MR imaging of the pituitary gland in central precocious puberty

1992 ◽  
Vol 22 (7) ◽  
pp. 481-484 ◽  
Author(s):  
S. C. S. Kao ◽  
J. S. Cook ◽  
J. R. Hansen ◽  
T. M. Simonson
1994 ◽  
Vol 162 (5) ◽  
pp. 1167-1173 ◽  
Author(s):  
M J Sharafuddin ◽  
A Luisiri ◽  
L R Garibaldi ◽  
D L Fulk ◽  
J B Klein ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Claudio Giacomozzi ◽  
Lisa Nicolì ◽  
Carlo Sozzi ◽  
Enrico Piovan ◽  
Mohamad Maghnie

IntroductionMagnetic Resonance Imaging (MRI) is the best approach to investigate the hypothalamic-pituitary region in children with central precocious puberty (CPP). Routine scanning is controversial in girls aged 6-8 year, due to the overwhelming prevalence of idiopathic forms and unrelated incidentalomas. Cerebral lipomas are rare and accidental findings, not usually expected in CPP. We report a girl with CPP and an unusually shaped posterior pituitary gland on SE-T1w sequences.Case DescriptionA 7.3-year-old female was referred for breast development started at age 7. Her past medical history and physical examination were unremarkable, apart from the Tanner stage 2 breast. X-ray of the left-hand revealed a bone age 2-years ahead of her chronological age, projecting her adult height prognosis below the mid parental height. LHRH test and pelvic ultrasound were suggestive for CPP. Routine brain MRI sequences, SE T1w and TSE T2w, showed the posterior pituitary bright spot increased in size and stretched upward. The finding was considered as an anatomical variant, in an otherwise normal brain imaging. Patient was started on treatment with GnRH analogue. At a thorough revaluation, imaging overlap with adipose tissue was suspected and a new MRI scan with 3D-fat-suppression T1w-VIBE sequences demonstrated a lipoma of the tuber cinereum, bordering a perfectly normal neurohypophysis. 3D-T2w-SPACE sequences, acquired at first MRI scan, would have provided a more correct interpretation if rightly considered.ConclusionThis is the first evidence, to our knowledge, of a cerebral lipoma mimicking pituitary gland abnormalities. Our experience highlights the importance of considering suprasellar lipomas in the MRI investigation of children with CPP, despite their rarity, should the T1w sequences show an unexpected pituitary shape. 3D-T2w SPACE sequences could be integrated into standard ones, especially when performing MRI routinely, to avoid potential misinterpretations.


2000 ◽  
Vol 30 (7) ◽  
pp. 444-446
Author(s):  
J. T. Van Beek ◽  
M. J. A. Sharafuddin ◽  
S. C. S. Kao ◽  
A. Luisiri ◽  
L. R. Garibaldi

2004 ◽  
Vol 112 (S 1) ◽  
Author(s):  
C Maier ◽  
M Riedl ◽  
M Clodi ◽  
C Bieglmayer ◽  
V Mlynarik ◽  
...  

2014 ◽  
Author(s):  
Elizabeth Shepherd ◽  
Leena Patel ◽  
Indi Banerjee ◽  
Peter Clayton ◽  
Sarah Ehtisham ◽  
...  

Author(s):  
Wannes S ◽  
Elmaleh-Berges M ◽  
Simon D ◽  
Zenaty D ◽  
Martinerie L ◽  
...  

2018 ◽  
Author(s):  
Liyan Pan ◽  
Guangjian Liu ◽  
Xiaojian Mao ◽  
Huixian Li ◽  
Jiexin Zhang ◽  
...  

BACKGROUND Central precocious puberty (CPP) in girls seriously affects their physical and mental development in childhood. The method of diagnosis—gonadotropin-releasing hormone (GnRH)–stimulation test or GnRH analogue (GnRHa)–stimulation test—is expensive and makes patients uncomfortable due to the need for repeated blood sampling. OBJECTIVE We aimed to combine multiple CPP–related features and construct machine learning models to predict response to the GnRHa-stimulation test. METHODS In this retrospective study, we analyzed clinical and laboratory data of 1757 girls who underwent a GnRHa test in order to develop XGBoost and random forest classifiers for prediction of response to the GnRHa test. The local interpretable model-agnostic explanations (LIME) algorithm was used with the black-box classifiers to increase their interpretability. We measured sensitivity, specificity, and area under receiver operating characteristic (AUC) of the models. RESULTS Both the XGBoost and random forest models achieved good performance in distinguishing between positive and negative responses, with the AUC ranging from 0.88 to 0.90, sensitivity ranging from 77.91% to 77.94%, and specificity ranging from 84.32% to 87.66%. Basal serum luteinizing hormone, follicle-stimulating hormone, and insulin-like growth factor-I levels were found to be the three most important factors. In the interpretable models of LIME, the abovementioned variables made high contributions to the prediction probability. CONCLUSIONS The prediction models we developed can help diagnose CPP and may be used as a prescreening tool before the GnRHa-stimulation test.


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