Evidence map›Paper›PMID 26456145›Full record

ArticleBone2016

Osteolytica: An automated image analysis software package that rapidly measures cancer-induced osteolytic lesions in in vivo models with greater reproducibility compared to other commonly used methods.

H R Evans, T Karmakharm, M A Lawson, R E Walker, W Harris, C Fellows, I D Huggins, P Richmond, A D Chantry

Open access · hybridAbstract readComparative Study
In one paragraph

Article in Bone, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
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

9 authors at 1 institution in 1 country.

H R EvansSheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: h.r.evans@sheffield.ac.uk.
T KarmakharmDepartment of Computer Science, University of Sheffield, Mappin Street, Sheffield S1 4DP, UK. Electronic address: t.karmakharm@sheffield.ac.uk.
M A LawsonSheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: m.a.lawson@sheffield.ac.uk.
R E WalkerSheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: beccywalker@googlemail.com.
W HarrisSheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: Wilson_26_@hotmail.com.
C FellowsSheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: clairfellows@live.co.uk.
I D HugginsMellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK. Electronic address: mdp11idh@sheffield.ac.uk.
P RichmondDepartment of Computer Science, University of Sheffield, Mappin Street, Sheffield S1 4DP, UK; Insigneo Institute for In silico Medicine, The Pam Liversige Building, Sir Frederick Mappin Building, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. Electronic address: p.richmond@sheffield.ac.uk.
A D ChantrySheffield Myeloma Research Team, Department of Oncology, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Mellanby Centre for Bone Research, Medical School, University of Sheffield, Beech Hill Road, Sheffield S10 2RX, UK; Department of Haematology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Glossop Road, Sheffield S10 2JF, UK; Insigneo Institute for In silico Medicine, The Pam Liversige Building, Sir Frederick Mappin Building, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. Electronic address: a.d.chantry@sheffield.ac.uk.
University of Sheffield · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

Methods currently used to analyse osteolytic lesions caused by malignancies such as multiple myeloma and metastatic breast cancer vary from basic 2-D X-ray analysis to 2-D images of micro-CT datasets analysed with non-specialised image software such as ImageJ. However, these methods have significant limitations. They do not capture 3-D data, they are time-consuming and they often suffer from inter-user variability. We therefore sought to develop a rapid and reproducible method to analyse 3-D osteolytic lesions in mice with cancer-induced bone disease. To this end, we have developed Osteolytica, an image analysis software method featuring an easy to use, step-by-step interface to measure lytic bone lesions. Osteolytica utilises novel graphics card acceleration (parallel computing) and 3-D rendering to provide rapid reconstruction and analysis of osteolytic lesions. To evaluate the use of Osteolytica we analysed tibial micro-CT datasets from murine models of cancer-induced bone disease and compared the results to those obtained using a standard ImageJ analysis method. Firstly, to assess inter-user variability we deployed four independent researchers to analyse tibial datasets from the U266-NSG murine model of myeloma. Using ImageJ, inter-user variability between the bones was substantial (±19.6%), in contrast to using Osteolytica, which demonstrated minimal variability (±0.5%). Secondly, tibial datasets from U266-bearing NSG mice or BALB/c mice injected with the metastatic breast cancer cell line 4T1 were compared to tibial datasets from aged and sex-matched non-tumour control mice. Analyses by both Osteolytica and ImageJ showed significant increases in bone lesion area in tumour-bearing mice compared to control mice. These results confirm that Osteolytica performs as well as the current 2-D ImageJ osteolytic lesion analysis method. However, Osteolytica is advantageous in that it analyses over the entirety of the bone volume (as opposed to selected 2-D images), it is a more rapid method and it has less user variability.

Indexed as

Image Processing, Computer-AssistedSoftwareAnimalsAutomationBreast NeoplasmsCell Line, TumorDisease Models, AnimalFemaleHumansMaleMice, Inbred BALB CMiddle AgedNeoplasmsOsteolysisReproducibility of ResultsUser-Computer InterfaceBoneBreast cancerLesionMicroCTMultiple myeloma

Identifiers

PMID26456145
PMCPMC4720217
OpenAlexW1905883306

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.