Evidence map›Paper›PMID 37207359›Full record

ArticleEndocrine reviews2023

Current and Future Advances in Surgical Therapy for Pituitary Adenoma.

Danyal Z Khan, John G Hanrahan, Stephanie E Baldeweg, Neil L Dorward, Danail Stoyanov, Hani J Marcus

Open access · hybridAbstract read
In one paragraph

Article in Endocrine reviews, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
9.9field-weighted citation impact, top 1% of its field
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

34 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. SurgAnt-ViVQA: learning to anticipate surgical events through GRU-driven temporal cross-attention.International journal of computer assisted radiology and surgery · 2026
    Article
  4. Review
  5. SurgViVQA: temporally grounded video question answering for surgical scene understanding.International journal of computer assisted radiology and surgery · 2026
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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

6 authors at 4 institutions in 1 country.

Danyal Z KhanDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.ORCID 0000-0001-9213-2550
John G HanrahanDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.ORCID 0000-0003-4584-2298
Stephanie E BaldewegDepartment of Diabetes & Endocrinology, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK.ORCID 0000-0002-8381-1612
Neil L DorwardDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.ORCID 0000-0001-8103-5579
Danail StoyanovWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TY, UK.ORCID 0000-0002-0980-3227
Hani J MarcusDepartment of Neurosurgery, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK.ORCID 0000-0001-8000-392X
Wellcome / EPSRC Centre for Interventional and Surgical Sciences · GBMedtronic (United Kingdom) · GBNational Hospital for Neurology and Neurosurgery · GBUniversity College London Hospitals NHS Foundation Trust · GB

Funding

Wellcome TrustWellcome Trust 203145Z/16/Z
6 · The paper itself

Abstract

The vital physiological role of the pituitary gland, alongside its proximity to critical neurovascular structures, means that pituitary adenomas can cause significant morbidity or mortality. While enormous advancements have been made in the surgical care of pituitary adenomas, numerous challenges remain, such as treatment failure and recurrence. To meet these clinical challenges, there has been an enormous expansion of novel medical technologies (eg, endoscopy, advanced imaging, artificial intelligence). These innovations have the potential to benefit each step of the patient's journey, and ultimately, drive improved outcomes. Earlier and more accurate diagnosis addresses this in part. Analysis of novel patient data sets, such as automated facial analysis or natural language processing of medical records holds potential in achieving an earlier diagnosis. After diagnosis, treatment decision-making and planning will benefit from radiomics and multimodal machine learning models. Surgical safety and effectiveness will be transformed by smart simulation methods for trainees. Next-generation imaging techniques and augmented reality will enhance surgical planning and intraoperative navigation. Similarly, surgical abilities will be augmented by the future operative armamentarium, including advanced optical devices, smart instruments, and surgical robotics. Intraoperative support to surgical team members will benefit from a data science approach, utilizing machine learning analysis of operative videos to improve patient safety and orientate team members to a common workflow. Postoperatively, neural networks leveraging multimodal datasets will allow early detection of individuals at risk of complications and assist in the prediction of treatment failure, thus supporting patient-specific discharge and monitoring protocols. While these advancements in pituitary surgery hold promise to enhance the quality of care, clinicians must be the gatekeepers of the translation of such technologies, ensuring systematic assessment of risk and benefit prior to clinical implementation. In doing so, the synergy between these innovations can be leveraged to drive improved outcomes for patients of the future.

Indexed as

AdenomaPituitary NeoplasmsArtificial IntelligenceEndoscopyHumansartificial intelligenceaugmented realitydigital healthpituitary adenomaroboticstranssphenoidal

Identifiers

PMID37207359
PMCPMC10502574
OpenAlexW4377087187

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.