Evidence map›Paper›PMID 20084078›Full record

ReviewNature reviews. Urology2010

Lymphotropic nanoparticle enhanced MRI for the staging of genitourinary tumors.

Samdeep K Mouli, Lee C Zhao, Reed A Omary, C Shad Thaxton

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Urology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Lymph node imaging in testicular cancer.Translational andrology and urology · 2018
    Review
  3. Review
  4. Towards clinically translatableNature reviews. Materials · 2017
    Article
  5. Review
  6. Review
  7. Diagnosis of prostate cancer via nanotechnological approach.International journal of nanomedicine · 2015
    Review
  8. Review
  9. Effect of ligand density, receptor density, and nanoparticle size on cell targeting.Nanomedicine : nanotechnology, biology, and medicine · 2013
    Article
  10. Preclinical lymphatic imaging.Molecular imaging and biology · 2011
    Review
  11. Article
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

4 authors.

Samdeep K MouliDepartment of Radiology, Northwestern University, 303 East Chicago Avenue, Tarry 16-703, Chicago, IL 60611, USA.
Lee C Zhao
Reed A Omary
C Shad Thaxton

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nanotechnology is poised to have a substantial influence on biomedicine. A unique example of a nanotechnology that has progressed from proof-of-concept to human clinical trials is the use of ultrasmall superparamagnetic iron oxide nanoparticles as a cell-specific contrast agent for MRI. When injected systemically, these particles are taken up by macrophages of the reticuloendothelial system and accumulate in lymph nodes. This passive, cell-specific targeting of the iron oxide nanoparticles to lymph nodes, and the differential cellular content of benign versus malignantly infiltrated nodes make this method suitable for cancer staging. By using lymphotropic nanoparticle enhanced MRI, differences in benign versus malignant infiltration of lymph nodes can be visualized, which adds accuracy to standard MRI beyond criteria based solely upon the size and shape of lymph nodes. This technology has been used to assess lymph node metastases in a large number of human cancer types. In this Review, we focus on lymphotropic nanoparticle enhanced MRI and its application for the staging of genitourinary malignancies.

Indexed as

NanoparticlesAnimalsHumansImage EnhancementLymphatic MetastasisMagnetic Resonance ImagingNeoplasm StagingUrogenital Neoplasms

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.