SynthesisWorld neurosurgery2017
Metabolic In Vivo Visualization of Pituitary Adenomas: a Systematic Review of Imaging Modalities.
Synthesis in World neurosurgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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.
Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Systematic review of pituitary gland and pituitary adenoma automatic segmentation techniques in magnetic resonance imaging.Frontiers in radiology · 2026Review
- Challenges and opportunities for new intraoperative optical techniques in the surgical treatment of pituitary adenomas: a review.Journal of biomedical optics · 2025Review
- Utility of F18-FDG PET/CT in the Evaluation of Pituitary Uptake.World journal of nuclear medicine · 2024Article
- Optoacoustic imaging in endocrinology and metabolism.Nature reviews. Endocrinology · 2021Review
- Case Report and Literature Review: Ectopic Thyrotropin-Secreting Pituitary Adenoma in the Suprasellar Region.Frontiers in endocrinology · 2021Review
- Elevated levels of circulating betahydroxybutyrate in pituitary tumor patients may differentiate prolactinomas from other immunohistochemical subtypes.Scientific reports · 2020Article
- [Neuroendocrine tumors : Classification, clinical presentation and imaging].Der Radiologe · 2019Review
- Ex VivoScientific reports · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectivePituitary adenomas (PAs) are the most common intrasellar mass. Functional PAs constitute most of pituitary tumors and can produce symptoms related to hormonal overproduction. Timely and accurate detection is therefore of vital importance to prevent potentially irreversible sequelae. Magnetic resonance imaging is the gold standard for detecting PAs, but is limited by poor sensitivity for microadenomas and an inability to differentiate scar tissue from tumor residual or predict treatment response. Several new modalities that detect PAs have been proposed.
methodsA systematic review of the PubMed database was performed for imaging studies of PAs since its inception. Data concerning study characteristics, clinical symptoms, imaging modalities, and diagnostic accuracy were collected.
resultsAfter applying exclusion criteria, 25 studies of imaging PAs using positron emission tomography (PET), magnetic resonance spectroscopy (MRS), and single photon emission computed tomography were reviewed. PET reliably detects PAs, particularly where magnetic resonance imaging is equivocal, although its efficacy is limited by high cost and low availability. Single photon emission computed tomography possesses good sensitivity for neuroendocrine tumors but its use with PAs is poorly documented. MRS consistently detects cellular proliferation and hormonal activity, but warrants further study at higher magnetic field strength.
conclusionsPET and MRS appear to have the strongest predictive value in detecting PAs. MRS has the advantage of low cost, but the literature is lacking in specific studies of the pituitary. Due to high recurrence rates of functional PAs and low sensitivity of existing diagnostic workups, further investigation of metabolic imaging is necessary.
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Registered trials
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.