7845 Machine Learning Methods In Differential Diagnosis Of Genetically Confirmed Multiple Endocrine Neoplasia Type 1 And Its Phenocopies

Abstract Disclosure: D. Trukhina: None. K. Voronov: None. E. Mamedova: None. A. Solodovnikov: None. Z. Belaya: None. Background. MEN-1 is a rare autosomal dominant disorder caused by mutations in the MEN1 gene encoding the menin protein (gMEN1). This syndrome is characterised by the occurrence of parathyroid tumours, gastroenteropancreatic neuroendocrine tumours, pituitary adenomas, and other endocrine and non-endocrine neoplasms. If a patient with the MEN-1 phenotype does not have any mutation in the MEN1 gene, the condition is considered a phenocopy of the syndrome (phMEN1).The goal of this study was to develop a machine learning algorithm to estimate the probability of gMEN-1 vs phMEN-1 based on easily available clinical features at first line differential diagnostics. Materials and methods. A single-center, one-stage, cohort, retrospective study was carried out. Patients were randomly stratified into 2 cohorts: training (80%) and test (20%). Eight machine learning algorithms were used to develop predictive models: Logistic Regression, k-nearest neighbors (kNN), Naive Bayes, binary decision tree (CART), C5.0 decision tree algorithms, Bagged CART, Random Forest, Gradient Boosting (Stochastic Gradient Boosting, GBM). Results. The study included 95 patients with genetically confirmed gMEN-1 syndrome and 60 patients with its phenocopies. As a result of preliminary data processing and selection for the most informative features, the final variables for the classification and prediction of gMEN-1 syndrome were identified: the number of affected parathyroid glands, age at diagnostics, pancreatic tumour existence, heredity, type of pituitary adenoma secretion, and gender. The kNN algorithm demonstrated the best diagnostic performance in a training sample (ROC-AUC - 0.96, sensitivity - 84%, specificity - 100%), which was confirmed in a test sample (ROC-AUC - 1.0; sensitivity - 94.4%, specificity - 100%) to determine genetically confirmed MEN-1 syndrome. Conclusion. The k-nearest neighbours (kNN) algorithm may be used as a first-line differential diagnostics method to select patients to be refered for genetic testing for gMEN-1. Presentation: 6/1/2024