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Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma.

ABSTRACT: The main challenge in the management of indeterminate incidentally discovered adrenal tumours is to differentiate benign from malignant lesions. In the absence of clear signs of invasion or metastases, imaging techniques do not always precisely define the nature of the mass. The present pilot study aimed to determine whether radiomics may predict malignancy in adrenocortical tumours. CT images in unenhanced, arterial, and venous phases from 19 patients who had undergone resection of adrenocortical tumours and a cohort who had undergone surveillance for at least 5 years for incidentalomas were reviewed. A volume of interest was drawn for each lesion using dedicated software, and, for each phase, first-order (histogram) and second-order (grey-level colour matrix and run-length matrix) radiological features were extracted. Data were revised by an unsupervised machine learning approach using the K-means clustering technique. Of operated patients, nine had non-functional adenoma and 10 carcinoma. There were 11 patients in the surveillance group. Two first-order features in unenhanced CT and one in arterial CT, and 14 second-order parameters in unenhanced and venous CT and 10 second-order features in arterial CT, were able to differentiate adrenocortical carcinoma from adenoma (P < 0.050). After excluding two malignant outliers, the unsupervised machine learning approach correctly predicted malignancy in seven of eight adrenocortical carcinomas in all phases. Radiomics with CT texture analysis was able to discriminate malignant from benign adrenocortical tumours, even by an unsupervised machine learning approach, in nearly all patients.

SUBMITTER: Torresan F

PROVIDER: S-EPMC7937424 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Adrenal myelolipoma (AML) is a relatively common and invariably benign tumor composed of adipose tissue and hematopoietic elements. Due to the variable proportion of fat and hematopoietic elements, it is sometimes challenging to differentiate AML from adrenocortical carcinoma (ACC). MicroRNAs have been identified as promising biomarkers in many tumors, including adrenocortical neoplasms, but the microRNA expression of adrenal myelolipoma has not been investigated, yet. Aims: To perform a large scale microRNA expression profiling in adrenal myelolipoma, benign and malignant adrenocortical tumors to identify potential microRNA biomarkers. Methods: Next-generation sequencing (NGS) on 30 formalin-fixed paraffin-embedded archived tissue samples (discovery cohort: 10 adrenocortical adenoma (ACA), 10 ACC and 10 myelolipoma) was performed by Illumina MiSeq. Significantly differentially expressed microRNAs were validated by real-time RT-qPCR in an independent validation cohort comprised of 10 ACA, 10 myelolipoma and 9 ACC samples. Results: We have found relative overexpression of miR- 451a, miR-486-5p, miR-363-3p and miR-150-5p in myelolipoma compared to the other two tumor groups by NGS. For ACC, we have found up-regulation of miR-184, miR-483-5p, miR-431-5p and miR-183-5p compared to myelolipoma and ACA. Validation by RT-qPCR, confirmed significant overexpression of miR-451a, miR-486-5p and miR-150-5p in myelolipomas relative to ACA and ACC, whereas significant overexpression of miR-184 and miR-183-5p was confirmed in ACC relative to the other 2 groups. The overexpression of miR-483-5p has not turned out to be significant in ACC relative to myelolipomas in the validation cohort. Conclusions: Overexpressed miR-451a, miR-486-5p and miR-150-5p might be potential tissue markers of adrenal myelolipoma. The lack of significance in the expression of miR-483-5p between ACC and myelolipoma is remarkable, as miR-483-5p has been considered to be the best marker of adrenal malignancy to date.

Project description:Purpose: To explore the application value of multiparametric computed tomography (CT) radiomics in non-invasive differentiation between aldosterone-producing and cortisol-producing functional adrenocortical adenomas. Methods: This retrospective review analyzed 83 patients including 41 patients with aldosterone-producing adenoma and 42 patients with cortisol-producing adenoma. The quantitative radiomics features were extracted from the complete unenhanced, arterial, and venous phase CT images. A comparative study of several frequently used machine learning models (linear discriminant analysis, logistic regression, random forest, and support vector machine) combined with different feature selection methods was implemented in order to determine which was most advantageous for differential diagnosis using radiomics features. Then, the integrated model using the combination of radiomic signature and clinic-radiological features was built, and the associated calibration curve was also presented. The diagnostic performance of these models was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC). Result: In the radiomics-based machine learning model, logistic regression model with LASSO (least absolute shrinkage and selection operator) outperformed the other models, which yielded a sensitivity of 0.935, a specificity of 0.823, and an accuracy of 0.887 [AUC = 0.882, 95% confidence interval (CI) = 0.819-0.945]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.915, a specificity of 0.928, and an accuracy of 0.922 (AUC = 0.902, 95% CI = 0.822-0.982), and it was better than that of the radiomics model alone. Conclusion: This study found that the combination of multiparametric radiomics signature and clinic-radiological features can non-invasively differentiate the subtypes of hormone-secreting functional adrenocortical adenomas, which may have good potential for facilitating the diagnosis and treatment in clinical practice.

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Radiomics: a new tool to differentiate adrenocortical adenoma from carcinoma.

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