Evidence map›Paper›PMID 32669129›Full record

ArticleWorld journal of surgical oncology2020

Tumor volume is more reliable to predict nodal metastasis in non-small cell lung cancer of 3.0 cm or less in the greatest tumor diameter.

Bei Jia, Biao Chen, Hao Long, Tiehua Rong, Xiaodong Su

Open access · goldAbstract read
In one paragraph

Article in World journal of surgical oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 24% 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

6 citing papers in PubMed, 10 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

5 authors at 1 institution in 1 country.

Bei JiaDepartment of Thoracic Surgery, Sun Yat Sen University Cancer Center, Guangzhou, China.
Biao ChenDepartment of Thoracic Surgery, Sun Yat Sen University Cancer Center, Guangzhou, China.
Hao LongDepartment of Thoracic Surgery, Sun Yat Sen University Cancer Center, Guangzhou, China.
Tiehua RongDepartment of Thoracic Surgery, Sun Yat Sen University Cancer Center, Guangzhou, China.
Xiaodong SuDepartment of Thoracic Surgery, Sun Yat Sen University Cancer Center, Guangzhou, China. suxd@sysucc.org.cn.ORCID http://orcid.org/0000-0001-8771-5022
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn this study, we sought to evaluate the correlation between TV, GTD, and lymph node metastases in NSCLC patients with tumors of GTD ≤ 3.0 cm.

methodsWe retrospectively analyzed the characteristics of clinicopathologic variables for lymph node involvement in 285 NSCLC patients with tumors of GTD ≤ 3.0 cm who accepted curative surgical resection. The TVs were semi-automatically measured by a software, and optimal cutoff points were obtained using the X-tile software. The relationship between GTD and TV were described using non-linear regression. The correlation between GTD, TV, and N stages was analyzed using the Pearson correlation coefficient. The one-way ANOVA was used to compare the GTD and TV of different lymph node stage groups.

resultsThe relationship between GTD and TV accorded with the exponential growth model: y = 0.113e

conclusionsFor NSCLC tumors with GTD ≤ 3.0 cm, TV is a more sensitive marker than GTD in predicting the positive lymph node metastases. The likelihood for metastasis increases with an increasing TV especially when GTD is > 2.0-3.0 cm.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsHumansLymph Node ExcisionLymph NodesNeoplasm StagingPrognosisRetrospective StudiesTumor BurdenGreatest tumor diameterLymph node metastasesNon-small cell lung cancerPulmonary noduleSurgical resectionTumor volume

Identifiers

PMID32669129
PMCPMC7364500
OpenAlexW3042696922

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.