Endocrine Abstracts

Objective: To test the utility of the artificial learning algorithms using magnetic resonance (MR) images of the pituitary gland in predicting the prognosis of prolactinoma.

Methods: This single-center, retrospective study was conducted in the Pituitary Center of a tertiary care university hospital. A total of 224 images derived from 38 patients with treatment-refractory prolactinoma, 23 patients with prolactinoma remission and 51 healthy individuals were used. Pituitary MRI protocols are of three sequences: T1-weighted imaging (T1WI), contrast-enhanced T1WI (CE-T1), and T2-weighted imaging (T2WI). A machine learning algorithm that includes image filtering and classification. Data were classified with support vector machine.

Results: No difference was found between the refractory and the remission groups in terms of age, sex, education, the baseline prolactin level and radiological features. Images were classified with a support vector machine; area under curve (AUC), accuracy rate, sensitivity and specificity of 0.90 (95% confidence interval, 0.679–1), 91.6%, 91.7%, 88.3%, respectively.

ROC Curve

Conclusion: These results indicate that a new image of unknown nature can be correctly identified with the specified percentages.