ArticleNanoscale advances2022
Tracking the fates of iron-labeled tumor cells
Article in Nanoscale advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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
12 citing papers in PubMed, 23 citations in OpenAlex.
- Magnetic Particle Imaging Distinguishes Viable and Damaged Cells by Exploiting Distinct Magnetic Signatures of Internalized Nanoparticles.Small science · 2026Article
- Magnetic nanoparticles for cancer theranostics.Biomedical physics & engineering express · 2026Review
- On the effect of reference and sample volume on magnetic particle imaging quantification.Journal of magnetism and magnetic materials · 2025Article
- Development of Iron Oxide Nanochains as a Sensitive Magnetic Particle Imaging Tracer for Cancer Detection.ACS applied materials & interfaces · 2025Article
- A study on indirect tumor localization using lung phantom during radiation therapy.Quantitative imaging in medicine and surgery · 2025Article
- Preclinical and Clinical-Scale Magnetic Particle Imaging of Natural Killer Cells: in vitro and ex vivo Demonstration of Cellular Sensitivity, Resolution, and Quantification.Molecular imaging and biology · 2025Article
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- A Comparison of the Sensitivity and Cellular Detection Capabilities of Magnetic Particle Imaging and Bioluminescence Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Magnetic Particle Imaging Reveals that Iron-Labeled Extracellular Vesicles Accumulate in Brains of Mice with Metastases.ACS applied materials & interfaces · 2024Article
- Principles and applications of magnetic nanomaterials in magnetically guided bioimaging.Materials today physics · 2023Article
- Dual Magnetic Particle Imaging and Akaluc Bioluminescence Imaging for Tracking Cancer Cell Metastasis.Tomography (Ann Arbor, Mich.) · 2023Article
- A primer onFrontiers in medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of imaging to detect and monitor the movement and accumulation of cells in living subjects can provide significant insights that can improve our understanding of metastasis and guide therapeutic development. For cell tracking using Magnetic Resonance Imaging (MRI), cells are labeled with iron oxides and the effects of the iron on water provides contrast. However, due to low specificity and difficulties in quantification with MRI, other modalities and approaches need to be developed. Magnetic Particle Imaging (MPI) is an emerging imaging technique which directly detects iron, allowing for a specific, quantitative and sensitive readout. Here, we use MPI to image iron-labeled tumor cells longitudinally, from implantation and growth at a primary site to movement to distant anatomic sites.
Identifiers
What OpenQuestion holds
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.