{"id":5740,"date":"2023-09-04T03:22:49","date_gmt":"2023-09-04T03:22:49","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/adrenal-acc-ki67-seg\/"},"modified":"2023-09-13T17:47:41","modified_gmt":"2023-09-13T17:47:41","slug":"adrenal-acc-ki67-seg","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/adrenal-acc-ki67-seg\/","title":{"rendered":"ADRENAL-ACC-KI67-SEG"},"featured_media":8543,"template":"","citation-tax":[],"cancer_types":["Adrenocortical Carcinoma"],"citations":[4734,4735,2925],"collection_doi":"10.7937\/1FPG-VM46","collection_download_info":"Click the Versions tab for more info about data releases.","collection_downloads":[5348,5349],"full_export":"

Summary<\/h1>\r\n
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\r\n\r\nAdrenocortical 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.\r\n\r\nThis retrospectively acquired data includes contrast enhanced CT imaging studies of 53 confirmed ACC patients 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<\/a>, 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.\u00a0<\/span>\r\n\r\nThe participants in this dataset fulfilled these inclusion <\/strong>criteria: <\/span>\r\n
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  1. Pathologically proven Adrenocortical carcinoma<\/span><\/li>\r\n \t
  2. Underwent surgical resection of the tumour<\/span><\/li>\r\n \t
  3. The Ki-67 index was determined as part of the histopathological evaluation of the resected tissue<\/span><\/li>\r\n \t
  4. Imaging data (pre-resection contrast-enhanced CT of the abdomen) were available.\u00a0<\/span><\/li>\r\n<\/ol>\r\nData from patients whose Ki-67 was quantified in biopsied tissue samples rather than from resected whole tumor, were excluded <\/strong>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.\r\n\r\nThere was no publicly-available library for adrenal lesions prior to this dataset.\u00a0 It can serve as a training set for machine learning 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 as described in this paper<\/a>.\u00a0\u00a0<\/span>\r\n\r\n<\/div>\r\n
    \r\n\r\n<\/span>\r\n\r\nCT 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).<\/em>\r\n\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n<\/div>\r\n

    Acknowledgements<\/h3>\r\n