Summary
The patients in this dataset should fulfill: (1) Pathologically proven Adrenocortical carcinoma, (2) underwent surgical resection of the tumour, (3) the Ki-67 index was determined as part of the histopathological evaluation of the resected tissue, (4) imaging data (pre-resection contrast-enhanced CT of the abdomen) were available. Data from patients whose Ki-67 was quantified in biopsied tissue samples rather than from resected whole tumor, were excluded from this study. This exclusion was based on previous studies concluding that Ki-67 quantification should be based on tissue samples collected from the whole tumour. Voxel level segmentation of the adrenal lesion in will be included as well.This dataset can serve as a training set for any machine learning algorithm for various purposes. We used the radiomic features extracted to predict the Ki-67 index (through regression) without the need of surgical intervention, it also can be used for multiple purposes for segmentation, and classification of adrenal tumors. In addition, there is no public available library for adrenal lesions, and this should be important to scientific community.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Mohab M. Elmohr
Aya A. Ahmed
Data Access
Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, 9.9 GB) | (Download requires the NBIA Data Retriever) |
Clinical data (CSV) |
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Detailed Description
Image Statistics | |
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Modalities | CT, SEG |
Number of Patients | 53 |
Number of Studies | 65 |
Number of Series | 177 |
Number of Images | 18,255 |
Images Size (GB) | 9.9 GB |
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Citations & Data Usage Policy
Data Citation
DOI <coming soon>
Title: Voxel-level segmentation of pathologically-proven Adrenocortical carcinoma with Ki-67 expression (Adrenal-Ki67-Seg) [Dataset]
Authors: Ahmed W. Moawad (https://orcid.org/0000-0001-6860-1513), Khaled M. Elsayes (https://orcid.org/0000-0003-0184-0717), David T. fuentes (https://orcid.org/0000-0002-2572-6962), M.A. Habra, S.B. Fisher, N.D. Perrier, M. Zhang, K.M. Elsayes
2022
Publication Citation
Ahmed, A. A., Elmohr, M. M., Fuentes, D., Habra, M. A., Fisher, S. B., Perrier, N. D., Zhang, M., & Elsayes, K. M. (2020). Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma. In Clinical Radiology (Vol. 75, Issue 6, p. 479.e17-479.e22). Elsevier BV. https://doi.org/10.1016/j.crad.2020.01.012
Acknowledgement
Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.
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: 10.1007/s10278-013-9622-7
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Version 1 (Current): Updated 2022/01/dd
Data Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |
Clinical Data (CSV) | Link |
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