Evidence map›Paper›PMID 32683563›Full record

ArticleLung2020

Negative Lymph Node Count Predicts Survival of Resected Non-small Cell Lung Cancer.

Xinyan Zhou, Chunxiao Wu, Qi Cheng

Abstract read
PubMed Publisher
In one paragraph

Article in Lung, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Predictive value of primary tumorCancer imaging : the official publication of the International Cancer Imaging Society · 2026
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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

3 authors.

Xinyan ZhouSchool of Public Health, Shanghai Jiao Tong University, 227 South Chongqing Road, Shanghai, 200025, China.
Chunxiao WuShanghai Municipal Center for Disease Control and Prevention, Shanghai Institutes of Preventive Medicine, 1380 Zhongshan West Road, Shanghai, 200336, China.
Qi ChengSchool of Public Health, Shanghai Jiao Tong University, 227 South Chongqing Road, Shanghai, 200025, China. chengqi2019@126.com.ORCID 0000-0001-6026-8162

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe purpose of this study was to explore the association between the negative lymph node (NLN) count and survival, as well as compare the prognostic value of the positive lymph node (PLN) count, lymph node ratio (the PLN count/total lymph nodes examined, LNR), and NLN count in patients with non-small cell lung cancer (NSCLC).

methodsWe identified patients diagnosed with NSCLC between 2005 and 2011 from the Surveillance, Epidemiology, and End Results database. Outcomes of interest were lung cancer-specific survival (LCSS) and overall survival (OS). Cases were divided into several groups based on the PLN count, NLN count, and LNR. The prognostic significance of the PLN count, NLN count, and LNR models was analyzed with the Kaplan-Meier method and the Cox regression model.

results39,959 patients with surgical resection for NSCLC were identified. Univariate analysis demonstrated that a greater count of NLNs was associated with better LCSS (P < 0.001) and OS (P < 0.001). Subgroup analysis showed that the NLN count could predict survival in both node-negative and node-positive patients. Multivariable analysis revealed that the NLN count was an independent prognostic factor for LCSS and OS.

conclusionThe NLN count is an independent prognostic factor of OS and LCSS in patients with NSCLC, as well as the PLN count and LNR. The prognostic value of the PLN count, NLN count, and LNR shows no difference.

Indexed as

Carcinoma, Non-Small-Cell LungLung NeoplasmsLymphatic MetastasisLymph NodesAgedFemaleHumansKaplan-Meier EstimateLymph Node ExcisionMaleNeoplasm StagingPrognosisRisk AssessmentSurvival AnalysisUnited StatesLymph nodesNon-small cell lung cancerPrognosisSurvival analysis

Identifiers

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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.