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  • Voxel-level segmentation of pathologically-proven Adrenocortical carcinoma with Ki-67 expression (Adrenal-ACC-Ki67-Seg)

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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:

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Data Access

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Detailed Description

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Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:


Data Citation

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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

Other Publications Using This Data

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Version X (Current): Updated yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images (DICOM, xx.x GB)

   

(Requires NBIA Data Retriever.)

Clinical Data (CSV)Link

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