Evidence map›Paper›PMID 35655730›Full record

ArticleContrast media & molecular imaging2022

CT Combined with Multiparameter MRI in Differentiating Pathological Subtypes of Non-Small-Cell Lung Cancer before Surgery.

Xinwen Li, Xiaoyan Wang, Qing Li, Lijie Bai

Open access · hybridAbstract read
In one paragraph

Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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 at 2 institutions in 1 country.

Xinwen LiDepartment of CT Diagnosis, Yan'an People's Hospital, Yan'an, Shaanxi 716000, China.
Xiaoyan WangDepartment of Anesthesiology, The Affiliated Hospital of Yan'an University, Yan'an, Shaanxi 716000, China.
Qing LiDepartment of General Surgey Ward, Yulin First Hospital, Yulin, Shaanxi 719000, China.
Lijie BaiDepartment of CT Diagnosis, Yan'an People's Hospital, Yan'an, Shaanxi 716000, China.ORCID 0000-0001-9839-3161
Yan'an University · CNYulin University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the diagnostic value of computed tomography (CT) combined with multiparametric magnetic resonance imaging (mpMRI) for preoperative differentiation of non-small-cell lung cancer (NSCLC). Methods: CT and MRI imaging data were collected from all patients with squamous lung cancer and adenocarcinoma admitted to our hospital from June 2019 to December 2020 (286 cases). ROC curves were plotted to evaluate the performance of CT, mpMRI, and CT combined with mpMRI to differentiate pathological subtypes of NSCLC. Univariate and multivariate regression were used to be independent predictors of pathological subtypes of NSCLC. Results: ROC curves showed that CT combined with mpMRI had the largest area under the curve, followed by mpMRI and CT successively. Univariate regression analysis showed that gender, smoking, tumor size, morphology, marginal lobulation, marginal burr, bronchial truncation sign, and vascular convergence sign were factors influencing the pathological subtype of NSCLC. Multivariate regression analysis suggested the fact that gender, tumor size, morphology, marginal lobulation, bronchial truncation, and vascular convergence sign are likely the independent predictors of NSCLC pathological subtypes. Conclusions: CT combined with mpMRI can effectively distinguish NSCLC pathological subtypes, which is worthy of clinical application.

Indexed as

AdenocarcinomaCarcinoma, Non-Small-Cell LungLung NeoplasmsNeuroblastomaHumansMagnetic Resonance ImagingTomography, X-Ray Computed

Identifiers

PMID35655730
PMCPMC9129958
OpenAlexW4280555856

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.