Abstract:
AIM:To determine the value of contrast-enhanced computed tomography (CT)-derived radiomic features in the preoperative prediction of Ki-67 expression in adrenocortical carcinoma (ACC) and to detect significant associations between radiomic features and Ki-67 expression in ACC. MATERIALS AND METHODS:For this retrospective analysis, patients with histopathologically proven ACC were reviewed. Radiomic features were extracted for all patients from the preoperative contrast-enhanced abdominal CT images. Statistical analysis identified the radiomic features predicting the Ki-67 index in ACC and analysed the correlation with the Ki-67 index. RESULTS:Fifty-three cases of ACC that met eligibility criteria were identified and analysed. Of the radiomic features analysed, 10 showed statistically significant differences between the high and low Ki-67 expression subgroups. Multivariate linear regression analysis yielded a predictive model showing a significant association between radiomic signature and Ki-67 expression status in ACC (R2=0.67, adjusted R2=0.462, p=0.002). Further analysis of the independent predictors showed statistically significant correlation between Ki-67 expression and shape flatness, elongation, and grey-level long run emphasis (p=0.002, 0.01, and 0.04, respectively). The area under the curve for identification of high Ki-67 expression status was 0.78 for shape flatness and 0.7 for shape elongation. CONCLUSION:Radiomic features derived from preoperative contrast-enhanced CT images show encouraging results in the prediction of the Ki-67 index in patients with ACC. Morphological features, such as shape flatness and elongation, were superior to other radiomic features in the detection of high Ki-67 expression.
journal_name
Clin Radioljournal_title
Clinical radiologyauthors
Ahmed AA,Elmohr MM,Fuentes D,Habra MA,Fisher SB,Perrier ND,Zhang M,Elsayes KMdoi
10.1016/j.crad.2020.01.012subject
Has Abstractpub_date
2020-06-01 00:00:00pages
479.e17-479.e22issue
6eissn
0009-9260issn
1365-229Xpii
S0009-9260(20)30048-9journal_volume
75pub_type
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