Evidence map›Paper›PMID 26720872›Full record

ArticleJournal of biomedical optics2015

Ultrahigh polarimetric image contrast enhancement for skin cancer diagnosis using InN plasmonic nanoparticles in the terahertz range.

Michael Ney, Ibrahim Abdulhalim

Abstract read
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In one paragraph

Article in Journal of biomedical optics, 2015. 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. Article
  3. Review
  4. Polarimetry terahertz imaging of human breast cancer surgical specimens.Journal of medical imaging (Bellingham, Wash.) · 2024
    Article
  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. 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

2 authors.

Michael Ney
Ibrahim Abdulhalim

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mueller matrix imaging sensitivity, to delicate water content changes in tissue associated with early stages of skin cancer, is demonstrated by numerical modeling to be enhanced by localized surface plasmon resonance (LSPR) effects at the terahertz (THz) range when InN nanoparticles (NPs) coated with Parylene-C are introduced into the skin. A skin tissue model tailored for THz wavelengths is established for a Monte Carlo simulation of polarized light propagation and scattering, and a comparative study based on simulated Mueller matrices is presented considering different NPs’ parameters and insertion into the skin methods. The insertion of NPs presenting LSPR in the THz is demonstrated to enable the application of polarization-based sample characterization techniques adopted from the scattering dominated visible wavelengths domain for the, otherwise, relatively low scattering THz domain, where such approach is irrelevant without the NPs. Through these Mueller polarimetry techniques, the detection of water content variations in the tissue is made possible and with high sensitivity. This study yields a limit of detection down to 0.0018% for relative changes in the water content based on linear degree of polarization--an improvement of an order of magnitude relative to the limit of detection without NPs calculated in a previous ellipsometric study.

Indexed as

Image EnhancementComputer SimulationHumansImage Processing, Computer-AssistedIndiumLightLimit of DetectionMonte Carlo MethodNanoparticlesPolymersReproducibility of ResultsScattering, RadiationSkinSkin NeoplasmsSurface Plasmon ResonanceTerahertz RadiationIndiumindium nitrideparylenePolymersXylenes

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.