Evidence map›Paper›PMID 40597183›Full record

SynthesisBMC endocrine disorders2025

Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis.

Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy and 1 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC endocrine disorders, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
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

11 authors.

Ibrahim Mohammadzadeh *Skull Base Research Center, Loghman-Hakim Hospital,, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Ibrahim.mdz7777@gmail.com.ORCID http://orcid.org/0000-0002-8862-0778
Bardia Hajikarimloo *Department of Neurological Surgery, University of Virginia, Charlottesville, VA, USA.
Behnaz NiroomandSkull Base Research Center, Loghman-Hakim Hospital,, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0001-5184-7445
Nasira FaiziSchool of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0009-0004-1266-416X
Pooya EiniToxicological Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0002-9457-8588
Mohammad Amin HabibiDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.ORCID http://orcid.org/0000-0001-7600-6925
Alireza MohseniClinical Research Development Unit of Torfe Medical Center, Shahid Beheshti University of Medical Science, Tehran, Iran.ORCID http://orcid.org/0009-0005-8975-622X
Mohammadmahdi SabahiDepartment of Neurological Surgery, Pauline Braathen Neurological Center, Cleveland Clinic Florida, Weston, FL, USA.
Abdulrahman AlbakrDepartment of Neurological Surgery, Pauline Braathen Neurological Center, Cleveland Clinic Florida, Weston, FL, USA.
Michael KarsyDepartment of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.ORCID http://orcid.org/0000-0002-0422-7937
Hamid Borghei-RazaviDepartment of Neurological Surgery, Pauline Braathen Neurological Center, Cleveland Clinic Florida, Weston, FL, USA. borgheh2@ccf.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPredicting pituitary adenoma (PA) recurrence after surgical resection is critical for guiding clinical decision-making, and machine learning (ML) based models show great promise in improving the accuracy of these predictions. These models can provide valuable insights to surgeons and oncologists, helping them tailor personalized treatment plans, enhance patient prognostication, and optimize follow-up strategies.

methodsWe systematically searched PubMed, Scopus, Embase, Cochrane Library, and Web of Science databases until November 2024, applying PRISMA guidelines.

resultsOut of 1240 studies screened, six met our eligibility criteria involving ML-based approaches to predict PA recurrence. The studies employed 12 different ML algorithms. Meta-analysis showed a pooled sensitivity of 0.87 [95% CI: 0.78-0.92], specificity of 0.86 [95% CI: 0.67-0.95], positive diagnostic likelihood ratio (DLR) of 6.32 [95% CI: 2.46-16.26], and negative DLR of 0.16 [95% CI: 0.1-0.25]. The diagnostic odds ratio (DOR) was 40.52 [95% CI: 13-126.27], and the diagnostic score was 3.7 [95% CI: 2.57-4.84]. The pooled AUC was 0.89 [95% CI: 0.86-0.92], indicating a high overall diagnostic performance. For the comparison between Logistic Regression (LR) and non-LR algorithms, LR-based algorithms exhibited numerically higher AUC and sensitivity; however, these differences were not statistically significant. Additionally, LR-based algorithms showed lower specificity, positive likelihood ratio, and diagnostic odds ratios, but the statistical tests did not provide strong evidence for meaningful differences.

conclusionAI-based models show strong predictive power for recurrence in both functional and non-functional pituitary adenomas, with an average accuracy above 80%. However, the lack of external validation and the complexity of input data pose challenges, highlighting the need for rigorous validation with multi-center datasets and standardized imaging techniques to enhance clinical applicability.

Indexed as

AdenomaMachine LearningNeoplasm Recurrence, LocalPituitary NeoplasmsHumansPrognosisArtificial intelligenceDeep learningMachine learningPituitary adenomaPredictorsRecurrence

Identifiers

PMID40597183
PMCPMC12219454

What OpenQuestion holds

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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.