cancer and underwent a robotic-assisted radical prostatectomy. A mold was generated from each MRI, and the prostatectomy specimen was first placed in the mold, then cut in the same plane as the MRI. The data was generated at the National Cancer Institute, Bethesda, Maryland, USA between 2008-2010. For scientific or other inquiries relating to this data set, please contact TCIA's Helpdesk.
Data Access
Version 1: Updated 2011/06/30
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Images | MR | DICOM | Download requires NBIA Data Retriever |
26 | 26 | 182 | 22,036 | CC BY 3.0 |
| Histopathology Images | Histopathology | JPG | Download requires IBM-Aspera-Connect plugin |
26 | 26 | 26 | 26 | CC BY 3.0 |
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
|
|
Choyke P, Turkbey B, Pinto P, Merino M, Wood B. (2016). Data From PROSTATE-MRI. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2016.6046GUDv |
Detailed Description
Note from the investigators: The DICOM elements for these values may no longer exist within the files themselves but: the b values are 0, 188, 375, 563, 750 for the diffusion weighted MRI of that dataset.
Update May 2018: The download of these data is no longer Limited to users with specific permission from the PIs of the Collection.
Related Publications
Publications by the Dataset Authors
The authors recommended this paper as the best source of additional information about this dataset:
No publications by dataset authors were found.
Research Community Publications
TCIA maintains a list of publications that leverage TCIA data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk. Below is a list of such publications using this Collection:
- Mayer, R., Simone II, C. B., Turkbey, B., & Choyke, P. (2021). Correlation of prostate tumor eccentricity and Gleason scoring from prostatectomy and multi-parametric-magnetic resonance imaging. In Quantitative Imaging in Medicine and Surgery (Vol. 11, Issue 10, pp. 4235–4244). AME Publishing Company. https://doi.org/10.21037/qims-21-24
- Mayer, R., Simone II, C. B., Turkbey, B., & Choyke, P. (2021). Algorithms applied to spatially registered multi-parametric MRI for prostate tumor volume measurement. In Quantitative Imaging in Medicine and Surgery (Vol. 11, Issue 1, pp. 119–132). AME Publishing Company. https://doi.org/10.21037/qims-20-137a
- Mayer, R., Simone, C. B., II, Skinner, W., Turkbey, B., & Choykey, P. (2018). Pilot study for supervised target detection applied to spatially registered multiparametric MRI in order to non-invasively score prostate cancer. In Computers in Biology and Medicine (Vol. 94, pp. 65–73). Elsevier BV. https://doi.org/10.1016/j.compbiomed.2018.01.003
- Ciga, O., Xu, T., & Martel, A. L. (2022). Self supervised contrastive learning for digital histopathology. Machine Learning with Applications, 7. doi:10.1016/j.mlwa.2021.100198
- Du, R., & Vardhanabhuti, V. (2020, 06-08 July 2020). 3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks. Paper presented at the Third Conference on Medical Imaging with Deep Learning (MIDL 2020), Montréal, QC, Canada.
- Elkhader, J. A. (2022). An Integrative Approach to Drug Development Using Machine Learning. (Ph. D. Dissertation). Weill Medical College of Cornell University ProQuest Dissertations Publishing, Available from TCIA 10.7937/k9tcia.2017.murs5cl ; 10.7937/K9/TCIA.2016.6046GUDV database. (29390845)
- Namakshenas, P., & Mojra, A. (2021). Optimization of polyethylene glycol-based hydrogel rectal spacer for focal laser ablation of prostate peripheral zone tumor. Physica Medica, 89, 104-113. doi:10.1016/j.ejmp.2021.07.034