A A Ahmed1, M M Elmohr2, D Fuentes2, M A Habra3, S B Fisher4, N D Perrier4, M Zhang5, K M Elsayes6. 1. Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 2. Department Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 3. Department Endocrine Neoplasia and Hormonal Disorders, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 4. Department Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 5. Department Pathology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 6. Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address: kmelsayes@mdanderson.org.
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.
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.
Authors: A De Leo; G Vara; G Di Dalmazi; C Mosconi; A Paccapelo; C Balacchi; V Vicennati; L Tucci; U Pagotto; S Selva; C Ricci; L Alberici; F Minni; C Nanni; F Ambrosi; D Santini; R Golfieri Journal: J Endocrinol Invest Date: 2022-06-10 Impact factor: 5.467