ArticleRadiology2015
Science to practice: can decreased lymph node MR imaging signal intensity be used as a biomarker for the efficacy of cancer vaccination?
Article in Radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- In Vivo MRI Tracking of Tumor Vaccination and Antigen Presentation by Dendritic Cells.Molecular imaging and biology · 2022Review
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Summary In the study of Zhang et al (1), tumor-bearing mice were vaccinated with magnetically labeled, tumor antigen-primed dendritic cells (DCs). After homing of these antigen-presenting cells to the draining lymph node (LN), it was shown that the iron oxide-induced decrease in LN magnetic resonance (MR) imaging signal intensity correlated with the observed tumor growth delay, suggesting that the degree of hypointensity can serve as a surrogate marker for the efficacy of tumor vaccination.
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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.