Evidence map›Paper›PMID 39035194›Full record

ArticleJournal of the National Cancer Center2023

Number of involved nodal stations: a better lymph node classification for clinical stage IA lung adenocarcinoma.

Mengwen Liu, Lei Miao, Rongshou Zheng, Liang Zhao, Xin Liang, Shiquan Yin, Jingjing Li, Cong Li, Meng Li, Li Zhang

Abstract read
In one paragraph

Article in Journal of the National Cancer Center, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

10 authors.

Mengwen LiuDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Lei MiaoDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Rongshou ZhengNational Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Liang ZhaoDepartment of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xin LiangMedical Statistics Office, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Shiquan YinMedical Records Room, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jingjing LiDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Cong LiMedical Records Room, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Meng LiDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Li ZhangDepartment of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the popularization of lung cancer screening, more early-stage lung cancers are being detected. This study aims to compare three types of N classifications, including location-based N classification (pathologic nodal classification [pN]), the number of lymph node stations (nS)-based N classification (nS classification), and the combined approach proposed by the International Association for the Study of Lung Cancer (IASLC) which incorporates both pN and nS classification to determine if the nS classification is more appropriate for early-stage lung cancer. Methods: We retrospectively reviewed the clinical data of lung cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences between 2005 and 2018. Inclusion criteria was clinical stage IA lung adenocarcinoma patients who underwent resection during this period. Sub-analyses were performed for the three types of N classifications. The optimal cutoff values for nS classification were determined with X-tile software. Kaplan‒Meier and multivariate Cox analyses were performed to assess the prognostic significance of the different N classifications. The prediction performance among the three types of N classifications was compared using the concordance index (C-index) and decision curve analysis (DCA). Results: Of the 669 patients evaluated, 534 had pathological stage N0 disease (79.8%), 82 had N1 disease (12.3%) and 53 had N2 disease (7.9%). Multivariate Cox analysis indicated that all three types of N classifications were independent prognostic factors for prognosis (all Conclusion: The nS classification might be a better choice for nodal classification in clinical stage IA lung adenocarcinoma.

Indexed as

Clinical stage IA lung adenocarcinomaIASLC-proposed N classificationsN classificationNumber of involved nodal stationsPathologic nodal classification

Identifiers

PMID39035194
PMCPMC11256629

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Registered trials

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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.