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PROSTATE-MRI

The Cancer Imaging Archive

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 3.6GB 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 Studies Series Images 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
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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.

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

Other Publications Using this Data

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