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    Machine learning as a clinical decision support tool for patients with acromegaly
    (SPRINGER, 2022) Sulu, Cem; Bektaş, Ayyüce Begüm; Şahin, Serdar; Durcan, Emre; Kara, Zehra; Demir, Ahmet Numan; Özkaya, Hande Mefkure; Tanrıöver, Necmettin; Çomunoğlu, Nil; Kızılkılıç, Osman; Gazioğlu, Nurperi; Gönen, Mehmet; Kadıoğlu, Pınar
    Objective 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.
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    Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome
    (Endocrine Society, 2025) Demir, Ahmet Numan; Ayata, Değer; Öz, Ahmet; Sulu, Cem; Kara, Zehra; Şahin, Serdar; Özaydın, Dilan; Korkmazer, Bora; Arslan, Serdar; Kızılkılıç, Osman; Çiftçi, Sema; Çelik, Özlem; Özkaya, Hande Mefkure; Tanrıöver, Necmettin; Gazioğlu, Nurperi; Kadıoğlu, Pınar
    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