Evidence map›Paper›PMID 40296332›Full record

ArticleNMR in biomedicine2025

Cluster Analysis of VERDICT MRI for Cancer Tissue Characterization in Neuroendocrine Tumors.

Lukas Lundholm, Mikael Montelius, Oscar Jalnefjord, Elin Schoultz, Eva Forssell-Aronsson, Maria Ljungberg

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NMR in biomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Lukas LundholmDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0003-4891-5907
Mikael MonteliusDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0001-9103-3446
Oscar JalnefjordDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.ORCID https://orcid.org/0000-0003-2741-5890
Elin SchoultzDepartment of Medical Biochemistry and Cell Biology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Eva Forssell-AronssonDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Maria LjungbergDepartment of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.

Funding

Assar Gabrielsson Cancer Research Foundation FB23-115Herbert & Karin Jacobsson Foundation 6/h21King Gustav V Jubilee Clinic Cancer Research Foundation 2022:424Sahlgrenska University Hospital Research Funds SU-984295Sahlgrenska University Hospital Research Funds SU-998331Swedish Cancer Society 21-1568PjSwedish Cancer Society 23 2975Swedish Cancer Society CAN20/1293Swedish Research Council 2021-02636Swedish state under the agreement between the Swedish government and the county councils-the ALF-agreement ALFGBG-942659Swedish state under the agreement between the Swedish government and the county councils-the ALF-agreement ALFGBG-965032Swedish state under the agreement between the Swedish government and the county councils-the ALF-agreement ALFGBG-966074
6 · The paper itself

Abstract

Diffusion MRI models accounting for varying diffusion times and high b-values, such as VERDICT, hold potential for non-invasively characterizing tumor tissue types, potentially enabling improved tumor grading, and treatment evaluation. Furthermore, cluster analysis can aid in identifying multidimensional patterns in the diffusion MRI (dMRI) data that are not apparent when analyzing individual parameters in isolation. The aim of this study was to evaluate how well cluster analysis of VERDICT parameters can be used for intratumor tissue characterization compared to ADC in a mouse model of human small intestine neuroendocrine tumor (GOT1), and to validate the method by histological analysis. Mice implanted with GOT1 were irradiated and subsequently imaged using a dMRI protocol designed for estimation of VERDICT parameters and ADC values. Histological analysis using hematoxylin and eosin (H&E), Masson's trichrome, and Ki67 staining identified three distinct tumor tissue types: necrotic, fibrotic, and viable tumor tissue. ROIs were drawn on regions of high and low ADC, which spatially matched with necrosis or fibrosis, and viable tumor tissue, respectively. Among the VERDICT parameters, the cell radius index (R) was most effective in distinguishing between necrotic and fibrotic tissue, whereas the intracellular fraction (f

Indexed as

Diffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedNeuroendocrine TumorsAnimalsCluster AnalysisFemaleHumansMiceReproducibility of Resultsanimal cancer modelcluster analysisdiffusionmicrostructure imagingpreclinical

Identifiers

PMID40296332
PMCPMC12038086

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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.