Evidence map›Paper›PMID 34109533›Full record

ArticleJournal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract2021

Role of Removed Lymph Nodes on the Prognosis of M0 Small-Bowel Neuroendocrine Tumors: a Propensity Score Matching Analysis from SEER Database.

Jie-Bin Xie, Yue-Shan Pang, Xun Li, Xiao-Ting Wu

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Article in Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Jie-Bin XieDepartment of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.ORCID 0000-0003-1016-7536
Yue-Shan PangDepartment of Geriatrics, The Second Clinical Medical College of North Sichuan Medical College, Nanchong Central Hospital, Nanchong, Sichuan, China.ORCID 0000-0003-1161-4879
Xun LiDepartment of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.ORCID 0000-0003-1866-3657
Xiao-Ting WuDepartment of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China. wxt1@medmail.com.cn.ORCID 0000-0002-3996-868X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCurrent studies on the number of removed lymph nodes (LNs) and their prognostic value in small-bowel neuroendocrine tumors (SBNETs) are limited. This study aimed to clarify the prognostic value of removed LNs for SBNETs.

methodsSBNET patients without distant metastasis from 2004 to 2017 in the SEER database were included. The optimal cutoff values of examined LNs (ELNs) and negative LNs (NLNs) were calculated by the X-tile software. Propensity score matching (PSM) was done to match patients 1:1 on clinicopathological characteristics between the two groups. The Kaplan-Meier method with log-rank test and multivariable Cox proportional-hazards regression model were used to evaluate the prognostic effect of removed LNs.

resultsThe cutoff values of 14 for ELNs and 9 for NLNs could well distinguish patients with different prognoses. After 1:1 PSM, the differences in clinicopathological characteristics between the two groups were significantly reduced (all P > 0.05). Removal of more than one LN significantly improved the prognosis of the patients (P < 0.001). The number of lymphatic metastasis in the sufficiently radical resection group (SRR, 3.74 ± 3.278, ELN > 14 and NLN > 9) was significantly more than that in the insufficiently radical resection group (ISRR, 2.72 ± 3.19, ELN < 14 or NLN < 9). The 10-year overall survival (OS) of the SRR was significantly better than that of the ISRR (HR = 1.65, P = 0.001, 95% CI: 1.24-2.19).

conclusionBoth ELNs and NLNs can well predict the OS of patients. Systematic removal of more than 14 LNs and more than 9 NLNs can increase the OS of SBNET patients.

Indexed as

Lymph NodesNeuroendocrine TumorsHumansNeoplasm StagingPrognosisPropensity ScoreRetrospective StudiesSEER ProgramLymph nodesNeuroendocrine tumorsPrognosisPropensity score matchingSEER

Identifiers

PMID34109533
PMCPMC8654718

What Socratic 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 Socratic graph.