Evidence map›Paper›PMID 35574188›Full record

ArticlePhotoacoustics2022

Quantification of vascular networks in photoacoustic mesoscopy.

Emma L Brown, Thierry L Lefebvre, Paul W Sweeney, Bernadette J Stolz, Janek Gröhl, Lina Hacker, Ziqiang Huang, Dominique-Laurent Couturier, Heather A Harrington, Helen M Byrne and 1 more

Abstract read
In one paragraph

Article in Photoacoustics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

  1. Article
  2. Early Radiation Therapy Response Assessment Using Multi-Scale Photoacoustic Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
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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

11 authors.

Emma L BrownDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
Thierry L LefebvreDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
Paul W SweeneyDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
Bernadette J StolzMathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK.
Janek GröhlDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
Lina HackerDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.
Ziqiang HuangCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK.
Dominique-Laurent CouturierCancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge CB2 0RE, UK.
Heather A HarringtonMathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK.
Helen M ByrneMathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK.
Sarah E BohndiekDepartment of Physics, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK.

Funding

Cancer Research UK 16267Cancer Research UK 21142Cancer Research UK 28870Wellcome Trust
6 · The paper itself

Abstract

Mesoscopic photoacoustic imaging (PAI) enables non-invasive visualisation of tumour vasculature. The visual or semi-quantitative 2D measurements typically applied to mesoscopic PAI data fail to capture the 3D vessel network complexity and lack robust ground truths for assessment of accuracy. Here, we developed a pipeline for quantifying 3D vascular networks captured using mesoscopic PAI and tested the preservation of blood volume and network structure with topological data analysis. Ground truth data of

Indexed as

Photoacoustic imagingSegmentationTopologyVasculature

Identifiers

PMID35574188
PMCPMC9095888

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.