Evidence map›Paper›PMID 33739453›Full record

SynthesisInternational journal of cancer2021

Novel imaging techniques for intraoperative margin assessment in surgical oncology: A systematic review.

Jan Heidkamp, Mirre Scholte, Camiel Rosman, Srirang Manohar, Jurgen J Fütterer, Maroeska M Rovers

Open access · hybridAbstract readSystematic Review
In one paragraph

Synthesis in International journal of cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed, 2 pooled it
3.2field-weighted citation impact, top 8% 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

35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 58 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  6. Article
  7. Review
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  9. Article
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  13. Article
  14. Surgical tooltip localization via concentric nested square markers and depth-RGB multi-coordinate fusion.International journal of computer assisted radiology and surgery · 2025
    Article
  15. Article
  16. Article
  17. Article
  18. Breast tissue imaging atlas using ultra-fast confocal microscopy to identify cancer lesions.Virchows Archiv : an international journal of pathology · 2025
    Article
  19. Review
  20. Current clinical applications of Cerenkov luminescence for intraoperative molecular imaging.European journal of nuclear medicine and molecular imaging · 2024
    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

6 authors at 2 institutions in 1 country.

Jan HeidkampDepartment of Radiology and Nuclear Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.ORCID 0000-0002-3335-1486
Mirre ScholteDepartment of Operating Rooms, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Camiel RosmanDepartment of Surgery, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Srirang ManoharMulti-Modality Medical Imaging group, Technical Medical Center, University of Twente, Enschede, The Netherlands.
Jurgen J FüttererDepartment of Radiology and Nuclear Medicine, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Maroeska M RoversDepartment of Operating Rooms, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
Radboud University Nijmegen · NLUniversity of Twente · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inadequate margins continue to occur frequently in patients who undergo surgical resection of a tumor, suggesting that current intraoperative methods are not sufficiently reliable in determining the margin status. This clinical demand has inspired the development of many novel imaging techniques that could help surgeons with intraoperative margin assessment. This systematic review provides an overview of novel imaging techniques for intraoperative margin assessment in surgical oncology, and reports on their technical properties, feasibility in clinical practice and diagnostic accuracy. PubMed, Embase, Web of Science and the Cochrane library were systematically searched (2013-2018) for studies reporting on imaging techniques for intraoperative margin assessment. Patient and study characteristics, technical properties, feasibility characteristics and diagnostic accuracy were extracted. This systematic review identified 134 studies that investigated and developed 16 groups of techniques for intraoperative margin assessment: fluorescence, advanced microscopy, ultrasound, specimen radiography, optical coherence tomography, magnetic resonance imaging, elastic scattering spectroscopy, bio-impedance, X-ray computed tomography, mass spectrometry, Raman spectroscopy, nuclear medicine imaging, terahertz imaging, photoacoustic imaging, hyperspectral imaging and pH measurement. Most studies were in early developmental stages (IDEAL 1 or 2a, n = 98); high-quality stage 2b and 3 studies were rare. None of the techniques was found to be clearly superior in demonstrating high feasibility as well as high diagnostic accuracy. In conclusion, the field of imaging techniques for intraoperative margin assessment is highly evolving. This review provides a unique overview of the opportunities and limitations of the currently available imaging techniques.

Indexed as

Margins of ExcisionSurgical OncologyHumansMagnetic Resonance ImagingNeoplasmsPrognosisTomography, X-Ray ComputedUltrasonographycancermedical imagingsurgical marginsurgical oncologysystematic review

Identifiers

PMID33739453
PMCPMC8252509
OpenAlexW3137822055

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.