The Cancer Imaging Archive (TCIA) Public Access

Space shortcuts

Child pages
  • Fused Radiology-Pathology Prostate Dataset (Prostate Fused-MRI-Pathology)

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&Estained) images of corresponding radical prostatectomy specimens. The MRI scans also have a mapping of extent of prostate cancer on them [1]. 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 [2]. 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 [3] and checked for accuracy by an expert pathologist and radiologist. Deformable co-registration [4] was employed to spatially co-registered the corresponding radiologic and histopathologic tissue sections to map disease extent onto the corresponding MRI scans.

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.

References

  1. 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).
  2. 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).
  3. 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).
  4. 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).


Data Access 

Data TypeDownload all or Query/FilterLicense
Images (DICOM, 4.4 GB)

      

(Download requires the NBIA Data Retriever)

CC BY 3.0

Annotated Whole Slide Pathology Images & Annotations (Tiff, XML 76.8 GB)

       

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC BY 3.0

Fused Rad-Path Matlab Files

 

CC BY 3.0


Click the Versions tab for more info about data releases.

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.

Detailed Description

Collection Statistics

Radiology Image Statistics

Pathology Image Statistics

Modalities

MRI

Pathology, Matlab

Number of Participants

28

16

Number of Studies

29

N/A

Number of Series

325N/A

Number of Images

32,508

114
Image Size (GB)4.476.8

Supporting Documentation

The data set is fully described in the following publications:

 

  1. 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).
  2. 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).
  3. 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).
  4. 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).


Citations & Data Usage Policy 

Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:

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

Publication Citation

  1. 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).
  2. 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
  3. 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
  4. 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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: https://doi.org/10.1007/s10278-013-9622-7

Other Publications Using This Data

TCIA maintains a list of publications that leverage our data. If you have a publication you'd like to add, please contact the TCIA Helpdesk.

  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. 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 


Version 1 (Current) Updated 11-30-2016

Data TypeDownload all or Query/Filter
Images (DICOM, 4.4 GB)

      

(Download requires the NBIA Data Retriever)

Annotated Whole Slide Pathology Images & Annotations (Tiff, XML 76.8 GB)

           

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package)             

Fused Rad-Path MATLAB Files

      




  • No labels