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PROSTATE-FUSED-MRI-PATHOLOGY

Prostate Fused-MRI-Pathology | Fused Radiology-Pathology Prostate Dataset

DOI: 10.7937/k9/TCIA.2016.tlpmr1am | Data Citation Required | Image Collection

Location Species Subjects Data Types Cancer Types Size Supporting Data Status Updated
Prostate Human 28 MR, Histopathology Prostate Cancer 81.54GB Image Analyses Public, Complete 2023/04/10

Summary

This collection comprises a total of 28 3 Tesla T1-weighted, T2-weighted, Diffusion weighted and Dynamic Contrast Enhanced prostate MRI along with accompanying digitized histopathology (H&E stained) images of corresponding radical prostatectomy specimens. The MRI scans also have a mapping of extent of prostate cancer on them [10.1002/jmri.24975]. Each surgically excised prostate specimen was originally sectioned and quartered resulting in 4 slides for each section. Each of these individual slides was digitized at 20x magnification using an Aperio slide scanner resulting in a set of 4 .svs images. Each of the 4 .svs images were then digitally stitched together to constitute a pseudo-whole mount section (.tiff) using the program in [PMCID: PMC4023035]. Annotations of cancer presence on the pseudo-whole mount sections were made by an expert pathologist. Slice correspondences were established between the individual T2w MRI and stitched pseudo-whole mount sections by the program in [10.1016/j.compmedimag.2010.12.003] and checked for accuracy by an expert pathologist and radiologist. Deformable co-registration [PMC3078156] was employed to spatially co-registered the corresponding radiologic and histopathologic tissue sections to map disease extent onto the corresponding MRI scans.

Data Access

Version 2: Updated 2023/04/10

Added a correspondence xlsx between MR and Pathology slides, imaging data are unchanged.

Title Data Type Format Access Points Subjects Studies Series Images License
Images MR DICOM
Download requires NBIA Data Retriever
 28 28 324 32,508 CC BY 3.0
Annotated Whole Slide Pathology Images & Annotations Histopathology XML and TIFF
Download requires IBM-Aspera-Connect plugin
 16 114 CC BY 3.0
Fused Rad-Path Matlab Files ZIP, MHA, and MATLAB 15 CC BY 3.0
Correspondence tables XLSX 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.

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

Madabhushi, A., & Feldman, M. (2016). Fused Radiology-Pathology Prostate Dataset (Prostate Fused-MRI-Pathology) . The Cancer Imaging Archive. doi; 10.7937/k9/TCIA.2016.tlpmr1am

Acknowledgements

  • Data collection and analysis was provided by Anant Madabhushi, PhD, Case Western Reserve University and Michael D. Feldman, MD, PhD, Hospital at the University of Pennsylvania. 
  • This work was supported by NIH R01CA136535.

Other Publications Using this Data

TCIA maintains a list of publications which leverage our data. If you have a publication you’d like to add, please contact TCIA’s Helpdesk.

The below were found to have cited this dataset:

  1. Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020). Formal methods for prostate cancer gleason score and treatment prediction using radiomic biomarkers. Magnetic resonance imaging, 66, 165-175. doi:https://doi.org/10.1016/j.mri.2019.08.030
  2. Chatzoudis, P. (2018). MRI prostate cancer radiomics: Assessment of effectiveness and perspectives. (Master of Biomedical Engineering). Delft University of Technology, Delft, Netherlands. Retrieved from http://resolver.tudelft.nl/uuid:b8459bdb-1761-4f17-8807-e3b1cf7da629
  3. Duran, A., Dussert, G., Rouviere, O., Jaouen, T., Jodoin, P. M., & Lartizien, C. (2022). ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans. Medical image analysis, 77, 102347. doi:https://doi.org/10.1016/j.media.2021.102347

The authors recommend that the below publications fully describe the data:

Publication Citation

Singanamalli, A. , Rusu, M. , Sparks, R. E., Shih, N. N., Ziober, A. , Wang, L. , Tomaszewski, J. , Rosen, M. , Feldman, M. and Madabhushi, A. (2016), Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer. J. Magn. Reson. Imaging, 43: 149-158. doi: 10.1002/jmri.24975 (PMID:26110513).

Publication Citation

Toth, R, Feldman, M, Yu, D, Tomaszewski, J, Madabhushi, A, “Histostitcher™: An Informatics Software Platform for Reconstructing Whole-Mount Prostate Histology using the Extensible Imaging Platform (XIP™) Framework,” Journal of Pathology Informatics, vol. 5, pg. 8, 2014 (PMID: 24843820, PMCID: PMC4023035). https://doi.org/10.4103/2153-3539.129441

Publication Citation

Xiao, G, Bloch, N, Chappelow, J, Genega, E, Rofsky, N, Lenkinsky, R, Tomaszewski, J, Feldman, M, Rosen, M, Madabhushi, A, “Determining Histology-MRI Slice Correspondences for Defining MRI-based Disease Signatures of Prostate Cancer,” Special Issue of Computerized Medical Imaging and Graphics on Whole Slide Microscopic Image Processing, vol. 35[7-8], pp. 568-78, 2011 (PMID: 21255974). https://doi.org/10.1016/j.compmedimag.2010.12.003

Publication Citation

Chappelow, J, Bloch, N., Rofsky, N, Genega, E, Lenkinski, R, DeWolf, W, Madabhushi, A,   “Elastic Registration of Multimodal Prostate MRI and Histology via Multi-Attribute Combined Mutual Information,” Medical Physics, vol. 38[4], pp. 2005-2018, 2011 (PMID: 21626933). https://doi.org/10.1118/1.3560879

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Previous Versions

Version 1: Updated 2016/11/30

Title Data Type Format Access Points Studies Series Images License
Images DICOM
Annotated Whole Slide Pathology Images & Annotations XML and TIFF
Fused Rad-Path MATLAB Files