Machine Learning-Based Texture Analysis in the Characterization of Cortisol Secreting vs. Non-Secreting Adrenocortical Incidentalomas in CT Scan - Maggio, Roberta; Messina, Filippo; D’Arrigo, Benedetta; Maccagno, Giacomo; Lardo, Pina; Palmisano, Claudia; Poggi, Maurizio; Monti, Salvatore; Matarazzo, Iolanda; Laghi, Andrea; Pugliese, Giuseppe; Stigliano, Antonio (Frontiers in Endocrinology, 2022)
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 followed at our center, subdivided in functioning and non functioning using laboratory findings. Each incidentaloma was studied in the preliminary non-contrast phase with a specific software (Mazda), surrounding a region of interest within each lesion. 314 features were extrapolated. Mean and standard deviations of features were obtained and the difference in means between the two groups was statistically analyzed. ROC curves were used to identify an optimal cut off for each variable and a prediction model was constructed via multivariate logistic regression with backward and stepwise selection. A 11-variables prediction model was constructed and a ROC curve was used to differentiate patients with high probability of functioning incidentalomas. Using a specific threshold value we obtained a sensitivity of 93.75% and a specificity of 100% in diagnosing functioning incidentaloma. Based on these results, computed tomography (CT) texture analysis appears a promising tool in the diagnostic definition of adrenal incidentalomas.