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

Basit Öğe Kaydı
dc.authoridDemir, Ahmet Numan/0000-0002-9997-7051
dc.authoridKadioglu, Pinar/0000-0002-8329-140X
dc.authoridOz, Ahmet/0000-0001-5665-7923
dc.authorwosidDemir, Ahmet Numan/AEK-2338-2022
dc.authorwosidOz, Ahmet/ITU-3274-2023
dc.contributor.authorDemir, Ahmet Numan
dc.contributor.authorAyata, Deger
dc.contributor.authorOz, Ahmet
dc.contributor.authorSulu, Cem
dc.contributor.authorKara, Zehra
dc.contributor.authorSahin, Serdar
dc.contributor.authorOzaydin, Dilan
dc.date.accessioned2024-05-19T14:39:24Z
dc.date.available2024-05-19T14:39:24Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
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.en_US
dc.identifier.doi10.1210/clinem/dgae180
dc.identifier.issn0021-972X
dc.identifier.issn1945-7197
dc.identifier.pmid38501466en_US
dc.identifier.urihttps://doi.org10.1210/clinem/dgae180
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4770
dc.identifier.wosWOS:001199553700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherEndocrine Socen_US
dc.relation.ispartofJournal of Clinical Endocrinology & Metabolismen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBilateral Inferior Petrosal Sinus Samplingen_US
dc.subjectCushing Diseaseen_US
dc.subjectCushing Syndromeen_US
dc.subjectEctopic Acth Syndromeen_US
dc.subjectMachine Learningen_US
dc.titleMachine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndromeen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu