Evidence map›Paper›PMID 38911587›Full record

ArticleFrontiers in neurology2024

Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice.

Melike Mut, Miaomiao Zhang, Ishita Gupta, P Thomas Fletcher, Faraz Farzad, Divine Nwafor

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
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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.

Melike MutDepartment of Neurosurgery, University of Virginia, Charlottesville, VA, United States.
Miaomiao ZhangDepartment of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States.
Ishita GuptaDepartment of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States.
P Thomas FletcherDepartment of Electrical and Computer Engineering, Department of Computer Science, University of Virginia, Charlottesville, VA, United States.
Faraz FarzadDepartment of Neurosurgery, University of Virginia, Charlottesville, VA, United States.
Divine NwaforDepartment of Neurosurgery, University of Virginia, Charlottesville, VA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgical decision-making for glioblastoma poses significant challenges due to its complexity and variability. This study investigates the potential of artificial intelligence (AI) tools in improving "decision-making processes" for glioblastoma surgery. A systematic review of literature identified 10 relevant studies, primarily focused on predicting resectability and surgery-related neurological outcomes. AI tools, especially rooted in radiomics and connectomics, exhibited promise in predicting resection extent through precise tumor segmentation and tumor-network relationships. However, they demonstrated limited effectiveness in predicting postoperative neurological due to dynamic and less quantifiable nature of patient-related factors. Recognizing these challenges, including limited datasets and the interpretability requirement in medical applications, underscores the need for standardization, algorithm optimization, and addressing variability in model performance and then further validation in clinical settings. While AI holds potential, it currently does not possess the capacity to emulate the nuanced decision-making process utilized by experienced neurosurgeons in the comprehensive approach to glioblastoma surgery.

Indexed as

artificial intelligenceconnectomicsdeep learningglioblastomamachine learningresectionsurgical decision making

Identifiers

PMID38911587
PMCPMC11191873

What OpenQuestion holds

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LicenceCC BY
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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.