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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

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Excerpt

Adrenocortical carcinoma (ACC) is a rare tumor of the adrenal cortex with a reported annual incidence of one case per million population. ACC is a highly aggressive, highly fatal tumor with 5-year overall survival rates ranging from 14% to 44%. Diagnosis of ACC is primarily based on histopathological parameters from resected tumors, which include Ki-67 expression status. The Ki-67 index is one of the most important established prognostic markers for local recurrence of ACC. Radiomic feature extraction showed a significant association between radiomic signature and Ki-67 expression status in ACC.

This retrospectively acquired data includes contrast enhanced CT imaging studies of 53 confirmed ACC patients

in this dataset should fulfill: (1) Pathologically proven Adrenocortical carcinoma, (2) underwent

between 2006 to 2018 with available clinical and pathological data, including Ki-67 index. Semi-automatic segmentation of the adrenal tumor was created using AMIRA, then manually refined by an experienced radiologist. Voxel level segmentation of the adrenal lesion are included as well. The segmentations of each contrast-enhanced CT were done for the purpose of radiomic features extraction. 

The participants in this dataset fulfilled these inclusion criteria:

  1. Pathologically proven Adrenocortical carcinoma
  2. Underwent surgical resection of the tumour
, (3) the
  2. The Ki-67 index was determined as part of the histopathological evaluation of the resected tissue
, (4) imaging
  2. 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

There was no publicly-available library for adrenal lesions prior to this dataset.  It can serve as a training set for

any

machine learning

algorithm

algorithms for various purposes including segmentation and classification of adrenal tumors. 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:

as described in this paper.  



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Image Added

CT scan of the abdomen (A) showing left adrenal mass. The adrenal mass (red) is segmented in all planes (Axial (B), Sagittal (C) and coronal (D) planes).


Acknowledgements

  • The University of Texas MD Anderson Cancer Center, departments of Surgical Oncology, Endocrinology, Pathology, Imaging Physics, and Diagnostic Radiology.
  • The authors would like to thank the Scientific Publication department at the University of Texas MD Anderson Cancer Center for their contribution to this dataset and articles.  

  • Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  • Continue with any names from additional submitting sites if collection consists of more that one.


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titleData Access

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titleDetailed Description

Detailed Description

Image Statistics

Radiology Image statistics

Modalities

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Number of Patients

53

Number of Studies

65

Number of Series

177

Number of Images

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titleCitations & Data Usage Policy

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titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval formMoawad, A. W., Ahmed, A. A., ElMohr, M., Eltaher, M., Habra, M. A., Fisher, S., Perrier, N., Zhang, M., Fuentes, D., & Elsayes, K. (2023). Voxel-level segmentation of pathologically-proven Adrenocortical carcinoma with Ki-67 expression (Adrenal-ACC-Ki67-Seg) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/1FPG-VM46


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titlePublication Citation

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titleAcknowledgement
Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.

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


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titleTCIA 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

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titleVersions

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