Evidence map›Paper›PMID 32802444›Full record

ArticleJournal of thoracic disease2020

Development of a nomogram for preoperative prediction of lymph node metastasis in non-small cell lung cancer: a SEER-based study.

Chufan Zhang, Qian Song, Lanlin Zhang, Xianghua Wu

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of thoracic disease, 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
1.0field-weighted citation impact, top 20% 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, 13 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

4 authors at 3 institutions in 1 country.

Chufan ZhangDepartmemt of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Qian SongDepartmemt of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Lanlin ZhangDepartmemt of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Xianghua WuDepartmemt of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Shanghai Medical College of Fudan University · CNChinese University of Hong Kong · CNFudan University Shanghai Cancer Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLymph node dissection is an important part of lung cancer surgery. Preoperational evaluation of lymph node metastases decides which dissection pattern should be chosen. The present study aimed to develop a nomogram to predict lymph node metastases on the basis of clinicopathological features of non-small cell lung cancer (NSCLC) patients.

methodsA total of 35,138 patients diagnosed with NSCLC from 2010-2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into training cohort and validation cohort. Possible risk factors were included and analyzed by logistic regression models. A nomogram was then constructed and validated.

results21.83% of all patients were confirmed with positive lymph node metastasis. Age at diagnosis, sex, stage, T status, tumor size, grade and laterality were identified as predicting factors for lymph node involvement. These variables were included to build the nomogram. The AUC of the model was 0.696 (95% CI, 0.617 to 0.775). The model was further validated in the validation set with AUC 0.693 (95% CI, 0.628 to 0.758). The model presented with good prediction accuracy in both training cohort and validation cohort.

conclusionsWe developed a convenient clinical prediction model for regional lymph node metastases in NSCLC patients. The nomogram will help physicians to determine which patients will receive the most benefit from lymph node dissection.

Indexed as

lymph node metastasisnomogramNon-small cell lung cancer (NSCLC)Surveillance, Epidemiology, and End Results (SEER)

Identifiers

PMID32802444
PMCPMC7399438
OpenAlexW3040988557

What OpenQuestion holds

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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.