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    Changing presentation of acromegaly in half a century: a single-center experience
    (Springer, 2023) Demir, Ahmet Numan; Sulu, Cem; Kara, Zehra; Sahin, Serdar; Ozaydin, Dilan; Sonmez, Ozge; Keskin, Fatma Ela
    ObjectiveInvestigate the changes in the characteristics of presentation, in patients with acromegaly over a period of approximately half a century.MethodsThe medical records of patients diagnosed with acromegaly between 1980 and 2023 were retrospectively reviewed. The collected data were examined to assess any changes observed over the years and a comparison was made between the characteristics of patients diagnosed in the last decade and those diagnosed in previous years.ResultsA total of 570 patients were included in the study, 210 (37%) patients were diagnosed in the last decade. Patients diagnosed before 2014 had longer symptom duration before diagnosis, advanced age, larger pituitary adenomas, higher incidence of cavernous sinus invasion, and higher GH and IGF-1 levels than those diagnosed last decade (p < 0.05, for all). Furthermore, the patients diagnosed before 2014 had a lower rate of surgical remission (p < 0.001), and a higher prevalence of comorbidities such as diabetes, hypertension, colon polyps, and thyroid cancer at the time of diagnosis (p < 0.05, for all).ConclusionThere may be a trend for earlier detection of patients with acromegaly.
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    Germinoma Misdiagnosed as Lymphocytic Hypophysitis
    (Galenos Publ House, 2023) Sahin, Serdar; Baskurt, Ozan; Comunoglu, Nil; Kadioglu, Pinar; Gazioglu, Nurperi
    [Abstract Not Available]
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    Machine Learning May Be an Alternative to BIPSS in the Differential Diagnosis of ACTH-dependent Cushing Syndrome
    (Endocrine Soc, 2024) Demir, Ahmet Numan; Ayata, Deger; Oz, Ahmet; Sulu, Cem; Kara, Zehra; Sahin, Serdar; Ozaydin, Dilan
    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.