Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome

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dc.authorscopusidNurperi Gazioğlu / 6601973313
dc.authorwosidNurperi Gazioğlu / AAJ-3648-2021
dc.contributor.authorDemir, Ahmet Numan
dc.contributor.authorAyata, Değer
dc.contributor.authorÖz, Ahmet
dc.contributor.authorSulu, Cem
dc.contributor.authorKara, Zehra
dc.contributor.authorŞahin, Serdar
dc.contributor.authorÖzaydın, Dilan
dc.contributor.authorKorkmazer, Bora
dc.contributor.authorArslan, Serdar
dc.contributor.authorKızılkılıç, Osman
dc.contributor.authorÇiftçi, Sema
dc.contributor.authorÇelik, Özlem
dc.contributor.authorÖzkaya, Hande Mefkure
dc.contributor.authorTanrıöver, Necmettin
dc.contributor.authorGazioğlu, Nurperi
dc.contributor.authorKadıoğlu, Pınar
dc.date.accessioned2025-04-18T10:49:07Z
dc.date.available2025-04-18T10:49:07Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü
dc.description.abstractContext: 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
dc.identifier.citationDemir, 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.
dc.identifier.doi10.1210/clinem/dgae180
dc.identifier.endpagee4221
dc.identifier.issn0021972X
dc.identifier.issue2
dc.identifier.pmid38501466
dc.identifier.scopus2-s2.0-85216607195
dc.identifier.scopusqualityQ1
dc.identifier.startpagee412
dc.identifier.urihttp://dx.doi.org/10.1210/clinem/dgae180
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7202
dc.identifier.volume110
dc.identifier.wosWOS:001199553700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.indekslendigikaynakWeb of Science
dc.institutionauthorGazioğlu, Nurperi
dc.institutionauthoridNurperi Gazioğlu / 0000-0001-7785-8628
dc.language.isoen
dc.publisherEndocrine Society
dc.relation.ispartofJournal of Clinical Endocrinology and Metabolism
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectBilateral İnferior Petrosal Sinus Sampling
dc.subjectCushing Disease
dc.subjectEctopic ACTH Syndrome
dc.subjectMachine Learning
dc.titleMachine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome
dc.typeArticle

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