Purpose: Non-syndromic pituitary gigantism (PG) is a very rare disease. Aryl hydrocarbon receptor-interacting protein (AIP) and G protein-coupled receptor 101 (GPR101) genetic abnormalities represent important etiologic causes of PG and may account for up to 40% of these cases. Here, we aimed to characterize the clinical and molecular findings and long-term outcomes in 18 patients (15 males, three females) with PG followed at a single tertiary center in Sao Paulo, Brazil.
Methods: Genetic testing for AIP and GPR101 were performed by DNA sequencing, droplet digital PCR and array comparative genomic hybridization (aCGH).
Results: Pathogenic variants in the AIP gene were detected in 25% of patients, including a novel variant in splicing regulatory sequences which was present in a sporadic male case. X-LAG due to GPR101 microduplication was diagnosed in two female patients (12.5%). Of interest, these patients had symptoms onset by age 5 and 9 years old and diagnosis at 5 and 15 years, respectively. X-LAG, but not AIP, patients had a significantly lower age of symptoms onset and diagnosis and a higher height Z-score when compared to non-X-LAG. No other differences in clinical features and/or treatment outcomes were observed among PG based on their genetic background.
Conclusion: We characterize the clinical and molecular findings and long-term outcome of the largest single-center PG cohort described so far.
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http://dx.doi.org/10.1007/s11102-020-01105-4 | DOI Listing |
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Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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