Evidence map›Paper›PMID 31609977›Full record

ArticlePloS one2019

An innovative non-invasive technique for subcutaneous tumour measurements.

Juan Delgado-SanMartin, Beate Ehrhardt, Marcin Paczkowski, Sean Hackett, Andrew Smith, Wajahat Waraich, James Klatzow, Adeala Zabair, Anna Chabokdast, Leonardo Rubio-Navarro and 2 more

Abstract read
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Review
  2. Harnessing HfOACS nano · 2024
    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

12 authors.

Juan Delgado-SanMartinFuel3D, Oxford Science Park, Oxford, England, United Kingdom.ORCID 0000-0001-7247-0667
Beate EhrhardtAstraZeneca IMED Biotech Unit, Discovery Sciences, Cambridge Science Park, Cambridge, England, United Kingdom.
Marcin PaczkowskiFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Sean HackettFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Andrew SmithFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Wajahat WaraichFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
James KlatzowFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Adeala ZabairFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Anna ChabokdastFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Leonardo Rubio-NavarroFuel3D, Oxford Science Park, Oxford, England, United Kingdom.
Amar RahiAstraZeneca IMED Biotech Unit, Oncology, Alderley Park, Macclesfield, England, United Kingdom.
Zena WilsonAstraZeneca IMED Biotech Unit, Oncology, Alderley Park, Macclesfield, England, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionIn oncological drug development, animal studies continue to play a central role in which the volume of subcutaneous tumours is monitored to assess the efficacy of new drugs. The tumour volume is estimated by taking the volume to be that of a regular spheroid with the same dimensions. However, this method is subjective, insufficiently traceable, and is subject to error in the accuracy of volume estimates as tumours are frequently irregular. METHODS &

resultsThis paper reviews the standard technique for tumour volume assessment, calliper measurements, by conducting a statistical review of a large dataset consisting of 2,500 tumour volume measurements from 1,600 mice by multiple operators across 6 mouse strains and 20 tumour models. Additionally, we explore the impact of six different tumour morphologies on volume estimation and the detection of treatment effects using a computational tumour growth model. Finally, we propose an alternative method to callipers for estimating volume-BioVolumeTM, a 3D scanning technique. BioVolume simultaneously captures both stereo RGB (Red, Green and Blue) images from different light sources and infrared thermal images of the tumour in under a second. It then detects the tumour region automatically and estimates the tumour volume in under a minute. Furthermore, images can be processed in parallel within the cloud and so the time required to process multiple images is similar to that required for a single image. We present data of a pre-production unit test consisting of 297 scans from over 120 mice collected by four different operators.

conclusionThis work demonstrates that it is possible to record tumour measurements in a rapid minimally invasive, morphology-independent way, and with less human-bias compared to callipers, whilst also improving data traceability. Furthermore, the images collected by BioVolume may be useful, for example, as a source of biomarkers for animal welfare and secondary drug toxicity / efficacy.

Indexed as

Image Processing, Computer-AssistedTumor BurdenAnimalsHumansMiceNeoplasms, Experimental

Identifiers

PMID31609977
PMCPMC6791540

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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.