Evidence map›Paper›PMID 25412956›Full record

ArticleTumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine2015

Prognostic significance of lymph node ratio in esophageal cancer.

Nana Wang, Yibin Jia, Jianbo Wang, Xintong Wang, Cihang Bao, Qingxu Song, Bingxu Tan, Yufeng Cheng

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Article in Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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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

18 citing papers in PubMed.

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  17. Differentially Expressed mRNAs and Potential Mechanisms of Radiation-Induced TUT4Dose-response : a publication of International Hormesis Society
    Article
  18. An RNA-seq-Based Expression Profiling of Radiation-Induced Esophageal Injury in a Rat Model.Dose-response : a publication of International Hormesis Society
    Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Nana WangDepartment of Radiation Oncology, Qilu Hospital of Shandong University, 107 West Wenhua Road, Jinan, 250012, People's Republic of China.
Yibin Jia
Jianbo Wang
Xintong Wang
Cihang Bao
Qingxu Song
Bingxu Tan
Yufeng Cheng

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

N staging predicting esophageal cancer patient prognosis has been studied. Lymph node ratio, which is considered to show metastatic lymph node status more accurately, is found to have prognostic significance in several tumors. We investigated whether lymph node ratio (LNR) was associated with the prognosis of esophageal cancer in this study. Esophageal cancer patients who underwent esophagectomy at Qilu Hospital of Shandong University from January 2007 to December 2008 were studied. A total of 209 cases were evaluated in this study. The median disease-free survival (DFS) of this cohort was 35.2 months, and 5-year DFS rate was 32.1%. The median overall survival (OS) was 46.4 months, and 5-year OS rate was 40.0%. Kaplan-Meier survival analysis revealed that patients with LNR higher than 0.2 had significantly poorer DFS (p < 0.001) and OS (p < 0.001) than those with LNR less than 0.2. In a multivariate analysis, LNR was found to be an independent prognostic factor for DFS (p = 0.008, HR 1.863, 95% CI 1.180-2.942) and OS (p = 0.025, HR 1.708, 95% CI 1.068-2.731). N stage (p = 0.028, HR 1.626, 95% CI 1.055-2.506) was also found to be an independent prognostic factors for OS. Subgroups analysis revealed significant difference in OS and DFS rates between different LNR categories within the same N stages (p < 0.05) but not between different N stages within the same LNR category (p > 0.05). LNR was recognized as an independent factor in both OS and DFS in esophageal cancer. Besides, LNR showed a better prognostic value than N stage for esophageal cancer.

Indexed as

PrognosisAgedDisease-Free SurvivalEsophageal NeoplasmsFemaleHumansLymphatic MetastasisLymph Node ExcisionLymph NodesMaleMiddle AgedNeoplasm Staging

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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.