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    A CT Radiomics Analysis of the Adrenal Masses: Can We Discriminate Lipid-poor Adenomas from the Pheochromocytoma and Malignant Masses?
    (Bentham Science Publ Ltd, 2023) Mendi, Bokebatur Ahmet Rasit; Gulbay, Mutlu
    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.
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    Bone marrow lesions of the femoral head: can radiomics distinguish whether it is reversible?
    (Termedia Publishing House Ltd., 2023) Batur, Halitcan; Mendi, Bokebatur Ahmet Rasit; Cay, Nurdan
    Purpose: Contrary to the self-limiting nature of reversible bone marrow lesions, irreversible bone marrow lesions require early surgical intervention to prevent further morbidity. Thus, early discrimination of irreversible pathology is necessitated. The purpose of this study is to evaluate the efficacy of radiomics and machine learning regarding this topic. Material and methods: A database was scanned for patients who had undergone MRI of the hip for differential diagnosis of bone marrow lesions and had had follow-up images acquired within 8 weeks after the first imaging. Images that showed resolution of oedema were included in the reversible group. The remainders that showed progression into characteristic signs of osteonecrosis were included in the irreversible group. Radiomics was performed on the first MR images, calculating first-and second-order parameters. Support vector machine and random forest classifiers were performed using these parameters. Results: Thirty-seven patients (seventeen osteonecrosis) were included. A total of 185 ROIs were segmented. Forty-seven parameters were accepted as classifiers with an area under the curve value ranging from 0.586 to 0.718. Support vector machine yielded a sensitivity of 91.3% and a specificity of 85.1%. Random forest classifier yielded a sensitivity of 84.8% and a specificity of 76.7%. Area under curves were 0.921 for support vector machine and 0.892 for random forest classifier. Conclusions: Radiomics analysis could prove useful for discrimination of reversible and irreversible bone marrow lesions before the irreversible changes occur, which could prevent morbidities of osteonecrosis by guiding the decision-making process for management. © Pol J Radiol 2023.
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    Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning
    (Springer, 2022) Cay, Nurdan; Mendi, Bokebatur Ahmet Rasit; Batur, Halitcan; Erdogan, Fazli
    Purpose To evaluate the diagnostic capability of radiomics in distinguishing lipoma and Atypic Lipomatous Tumors/Well-Differentiated Liposarcomas (ALT/WDL) with Magnetic Resonance Imaging (MRI). Materials and methods Patients with a histopathologic diagnosis of lipoma (n = 45) and ALT/WDL (n = 20), who had undergone pre-surgery or pre-biopsy MRI, were enrolled. The MDM2 amplification was accepted as gold-standard test. The T1-weighted turbo spin echo images were used for radiomics analysis. Utility of a predefined standardized imaging protocol and a single type of 1.5 T scanner were sought as inclusion criteria. Radiomics parameters that show a certain level of reproducibility were included in the study and supplied to Support Vector Machine (SVM) as a machine learning method. Results No significant difference was found in terms of gender, location and age between the lipoma and ALT/WDL groups. Sixty-five parameters were accepted as reproducible. Fifty-seven parameters were able to distinguish the two groups significantly (AUC range 0.564-0.902). Diagnostic performance of the SVM was one of the highest among literature findings: sensitivity = 96.8% (95% CI 94.03-98.39%), specificity = 93.72% (95% CI 86.36-97.73%) and AUC = 0.987 (95% CI 0.972-0.999). Conclusion Although radiomics has been proven to be useful in previous literature regarding discrimination of lipomas and ALT/WDLs, we found that its accuracy could further be improved with utility of standardized hardware, imaging protocols and incorporation of machine learning methods.