Evidence map›Paper›PMID 41559465›Full record

ArticleAnnals of surgical oncology2026

N-LODDS: A Novel Integrated Lymph Node Staging System Enhancing Prognostic Accuracy in Non-Small-Cell Lung Cancer.

Qiying Chen, Meihong Yao, Zishan Chen, Shiwen Liu, Jinman Zhuang, Xi Chen, Jie Yi, Binghua Tu, Ziyue Yang, Yinghong Yang and 1 more

Abstract read
In one paragraph

Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Qiying Chen *Department of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Meihong Yao *Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China.
Zishan ChenMaternal and Child Health Hospital of Fuzhou Second General Hospital, Fuzhou, Fujian, China.
Shiwen LiuDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Jinman ZhuangDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Xi ChenDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Jie YiDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Binghua TuDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Ziyue YangDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China.
Yinghong YangDepartment of Pathology, Fujian Medical University Union Hospital, Fuzhou, Fujian, China. 1555yyh@fjmu.edu.cn.
Fei HeDepartment of Epidemiology and Health Statistics, The School of Public Health, Fujian Medical University, Fuzhou, Fujian, China. i.fei.he@fjmu.edu.cn.

Funding

Fujian province Funds for the program of science and Technology 2022Y0017Fujian province Joint Funds for the innovation of science and Technology 2019Y9096Startup Fund for scientific research, Fujian Medical University 2017XQ1042
6 · The paper itself

Abstract

backgroundThis study aimed to develop and validate a novel lymph node staging system integrating anatomical location and quantitative characteristics, evaluate its prognostic prediction efficacy in non-small-cell lung cancer (NSCLC), and establish a multivariate prognostic model.

methodsA total of 23,676 patients with NSCLC from the SEER database (2010-2015) were enrolled. Optimal cutoffs for lymph node parameters (NPLN, LNR, LODDS) were determined using X-tile software. Composite variables (N-NPLN, N-LNR, N-LODDS) were constructed by integrating N staging. Independent prognostic factors were screened via Cox regression, and a nomogram was developed. Performance was assessed using the receiver operating characteristic curves, calibration curves, and decision curve analysis.

resultsN-LODDS staging demonstrated optimal prognostic prediction, significantly outperforming N-LNR and N-NPLN. The nomogram incorporating N-LODDS, tumor size, and nine independent prognostic factors showed superior discrimination and calibration (5 year area under the curve 0.740; 95% confidence interval 0.731-0.749) in both training and validation cohorts, with significant advantages over the TNM staging system (all P<0.001).

conclusionThe N-LODDS staging system significantly improves prognostic accuracy by integrating anatomical and quantitative lymph node features, providing a novel tool for personalized NSCLC management. Future multicenter prospective studies are needed to validate its clinical utility.

Indexed as

AdenocarcinomaCarcinoma, Non-Small-Cell LungCarcinoma, Squamous CellLung NeoplasmsLymph NodesNeoplasm StagingNomogramsAgedFemaleFollow-Up StudiesHumansLymphatic MetastasisMaleMiddle AgedPrognosisROC CurveLog odds of positive lymph nodes (LODDS)Lymph node metastasisNomogramNon-small cell lung cancer (NSCLC)Surveillance, Epidemiology, and End Results (SEER)

Identifiers

PMID41559465
PMCPMC13083349

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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.