Evidence map›Paper›PMID 41675766›Full record

ArticleAnnals of medicine and surgery (2012)2026

Augmented reality tumor mapping: eliminating positive margins in oncosurgery.

Muhammad Khizar, Muhammad Zaib, Savera Ejaz Ahmed, Hasiba Karimi, Hasibullah Aminpoor

Abstract readLetter
In one paragraph

Article in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Muhammad KhizarFaculty of Medicine, Georgian American University, Tbilisi, Georgia.ORCID https://orcid.org/0009-0005-5229-5052
Muhammad ZaibFaculty of Medicine, Georgian American University, Tbilisi, Georgia.ORCID https://orcid.org/0009-0008-7675-7161
Savera Ejaz AhmedDepartment of Medicine, Shaheed Mohtarma Benazir Bhutto Medical College, Lyari, Karachi, Pakistan.ORCID https://orcid.org/0009-0009-4789-7154
Hasiba KarimiFaculty of Medicine, Bezmialem Vakif University, Istanbul, Turkey.ORCID https://orcid.org/0000-0002-7618-3849
Hasibullah AminpoorFaculty of Medicine, Kabul University of Medical Sciences "Abu Ali Ibn Sina", Kabul, Afghanistan.ORCID https://orcid.org/0000-0003-1817-7800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Augmented reality (AR) is transforming cancer surgery by combining medical imaging with real-time visualization during operations. Using 3D digital models of tumors, AR allows surgeons to see the precise location and borders of cancers directly on the patient. Early studies in breast, brain, urologic, and skin cancers show that AR improves accuracy and helps surgeons remove tumors more completely and safely. Research from China and South Korea has demonstrated accurate margin identification in skin and breast tumor surgeries, while teams in Italy and Belgium have reported better surgical planning and shorter operation times in prostate and brain tumor procedures. AR is also being used to train surgeons in low-resource settings, improving access to advanced surgical methods worldwide. Positive surgical margins, which are associated with increased local recurrence rates and reduced survival, highlight the critical need for accurate intraoperative tumor visualization across tumor types. Although more clinical trials are needed to confirm its ability to reduce positive margins, AR-guided tumor mapping shows great promise as a standard tool to support safer, more precise oncologic surgery.

Indexed as

cancermedical mappingoncologic surgeryprecision medicinetumor

Identifiers

PMID41675766
PMCPMC12889559

What OpenQuestion holds

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Registered trials

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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.