Evidence map›Paper›PMID 23093486›Full record

ArticleInsights into imaging2012

Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Fergus Davnall, Connie S P Yip, Gunnar Ljungqvist, Mariyah Selmi, Francesca Ng, Bal Sanghera, Balaji Ganeshan, Kenneth A Miles, Gary J Cook, Vicky Goh

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06370234 (The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.), which is not on this map. Cited by 500 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
500citing papers in PubMed, 10 pooled it
–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.

NCT06370234 nacompletednot on this mapstarted 2015, after this paper: background citation

The Prediction Model of Neoadjuvant Chemotherapy Response for Breast Cancer Based on The Parametric Dynamics Features of The Pretreatment and Early-Treatment MR-PET and QDS-IR Images.

TypeinterventionalSponsorNational Taiwan University HospitalRan2015 to 2020Enrolled60ConditionsBreast Cancer, Chemotherapy Effect, Diffusion Weighted MRI, PET ImagingArmsWhole body 18F-FDG Positron Emission Tomography
3 · Its place in the literature

Who cites it

500 citing papers in PubMed, 10 syntheses or guidelines pooled it.

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440 more citing papers are in PubMed but not listed here.

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

10 authors.

Fergus DavnallDivision of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.
Connie S P Yip
Gunnar Ljungqvist
Mariyah Selmi
Francesca Ng
Bal Sanghera
Balaji Ganeshan
Kenneth A Miles
Gary J Cook
Vicky Goh

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTumor spatial heterogeneity is an important prognostic factor, which may be reflected in medical images

methodsImage texture analysis is an approach of quantifying heterogeneity that may not be appreciated by the naked eye. Different methods can be applied including statistical-, model-, and transform-based methods.

resultsEarly evidence suggests that texture analysis has the potential to augment diagnosis and characterization as well as improve tumor staging and therapy response assessment in oncological practice.

conclusionThis review provides an overview of the application of texture analysis with different imaging modalities, CT, MRI, and PET, to date and describes the technical challenges that have limited its widespread clinical implementation so far. With further efforts to refine its application, image texture analysis has the potential to develop into a valuable clinical tool for oncologic imaging. TEACHING POINTS : • Tumor spatial heterogeneity is an important prognostic factor. • Image texture analysis is an approach of quantifying heterogeneity. • Different methods can be applied, including statistical-, model-, and transform-based methods. • Texture analysis could improve the diagnosis, tumor staging, and therapy response assessment.

Identifiers

PMID23093486
PMCPMC3505569

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Registered trials

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.