Evidence map›Paper›PMID 31143239›Full record

ReviewJournal of research in medical sciences : the official journal of Isfahan University of Medical Sciences2019

Medical imaging modalities using nanoprobes for cancer diagnosis: A literature review on recent findings.

Daryoush Shahbazi-Gahrouei, Pegah Moradi Khaniabadi, Bita Moradi Khaniabadi, Saghar Shahbazi-Gahrouei

Abstract readReview
In one paragraph

Review in Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  6. Nanotechnology for colorectal cancer detection and treatment.World journal of gastroenterology · 2022
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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.

Daryoush Shahbazi-GahroueiDepartment of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Pegah Moradi KhaniabadiSchool of Physics, University Sains Malaysia, Pulau Penang, Malaysia.
Bita Moradi KhaniabadiChild Growth and Development Research Center, Research Institute for Primordial Prevention of Non-Communicable Disease, Isfahan University of Medical Sciences, Isfahan, Iran.
Saghar Shahbazi-GahroueiSchool of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Medical imaging modalities are used for different types of cancer detection and diagnosis. Recently, there have been a lot of studies on developing novel nanoparticles as new medical imaging contrast agents for the early detection of cancer. The aim of this review article is to categorize the medical imaging modalities accompanying with using nanoparticles to improve potential imaging for cancer detection and hence valuable therapy in the future. Nowadays, nanoparticles are becoming potentially transformative tools for cancer detection for a wide range of imaging modalities, including computed tomography (CT), magnetic resonance imaging, single photon emission CT, positron emission tomography, ultrasound, and optical imaging. The study results seen in the recent literature provided and discussed the diagnostic performance of imaging modalities for cancer detections and their future directions. With knowledge of the correlation between the application of nanoparticles and medical imaging modalities and with the development of targeted contrast agents or nanoprobes, they may provide better cancer diagnosis in the future.

Indexed as

Cancerdiagnosismedical imagingnanoparticles

Identifiers

PMID31143239
PMCPMC6521609

What OpenQuestion holds

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Registered trials

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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.