Evidence map›Paper›PMID 38201571›Full record

ArticleCancers2023

Diffusion-Weighted MRI as a Quantitative Imaging Biomarker in Colon Tumors.

Peter Obel Otto, Martina Kastrup Loft, Søren Rafael Rafaelsen, Malene Roland Vils Pedersen

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

Peter Obel OttoDepartment of Radiology, University Hospital of Southern Denmark, Beriderbakken 4, 7100 Vejle, Denmark.
Martina Kastrup LoftDepartment of Radiology, University Hospital of Southern Denmark, Beriderbakken 4, 7100 Vejle, Denmark.
Søren Rafael RafaelsenDepartment of Radiology, University Hospital of Southern Denmark, Beriderbakken 4, 7100 Vejle, Denmark.ORCID 0000-0001-5182-5545
Malene Roland Vils PedersenDepartment of Radiology, University Hospital of Southern Denmark, Beriderbakken 4, 7100 Vejle, Denmark.ORCID 0000-0001-5167-7968
University of Southern Denmark · DK

Funding

This research was funded by Lillebaelt Research Council, Vejle Hospital, Denmark. This research was funded by Lillebaelt Research Council, Vejle Hospital, Denmark.
6 · The paper itself

Abstract

purposeTo assess the use of quantitative diffusion-weighted MRI (DW-MRI) as a diagnostic imaging biomarker in differentiating between benign colon adenoma, early, and advanced cancer of the colon, as well as predicting lymph node involvement, and finally comparing mucinous-producing colon cancer with adenomas and non-mucinous colon cancer.

methodPatients with a confirmed tumor on colonoscopy were eligible for inclusion in this study. Using a 3.0 Tesla MRI machine, the main tumor mean apparent diffusion coefficient (mADC) was obtained. Surgically resected tumor specimens served as an endpoint, except in mucinous colon cancers, which were classified based on T2 images.

resultsA total of 152 patients were included in the study population. The mean age was 71 years. A statistically significant mADC mean difference of -282 × 10

conclusionQuantitative DW-MRI is potentially useful for determining whether a colonic tumor is benign or malignant. Mucinous colon cancer shows less diffusion restriction when compared to non-mucinous colon cancer, a potential pitfall.

Indexed as

ADCadenomacolon cancerdiffusion weighted imagingDWIMRImucinous

Identifiers

PMID38201571
PMCPMC10778248
OpenAlexW4390266123

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.