A CT Radiomics Analysis of the Adrenal Masses: Can We Discriminate Lipid-poor Adenomas from the Pheochromocytoma and Malignant Masses?
Current Medical Imaging, cilt.19, sa.9, ss.1018-1030, 2023 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 19 Sayı: 9
- Basım Tarihi: 2023
- Doi Numarası: 10.2174/1573405619666221115124352
- Dergi Adı: Current Medical Imaging
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Biotechnology Research Abstracts, EMBASE, MEDLINE
- Sayfa Sayıları: ss.1018-1030
- Anahtar Kelimeler: adrenal, computed tomography, logit fit, Radiomics, random forest, texture analysis
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Hayır
Özet
Aims: The aim of the study is to demonstrate a non-invasive alternative method to aid the decision making process in the management of adrenal masses. Background: Lipid-poor adenomas constitute 30% of all adrenal adenomas. When discovered incidentally, additional dynamic adrenal examinations are required to differentiate them from an adrenal malignancy or pheochromocytoma. Objective: In this retrospective study, we aimed to discriminate lipid-poor adenomas from other lipid-poor adrenal masses by using radiomics analysis in single contrast phase CT scans. Materials and Methods: A total of 38 histologically proven lipid-poor adenomas (Group 1) and 38 cases of pheochromocytoma or malignant adrenal mass (Group 2) were included in this retrospective study. Lesions were segmented volumetrically by two independent authors, and a total of 63 sizes, shapes, and first-and second-order parameters were calculated. Among these parameters, a logit-fit model was produced by using 6 parameters selected by the LASSO (least absolute shrinkage and selection operator) regression. The model was cross-validated with LOOCV (leave-one-out cross-validation) and 1000-bootstrap sampling. A random forest model was also generated in order to use all parameters without the risk of multicollinearity. This model was examined with the nested cross-validation method. Results: Sensitivity, specificity, accuracy and AUC were calculated in test sets as 84.2%, 81.6%, 82.9% and 0.829 in the logit fit model and 91%, 80%, 82.8% and 0.975 in the RF model, respectively. Conclusion: Predictive models based on radiomics analysis using single-phase contrast-enhanced CT can help characterize adrenal lesions.