Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Endocrine Society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Context: Artificial intelligence research in the field of neuroendocrinology has accelerated. It is possible to develop noninvasive, easy-to-use and cost-effective procedures that can replace invasive procedures for the differential diagnosis of adrenocorticotropin (ACTH)-dependent Cushing syndrome (CS) by artificial intelligence. Objective: This study aimed to develop machine-learning (ML) algorithms for the differential diagnosis of ACTH-dependent CS based on biochemical and radiological features. Methods: Logistic regression algorithms were used for ML, and the area under the receiver operating characteristics curve was used to measure performance. We used Shapley contributed comments (SHAP) values, which help explain the results of the ML models to identify the meaning of each feature and facilitate interpretation. Results: A total of 106 patients, 80 with Cushing disease (CD) and 26 with ectopic ACTH syndrome (EAS), were enrolled in the study. The ML task was created to classify patients with ACTH-dependent CS into CD and EAS. The average AUROC value obtained in the cross-validation of the logistic regression model created for the classification task was 0.850. The diagnostic accuracy of the algorithm was 86%. The SHAP values indicated that the most important determinants for the model were the 2-day 2-mg dexamethasone suppression test, greater than 50% suppression in the 8-mg high-dose dexamethasone test, late-night salivary cortisol, and the diameter of the pituitary adenoma. We have also made our algorithm available to all clinicians via a user-friendly interface. Conclusion: ML algorithms have the potential to serve as an alternative decision-support tool to invasive procedures in the differential diagnosis of ACTH-dependent CS. © The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved
Açıklama
Anahtar Kelimeler
Bilateral İnferior Petrosal Sinus Sampling, Cushing Disease, Ectopic ACTH Syndrome, Machine Learning
Kaynak
Journal of Clinical Endocrinology and Metabolism
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
110
Sayı
2
Künye
Demir, A. N., Ayata, D., Oz, A., Sulu, C., Kara, Z., Sahin, S., ... & Kadioglu, P. (2025). Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome. The Journal of Clinical Endocrinology & Metabolism, 110(2), e412-e422.
