Evidence map›Paper›PMID 39133946›Full record

ArticleJournal of neurosurgery. Case lessons2024

Collision of pituitary neuroendocrine tumor and anterior clinoid meningioma treated with a two-stage minimally invasive surgical strategy: illustrative case.

Shintaro Inoue, Hirotaka Hasegawa, Satoshi Koizumi, Motoyuki Umekawa, Naoyuki Shono, Kenji Kondo, Nobuhito Saito

Abstract read
In one paragraph

Article in Journal of neurosurgery. Case lessons, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

7 authors.

Shintaro InoueDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.
Hirotaka HasegawaDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.
Satoshi KoizumiDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.
Motoyuki UmekawaDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.
Naoyuki ShonoDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.
Kenji KondoDepartments of Otorhinolaryngology, The University of Tokyo, Tokyo, Japan.
Nobuhito SaitoDepartments of Neurosurgery, The University of Tokyo, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe locations of pituitary neuroendocrine tumor (PitNET) and anterior clinoid meningioma (ACM) appear to be very close but essentially different, as the former is inside the sella turcica while the latter is in the intradural space. A collision tumor thus requires a specific treatment strategy but has been rarely reported. OBSERVATIONS: A 77-year-old woman presented with progressive visual impairment due to significant compression of the optic apparatus by a collision of PitNET and ACM. With a minimally invasive two-stage therapeutic strategy, the ACM was first resected using an endoscopic mini-pterional approach, then the PitNET was removed with an endoscopic transnasal approach 8 weeks later, leading to significant improvement in visual function. The authors' literature search identified only 2 documented cases of a similar collision. LESSONS: This case underscores the complexity of the decision-making process in the management of collision tumors and suggests the potential benefit of staged minimally invasive surgical interventions in preserving visual function. Further accumulation of cases is warranted for refining the treatment strategy. https://thejns.org/doi/10.3171/CASE24240.

Indexed as

case reportcollision tumormeningiomapituitary neuroendocrine tumorvision loss

Identifiers

PMID39133946
PMCPMC11323851

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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.