Evidence map›Paper›PMID 36993142›Full record

ArticleJournal of biomedical optics2023

Enhancing intraoperative tumor delineation with multispectral short-wave infrared fluorescence imaging and machine learning.

Dale J Waterhouse, Laura Privitera, John Anderson, Danail Stoyanov, Stefano Giuliani

Full text read
In one paragraph

Article in Journal of biomedical optics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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

10 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
  9. Article
  10. 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.

Dale J WaterhouseUniversity College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom.ORCID 0000-0001-5530-6857
Laura PriviteraUniversity College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom.
John AndersonUCL Great Ormond Street Institute of Child Health, Cancer Section, Developmental Biology and Cancer Programme, London, United Kingdom.
Danail StoyanovUniversity College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom.ORCID 0000-0002-0980-3227
Stefano GiulianiUniversity College London, Wellcome, EPSRC Centre for Interventional and Surgical Sciences, London, United Kingdom.

Funding

Medical Research Council MR/T005491/1Wellcome Trust 203145Z/16/ZWellcome Trust 222111/Z/20/Z
6 · The paper itself

Abstract

Significance: Fluorescence-guided surgery (FGS) provides specific real-time visualization of tumors, but intensity-based measurement of fluorescence is prone to errors. Multispectral imaging (MSI) in the short-wave infrared (SWIR) has the potential to improve tumor delineation by enabling machine-learning classification of pixels based on their spectral characteristics. Aim: Determine whether MSI can be applied to FGS and combined with machine learning to provide a robust method for tumor visualization. Approach: A multispectral SWIR fluorescence imaging device capable of collecting data from six spectral filters was constructed and deployed on neuroblastoma (NB) subcutaneous xenografts ( Results: The spectra of tumor and non-tumor tissue were subtly different and conserved between individuals. In classification, a combine principal component analysis and Conclusions: The development of dozens of new imaging agents provides a timely opportunity for multispectral SWIR imaging to revolutionize next-generation FGS.

Indexed as

NeoplasmsFluorescent DyesHumansMachine LearningNeural Networks, ComputerOptical ImagingFluorescent Dyescancerfluorescence-guided surgerymachine-learningmultispectralneuroblastomashort-wave infrared

Identifiers

PMID36993142
PMCPMC10042297

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read16
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.