Evidence map›Paper›PMID 39918630›Full record

Observational studyClinical neuroradiology2025

Pituitary Adenomas in Children: : Specific Imaging Features According to Hormonal Secretion.

Fares Kassem, Raphael Levy, Arnault Tauziède-Espariat, Charles-Joris Roux, Thomas Samoyeau, Alexis Ollitrault, Graziella Pinto, Dinane Samara-Boustani, Dulanjalee Kariyawasam, Michel Polak and 5 more

Abstract readComparative StudyObservational Study
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In one paragraph

Observational study in Clinical neuroradiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Fares KassemHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Raphael LevyHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Arnault Tauziède-EspariatNeuropathology Department, GHU Paris, Université Paris Cité, 1 rue Cabanis, 75014, Paris, France.
Charles-Joris RouxHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Thomas SamoyeauHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Alexis OllitraultHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Graziella PintoPediatric Endocrinology, Diabetology and Gynecology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Dinane Samara-BoustaniPediatric Endocrinology, Diabetology and Gynecology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Dulanjalee KariyawasamPediatric Endocrinology, Diabetology and Gynecology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Michel PolakPediatric Endocrinology, Diabetology and Gynecology Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Kevin BeccariaPediatric Neurosurgery Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Thomas BlauwblommePediatric Neurosurgery Department, AP-HP, Hôpital Universitaire Necker-Enfants Malades, 75015, Paris, France.
Pascale VarletNeuropathology Department, GHU Paris, Université Paris Cité, 1 rue Cabanis, 75014, Paris, France.
Nathalie BoddaertHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France.
Volodia Dangouloff-RosHôpital Universitaire Necker-Enfants Malades, Department of Pediatric Radiology, AP-HP, 149 rue de Sèvres, 75015, Paris, France. volodia.dangouloff-ros@aphp.fr.ORCID http://orcid.org/0000-0003-1260-5886

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePituitary adenomas are much rarer in children than in adults. We aimed to analyze their imaging characteristics in this age group and to compare them according to the hormonal secretion. We conducted an observational monocentric retrospective study on clinical and imaging data.

methodsWe analyzed imaging features before surgery or drug treatment of pituitary adenomas in children confirmed by histopathology or hormonal secretion. We assessed tumoral signal intensity, volume and aggressiveness, and compared it according to the hormonal secretion.

resultsWe included 31 children (13 lactotroph (42%), 8 corticotroph (26%), 5 somatotroph (16%), 5 plurihormonal adenomas (16%) including 1 non-secreting macroadenoma) with a median age of 13 years (range 2-16 years-old), without age or sex difference between secretion types. Lactotroph and somatotroph adenomas were larger than corticotroph adenomas (p = 0.007) and were more aggressive (p = 0.01). They also had higher signal intensity on T2-weighted images (p = 0.04). T1 signal intensity was similar between the groups on pre and post-contrast images (lower enhancement than the normal pituitary). No non-secreting micro-adenoma became clinically significant enough to lead to a pathological confirmation or specific treatment. Genetic research was conducted on 20 children, finding MEN1 mutations in 80% of the patients.

conclusionsPituitary adenomas in children are rare but should be considered when facing similar imaging features than in adults. Imaging characteristics may allow to suspect the hormonal secretion.

Indexed as

AdenomaMagnetic Resonance ImagingPituitary NeoplasmsAdolescentChildChild, PreschoolFemaleHumansMaleRetrospective StudiesAdenomaChildrenImagingMRIPituitary

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.