6999 Utilizing Machine Learning for One-Step Diagnosis Of Adrenal Diseases: Integrating Clinical, Serum Steroid, And Body Composition Data

Abstract

Abstract Disclosure: S. Park: None. J. Noh: None. J. Kim: None. T. Kim: None. H. Seo: None. J. Choo: None. M. Choi: None. J. Kim: None. Background: With the increasing incidence of adrenal incidentalomas, the precise diagnosis of subtypes, such as mild autonomous cortisol secretion (MACS), adrenal Cushing’s syndrome (ACS), primary aldosteronism (PA), pheochromocytoma and paraganglioma (PPGL), or nonfunctioning adrenal adenomas (NFA) is becoming more critical. However, the process of making accurate diagnoses remains intricate and challenging. This study aims to explore a streamlined, one-step method for diagnosing adrenal nodules, employing a machine learning (ML) model that integrates clinical data (CL), serum steroid profiles (SSP), and computed tomography-based body composition data (BC). Methods: We utilized ML models for subtyping adrenal diseases within a cohort of 641 patients (comprising MACS=141, ACS=64, PA=265, PPGL=78, and NFA=93), incorporating 37 CL, 64 SSP, and 15 BC features. Patients were randomly split into training and test cohorts at a 4:1 ratio, with each disease proportionally represented. The ML-based pipeline involved ML model selection, outlier detection, feature selection, and re-sampling algorithm. An ML model was developed to classify each of the five distinct adrenal diseases individually. In addition, separate ML models were also developed and optimized for individual comparisons between NFA and each of the other four diseases. The performance was evaluated using balanced accuracy, sensitivity, specificity, area under the curve (AUC), and F1 score. Results: The OneVsRest Classifier exhibited the best performance in the multi-class classification of the five adrenal diseases, achieving a balanced accuracy (0.78), sensitivity (0.77), specificity (0.93), and an AUC of 0.89. In this process, 10 CL features and 39 SSP features were utilized, whereas BC did not yield significant features. Using only SSP, the ML model achieved a balanced accuracy of 0.71 and an AUC of 0.89, whereas the ML model using only CL demonstrated a relatively lower accuracy of 0.58 and an AUC of 0.82. In models comparing MACS, ACS, PA, and PPGL with NFAs, the accuracies were 0.85, 0.94, 0.78, and 0.86, respectively, with AUCs of 0.96, 0.99, 0.90, and 0.94, respectively. The ML model differentiating between NFA and the other functioning adrenal diseases exhibited an accuracy of 0.75 and an AUC of 0.79 Conclusion: We have developed a highly sensitive and specific one-step ML model for differentiating adrenal diseases using CL, SSP, and BC. This approach is anticipated to facilitate precise diagnoses of adrenal nodules, thereby enhancing patient care and improving clinical outcomes. Presentation: 6/2/2024