Evidence map›Paper›PMID 29499032›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2018

Dielectric Properties for Differentiating Normal and Malignant Thyroid Tissues.

Yiou Cheng, Minghuan Fu

Abstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Real-Time Differentiation Between Benign and Malignant Breast Tumors and Other Tissues Using Dielectric Properties.Medical science monitor : international medical journal of experimental and clinical research · 2025
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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

2 authors.

Yiou ChengSchool of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China (mainland).
Minghuan FuDepartment of Gerontology, Hospital of the University of Electronic Science and Technology of China and Sichuan Provincial People's Hospital, Chengdu, Sichuan, China (mainland).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND The incidence rate of thyroid cancer has increased greatly during the last few decades, and highly sensitive and specific methods for early diagnosis and prognostic evaluation remain lacking. In this study, we investigated a novel approach based on microwave theory to detect thyroid cancer. MATERIAL AND METHODS Freshly excised thyroid tissues (n=236) from 48 patients were identified as normal or malignant using histology. Each sample was measured for effective dielectric permittivity and effective conductivity (0.5-8 GHz). The means of each of these parameters of the normal and malignant groups were compared. RESULTS The effective dielectric permittivities of normal and malignant thyroid tissues were 24.026±1.951 to 17.950±1.648 and 69.782±2.734 to 57.356±1.802, respectively. Also, as a function of frequency, the effective conductivities of normal and malignant thyroid cancer were 0.8395±0.2013 to 1.8730±0.0979 and 1.8960±0.5024 to 9.7461±0.9349 (S/m), respectively. The mean effective dielectric permittivities and effective conductivities of normal thyroid tissues were significantly lower than that of thyroid cancer tissues. CONCLUSIONS Measuring the effective dielectric permittivity and effective conductivity of excised thyroid tissues may be a new and viable method to determine malignancy in thyroid cancer.

Indexed as

ChinaDielectric SpectroscopyElectric ConductivityElectric ImpedanceFemaleHumansMaleMicrowavesNeoplasmsThyroid GlandThyroid Neoplasms

Identifiers

PMID29499032
PMCPMC5844190

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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.