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. 2022 Jun 17:13:873189.
doi: 10.3389/fendo.2022.873189. eCollection 2022.

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

Affiliations

Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan

Roberta Maggio et al. Front Endocrinol (Lausanne). .

Abstract

New radioimaging techniques, exploiting the quantitative variables of imaging, permit to identify an hypothetical pathological tissue. We have applied this potential in a series of 72 adrenal incidentalomas (AIs) followed at our center, subdivided in functioning and non-functioning using laboratory findings. Each AI was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. A total of 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. Receiver Operating Characteristic (ROC) curves were used to identify an optimal cutoff for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variable prediction model was constructed, and a ROC curve was used to differentiate patients with high probability of functioning AI. Using a threshold value of >-275.147, we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning AI. On the basis of these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of AIs.

Keywords: adrenal incidentalomas; cortisol secreting adrenal mass; differential diagnosis of adrenal mass; non-secreting adrenal mass; radiomics; subclinical hypercortisolism; texture analysis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
CT ROI segmentation within left adrenal lesion.
Figure 2
Figure 2
Radiomics workflow and predictive model setup procedure. (A) Patients included in the study: 18 of them showed a cortisol secreting and 28 non-functioning adrenal masses. (B) Definition of ROI. (C) Extraction of 314 features candidates. (D) Construction of predictive model by a multivariate logistic regression with eight different variables.
Figure 3
Figure 3
ROC curve graph of eight-variable predictive model.

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