Machine learning as a clinical decision support tool for patients with acromegaly

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dc.authoridNurperi Gazioğlu / 0000-0001-7785-8628en_US
dc.authorscopusidNurperi Gazioğlu / 6601973313
dc.authorwosidNurperi Gazioğlu / AAJ-3648-2021en_US
dc.contributor.authorSulu, Cem
dc.contributor.authorBektaş, Ayyüce Begüm
dc.contributor.authorŞahin, Serdar
dc.contributor.authorDurcan, Emre
dc.contributor.authorKara, Zehra
dc.contributor.authorDemir, Ahmet Numan
dc.contributor.authorÖzkaya, Hande Mefkure
dc.contributor.authorTanrıöver, Necmettin
dc.contributor.authorÇomunoğlu, Nil
dc.contributor.authorKızılkılıç, Osman
dc.contributor.authorGazioğlu, Nurperi
dc.contributor.authorGönen, Mehmet
dc.contributor.authorKadıoğlu, Pınar
dc.date.accessioned2022-05-31T14:18:23Z
dc.date.available2022-05-31T14:18:23Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümüen_US
dc.description.abstractObjective To develop machine learning (ML) models that predict postoperative remission, remission at last visit, and resistance to somatostatin receptor ligands (SRL) in patients with acromegaly and to determine the clinical features associated with the prognosis. Methods We studied outcomes using the area under the receiver operating characteristics (AUROC) values, which were reported as the performance metric. To determine the importance of each feature and easy interpretation, Shapley Additive explanations (SHAP) values, which help explain the outputs of ML models, are used. Results One-hundred fifty-two patients with acromegaly were included in the final analysis. The mean AUROC values resulting from 100 independent replications were 0.728 for postoperative 3 months remission status classification, 0.879 for remission at last visit classification, and 0.753 for SRL resistance status classification. Extreme gradient boosting model demonstrated that preoperative growth hormone (GH) level, age at operation, and preoperative tumor size were the most important predictors for early remission; resistance to SRL and preoperative tumor size represented the most important predictors of remission at last visit, and postoperative 3-month insulin-like growth factor 1 (IGF1) and GH levels (random and nadir) together with the sparsely granulated somatotroph adenoma subtype served as the most important predictors of SRL resistance. Conclusions ML models may serve as valuable tools in the prediction of remission and SRL resistance.en_US
dc.identifier.citationSulu, C., Bektas, A. B., Sahin, S., Durcan, E., Kara, Z., Demir, A. N., Ozkaya, H. M., Tanriover, N., Comunoglu, N., Kizilkilic, O., Gazioglu, N., Gonen, M., Kadioglu, P. (2022). Machine learning as a clinical decision support tool for patients with acromegaly. Pituitary.en_US
dc.identifier.doi10.1007/s11102-022-01216-0en_US
dc.identifier.issn1386-341Xen_US
dc.identifier.scopus2-s2.0-85128323192en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s11102-022-01216-0
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2771
dc.identifier.wosWOS:000783392100001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorGazioğlu, Nurperi
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofPITUITARYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMachine Learningen_US
dc.subjectAcromegalyen_US
dc.subjectPrognosisen_US
dc.subjectSomatostatin Receptor Liganden_US
dc.titleMachine learning as a clinical decision support tool for patients with acromegalyen_US
dc.typeArticleen_US

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