Evidence map›Paper›PMID 35756662›Full record

ArticleFrontiers in oncology2022

Extracellular Volume Fraction Derived From Dual-Layer Spectral Detector Computed Tomography for Diagnosing Cervical Lymph Nodes Metastasis in Patients With Papillary Thyroid Cancer: A Preliminary Study.

Yan Zhou, Di Geng, Guo-Yi Su, Xing-Biao Chen, Yan Si, Mei-Ping Shen, Xiao-Quan Xu, Fei-Yun Wu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.1field-weighted citation impact, top 27% of its field
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

13 citing papers in PubMed, 14 citations in OpenAlex.

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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

8 authors at 3 institutions in 1 country.

Yan ZhouDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Di GengDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Guo-Yi SuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xing-Biao ChenSection of Clinical Research, Philips Healthcare Ltd, Shanghai, China.
Yan SiDepartment of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Mei-Ping ShenDepartment of Thyroid Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiao-Quan XuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Fei-Yun WuDepartment of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Jiangsu Province Hospital · CNNanjing Medical University · CNPhilips (China) · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objectives: The current study evaluates the performance of dual-energy computed tomography (DECT) derived extracellular volume (ECV) fraction based on dual-layer spectral detector CT for diagnosing cervical lymph nodes (LNs) metastasis from papillary thyroid cancer (PTC) and compares it with the value of ECV derived from conventional single-energy CT (SECT). Methods: One hundred and fifty-seven cervical LNs (81 non-metastatic and 76 metastatic) were recruited. Among them, 59 cervical LNs (27 non-metastatic and 32 metastatic) were affected by cervical root artifact on the contrast-enhanced CT images in the arterial phase. Both the SECT-derived ECV fraction (ECV Results: ECV Conclusions: ECV

Indexed as

extracellular volumelymph nodemetastasismultidetector computed tomographypapillary thyroid cancer

Identifiers

PMID35756662
PMCPMC9213667
OpenAlexW4281686049

What OpenQuestion holds

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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.