ArticleWorld journal of surgical oncology2023
Quantitative dynamic contrast-enhance MRI parameters for rectal carcinoma characterization: correlation with tumor tissue composition.
Article in World journal of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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.
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Who cites it
3 citing papers in PubMed.
- Preoperative prediction of microsatellite instability in gastric cancer using quantitative multiparametric MRI with DCE-MRI and DWI.Abdominal radiology (New York) · 2026Article
- Artificial intelligence classification of rectal neoplasia by endoscopic fluorescence perfusion analysis.Scientific reports · 2026Article
- Multiparametric MRI for characterization of the tumour microenvironment.Nature reviews. Clinical oncology · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the relationship between dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) measurements and the potential composition of rectal carcinoma.
methodsTwenty-four patients provided informed consent for this study. DCE-MRI was performed before total mesorectal excision. Quantitative parameters were calculated based on a modified Tofts model. Whole-mount immunohistochemistry and Masson staining sections were generated and digitized at histological resolution. The percentage of tissue components area was measured. Pearson correlation analysis was used to evaluate the correlations between pathological parameters and DCE-MRI parameters.
resultsOn the World Health Organization (WHO) grading scale, there were significant differences in extracellular extravascular space (K
conclusionThe dynamic contrast-enhanced MRI-derived parameters measured in rectal cancer were significantly correlated with the proportion of histological components. This may serve as an optimal imaging biomarker to identify tumor tissue components.
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