Evidence map›Paper›PMID 28863161›Full record

ArticlePloS one2017

Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.

Georgios C Manikis, Kostas Marias, Doenja M J Lambregts, Katerina Nikiforaki, Miriam M van Heeswijk, Frans C H Bakers, Regina G H Beets-Tan, Nikolaos Papanikolaou

Erratum issuedAbstract readComparative Study
In one paragraph

Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Georgios C ManikisFoundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational Biomedicine Lab, Heraklion, Greece.ORCID http://orcid.org/0000-0002-3396-0644
Kostas MariasFoundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational Biomedicine Lab, Heraklion, Greece.
Doenja M J LambregtsDepartment of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
Katerina NikiforakiFoundation for Research and Technology - Hellas (FORTH), Institute of Computer Science, Computational Biomedicine Lab, Heraklion, Greece.
Miriam M van HeeswijkDepartment of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
Frans C H BakersGROW School for Oncology and Developmental Biology - Maastricht University Medical Centre+, Maastricht, The Netherlands.
Regina G H Beets-TanDepartment of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
Nikolaos PapanikolaouClinical Computational Imaging Group, Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal.ORCID http://orcid.org/0000-0003-3298-2072

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer. MATERIAL AND

methodsNineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio.

resultsAll candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.

conclusionNo single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior.

Indexed as

AdenocarcinomaAgedAged, 80 and overDiffusion Magnetic Resonance ImagingFemaleHumansImage Processing, Computer-AssistedMaleMiddle AgedModels, StatisticalNormal DistributionRectal Neoplasms

Identifiers

PMID28863161
PMCPMC5593499

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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.