(1)H MR spectroscopy can be used to characterize adrenal masses on the basis of spectral findings for benign adenomas, carcinomas, pheochromocytomas, and metastases.

aneurIsma Intraselar da carótIda Interna coexIstIndo com adenoma secretor de gh em pacIente acromegálIco

Context Artificial intelligence (AI), in particular machine learning (ML), may be used to deeply analyze biomarkers of response to first-generation somatostatin receptor ligands (fg-SRLs) in the treatment of acromegaly. Aim To develop a prediction model of therapeutic response of acromegaly to fg-SRL. Methods Patients with acromegaly not cured by primary surgical treatment and who had adjuvant therapy with fg-SRL for at least 6 months after surgery were included. Patients were considered controlled if they presented GH < 1.0 ng/mL and normal age-adjusted IGF-I levels. Six AI models were evaluated: logistic regression, k-nearest neighbor classifier, support vector machine, gradient-boosted classifier, random forest and multilayer perceptron. The features included in the analysis were age at diagnosis, sex, GH and IGF-I levels at diagnosis and at pretreatment, somatostatin receptor subtype 2 and 5 (SST2 and SST5) protein expression and cytokeratin granulation pattern (GP). Results A total of 153 patients were analyzed. Controlled patients were older (p = 0.002), had lower GH at diagnosis (p = 0.01), had lower pretreatment GH and IGF-I (p < 0.001), and more frequently harbored tumors that were densely granulated (p = 0.014) or highly expressed SST2 (p < 0.001).The model that performed best was the support vector machine with the features SST2, SST5, GP, sex, age, and pretreatment GH and IGF-I levels. It had an accuracy of 86.3%, positive predictive value of 83.3% and negative predictive value of 87.5%. Conclusion We developed a ML-based prediction model with high accuracy that has the potential to improve medical management of acromegaly, optimize biochemical control, decrease long-term morbidities and mortality and reduce health services costs.

Objective: Investigate the therapeutic response of acromegaly patients to pegvisomant (PEGV) in a real-life, Brazilian multicenter study. Subjects and methods: Characteristics of acromegaly patients treated with PEGV were reviewed at diagnosis, just before and during treatment. All patients with at least two IGF-I measurements on PEGV were included. Efficacy was defined as any normal IGF-I measurement during treatment. Safety data were reviewed. Predictors of response were determined by comparing controlled versus uncontrolled patients. Results: 109 patients [61 women; median age at diagnosis 34 years; 95.3% macroadenomas] from 10 Brazilian centers were studied. Previous treatment included surgery (89%), radiotherapy (34%), somatostatin receptor ligands (99%), and cabergoline (67%). Before PEGV, median levels of GH, IGF-I and IGF-I % of upper limit of normal were 4.3 µg/L, 613 ng/mL, and 209%, respectively. Pre-diabetes/diabetes was present in 48.6% and tumor remnant in 71% of patients. Initial dose was 10 mg/day in all except 4 cases, maximum dose was 30 mg/day, and median exposure time was 30.5 months. PEGV was used as monotherapy in 11% of cases. Normal IGF-I levels was obtained in 74.1% of patients. Glycemic control improved in 56.6% of patients with pre-diabetes/diabetes. Exposure time, pre-treatment GH and IGF-I levels were predictors of response. Tumor enlargement occurred in 6.5% and elevation of liver enzymes in 9.2%. PEGV was discontinued in 6 patients and 3 deaths unrelated to the drug were reported. Conclusions: In a real-life scenario, PEGV is a highly effective and safe treatment for acromegaly patients not controlled with other therapies.

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