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 lipidpoor 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 crossvalidation) 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 crossvalidation 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.
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Article Synopsis
- The study aimed to investigate the effectiveness of CT quantitative parameters in distinguishing between lipid-poor adenoma (LPA) and nodular hyperplasia in adrenal lesions.
- Researchers analyzed 128 patients, measuring various CT parameters, including attenuation values and lesion volume, and developed a logistic regression model to evaluate diagnostic accuracy.
- The model showed a good performance with an area under the ROC curve (AUC) of 0.835, with better sensitivity and specificity than radiologists' assessments, indicating that CT metrics can be more reliable for diagnosis.
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