Summary
Excerpt |
---|
This collection provides public access to a 3D pathology dataset of prostate cancer, allowing researchers to further investigate various 3D tissue structures and their correlation with prostate cancer patient outcomes (biochemical recurrence). These 3D tissue structures are revealed through: (1) a H&E-analog stain, (2) synthetically generated immunofluorescence staining of CK8 (targeting the luminal epithelial cells of all prostate glands), and (3) 3D segmentation masks of the gland lumen, epithelium, and stromal regions of prostate biopsies. This data collection will promote research in the field of computational 3D pathology for clinical decision support. In this TCIA collection, we provide the raw 3D open-top light-sheet (OTLS) microscope images of prostate biopsies stained with a fluorescent analog of H&E at original resolution (unfused image tiles), the 2x down-sampled fused OTLS-imaged images (H&E-analog staining), the synthetic cytokeratin-8 (CK8) immunofluorescent images at 2x-downsampled resolution, the 3D semantic segmentation masks of glands at 4x down-sampled resolution, the clinical data for patient outcomes (biochemical recurrence), and the coordinates for the cancer-enriched regions of each biopsy. All datasets are from the 50 patient cases studied in this publication: [W. Xie et al., Cancer Research, 2022]. The Python code for the deep-learning models, and for 3D glandular segmentations based on synthetic-CK8 datasets, are available on GitHub at https://github.com/WeisiX/ITAS3D. Note that the 3D pathology datasets provided in this collection were generated in Dr. Jonathan Liu’s lab at the University of Washington with a custom open-top light-sheet (OTLS) microscope developed by the lab [A.K. Glaser et al., Nature Communications, 2019]. There is no clinical metadata within the imaging files and all patients are referred to with coded identifiers. All of the clinical outcomes data provided in this collection have already been published within the supplement of [W. Xie et al., Cancer Research, 2022]. |
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Research Program (PCRP) through W81XWH-18-10358 (J.T.C. Liu, L.D. True and J.C. Vaughan), W81XWH-19-1-0589 (N.P. Reder), W81XWH-15-1-0558 (A. Madabhushi) and W81XWH-20-1-0851 (A. Madabhushi and J.T.C. Liu).
Support was also provided by the National Cancer Institute (NCI) through K99 CA240681 (A.K. Glaser), R01CA244170 (J.T.C. Liu), U24CA199374 (A. Madabhushi), R01CA249992 (A. Madabhushi), R01CA202752 (A. Madabhushi), R01CA208236 (A. Madabhushi), R01CA216579 (A. Madabhushi), R01CA220581 (A. Madabhushi), R01CA257612 (A. Madabhushi), U01CA239055 (A. Madabhushi), U01CA248226 (A. Madabhushi), and U54CA254566 (A. Madabhushi).
Additional support was provided by the National Heart, Lung and Blood Institute (NHLBI) through R01HL151277 (A. Madabhushi), the National Institute of Biomedical Imaging and Bioengineering (NIBIB) through R01EB031002 (J.T.C. Liu) and R43EB028736 (A. Madabhushi), the National Institute of Mental Health through R01MH115767 (J.C. Vaughan), the VA Merit Review Award IBX004121A from the United States Department of Veterans Affairs (A. Madabhushi), the National Science Foundation (NSF) 1934292 HDR: I-DIRSE-FW (J.T.C. Liu), the NSF Graduate Research Fellowships DGE-1762114 (K.W. Bishop) and DGE-1762114 (L. Barner), the Nancy and Buster Alvord Endowment (C.D. Keene), and the Prostate Cancer Foundation Young Investigator Award (N.P. Reder).
The training and inference of the deep learning models were facilitated by the advanced computational, storage, and networking infrastructure provided by the Hyak supercomputer system, as funded in part by the student technology fee (STF) at the University of Washington.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation, the National Institutes of Health, the Department of Defense, the Department of Veterans Affairs, or the United States Government.
Additional publications relating to the data
- Renier, Nicolas, et al. "iDISCO: a simple, rapid method to immunolabel large tissue samples for volume imaging." Cell 159.4 (2014): 896-910.
- Glaser, Adam K., et al. "Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues." Nature communications 10.1 (2019): 1-8.
Localtab Group | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|