European journal of endocrinology · 2025
Machine learning-based classification of adrenal tumors using clinical, hormonal, and body composition data.
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Park Seung Shin, Noh Jongsung, Kim Jinhee, Kim Taesung, Seo Hae Jin, Ahn Chang Ho, Choo Jaegul, Choi Man Ho, Kim Jung Hee
Аннотация
OBJECTIVE: Accurate diagnosis of adrenal tumors, including mild autonomous cortisol secretion (MACS), adrenal Cushing's syndrome (ACS), primary aldosteronism (PA), pheochromocytoma (PCC), and nonfunctioning adrenal adenomas (NFAs), is crucial but challenging. We aimed to develop a machine learning (ML)-based single-step diagnostic method for differentiating adrenal tumors by integrating clinical data, serum adrenal hormone profiles (SAPs), and body composition data.
METHODS: A total of 641 patients with adrenal tumors (MACS = 141, ACS = 64, PA = 265, PCC = 78, and NFA = 93), excluding adrenal metastases and adrenocortical carcinoma, were enrolled from Seoul National University Hospital. Patients were randomly divided into training and test cohorts at a 4:1 ratio. The ML models were developed to differentiate adrenal tumors using 32 clinical data points, 49 SAP markers, and 15 body composition data points.
RESULTS: The best-performing ML model for differentiating all 5 adrenal tumors achieved a balanced accuracy of 0.78, sensitivity of 0.77, specificity of 0.93, and area under the curve (AUC) of 0.89. To distinguish MACS, ACS, PA, and PCC from NFA, the accuracies were 0.85, 0.94, 0.78, and 0.86, with AUCs of 0.96, 0.99, 0.90, and 0.94, respectively. The ML model differentiating between NFA and the other functioning adrenal tumors exhibited an accuracy of 0.75 and an AUC of 0.79. The SAP features were identified as the most critical for differentiation, whereas body composition data contributed only minimally.
CONCLUSIONS: The ML model demonstrates high diagnostic accuracy in differentiating adrenal tumor subtypes by integrating clinical data, body composition, and SAP, potentially reducing the need for invasive procedures and aiding clinical decision-making.
METHODS: A total of 641 patients with adrenal tumors (MACS = 141, ACS = 64, PA = 265, PCC = 78, and NFA = 93), excluding adrenal metastases and adrenocortical carcinoma, were enrolled from Seoul National University Hospital. Patients were randomly divided into training and test cohorts at a 4:1 ratio. The ML models were developed to differentiate adrenal tumors using 32 clinical data points, 49 SAP markers, and 15 body composition data points.
RESULTS: The best-performing ML model for differentiating all 5 adrenal tumors achieved a balanced accuracy of 0.78, sensitivity of 0.77, specificity of 0.93, and area under the curve (AUC) of 0.89. To distinguish MACS, ACS, PA, and PCC from NFA, the accuracies were 0.85, 0.94, 0.78, and 0.86, with AUCs of 0.96, 0.99, 0.90, and 0.94, respectively. The ML model differentiating between NFA and the other functioning adrenal tumors exhibited an accuracy of 0.75 and an AUC of 0.79. The SAP features were identified as the most critical for differentiation, whereas body composition data contributed only minimally.
CONCLUSIONS: The ML model demonstrates high diagnostic accuracy in differentiating adrenal tumor subtypes by integrating clinical data, body composition, and SAP, potentially reducing the need for invasive procedures and aiding clinical decision-making.