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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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    The Combination of Dopamine Agonist Treatment and Surgery May Be the Best Option in Challenging Prolactinoma Cases: A Single-Centre Experience
    (Elsevier Science Inc, 2023) Demir, Dilan; Demir, Ahmet Numan; Sulu, Cem; Zulfaliyeva, Guldana; Cetintas, Semih Can; Ozkaya, Hande Mefkure; Kadioglu, Pinar
    -OBJECTIVE: To investigate the initial and long-term remission rates, factors related to remission, secondary treatments, and outcomes for patients with prolactinoma who underwent endoscopic transsphenoidal surgery (ETSS).-METHODS: The medical files of the 45 prolactinoma patients who underwent ETSS between 2015 and 2022 were retrospectively reviewed. Relevant demographic and clin-ical data were obtained. -RESULTS: Twenty-one (46.7%) patients were female. The median age of patients at ETSS was 35 (interquartile range, 22.5-50) years. The median clinical follow-up of patients was 28 (interquartile range 12-44) months. The initial surgical remission rate was 60%. Recurrence was detected in 7 patients (25.9%). Postoperative dopamine agonists were used in 25 patients, radiosurgery in 2, and second ETSS in 4 patients. After these secondary treatments, the long-term biochemical remission rate was 91.1%. The factors associated with failure in surgical remission are: male gender, older age, higher tumor size, advanced Knosp and Hardy stage, and elevated prolactin level at diagnosis. A prolactin level of <19 ng/mL in the first postoperative week predicted surgical remission with a sensitivity of 77.8% and a specificity of 70.6% in patients who received preoperative dopamine agonist treatment. -CONCLUSIONS: In macro adenomas and/or giant ade-nomas with cavernous sinus invasion, and significant suprasellar extension, which constitutes the difficult part of prolactinoma treatment, neither surgery nor medical treatment alone may be effective enough. Both treatment modalities should be carried out together by a team of neurosurgery and endocrinology in the management of these patients.
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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 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.