Evidence map›Paper›PMID 31791906›Full record

ArticleJournal of pediatric urology2020

Factors affecting lymph node sampling patterns and the impact on survival of lymph node density in patients with Wilms tumor: a Surveillance, Epidemiology, and End Result (SEER) database review.

Jonathan P Walker, Jared S Johnson, Megan M Eguchi, Amanda F Saltzman, Myles Cockburn, Nicholas G Cost

Abstract read
In one paragraph

Article in Journal of pediatric urology, 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
–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

6 citing papers in PubMed.

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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

6 authors.

Jonathan P WalkerDepartment of Surgery, Division of Urology, University of Colorado School of Medicine & Children's Hospital Colorado, 13123 E. 16th Ave, Aurora, CO, 80045, USA. Electronic address: jonathan.walker102310@gmail.com.
Jared S JohnsonUniversity of Colorado School of Medicine, 13001 East 17th Place, Aurora, CO, 80045, USA.
Megan M EguchiUniversity of Colorado Cancer Center, 13001 E. 17th Place, Building 500, 6th Floor, Aurora, CO, 80045, USA.
Amanda F SaltzmanDepartment of Surgery, Division of Urology, University of Colorado School of Medicine & Children's Hospital Colorado, 13123 E. 16th Ave, Aurora, CO, 80045, USA.
Myles CockburnUniversity of Colorado School of Public Health, Department of Epidemiology, 13001 E. 17th Place, Building 500, 6th Floor, Aurora, CO, 80045, USA.
Nicholas G CostDepartment of Surgery, Division of Urology, University of Colorado School of Medicine & Children's Hospital Colorado, 13123 E. 16th Ave, Aurora, CO, 80045, USA.

Funding

University of Colorado Cancer Center Support Grant - Lung Cancer Patient-Derived Xenografts with Autologous Human Immune SystemsP30CA046934 · NCI · UNIVERSITY OF COLORADO DENVER · PI James V Degregori · 1988 to 2026
$117.0M
NCI NIH HHS P30 CA046934
6 · The paper itself

Abstract

backgroundLymph node (LN) involvement is an important prognostic indicator for patients with Wilms tumor (WT), and there have been previous reports of utilizing LN density (LND = positive LN/LNs examined) as an advanced metric to risk-stratify patients with WT.

objectiveThe purpose of this study was to describe patient characteristics that affect LN yield and assess the effect of LND on the overall survival (OS) in patients with WT, with the expectation that patients with LNDs above a critical cut-point would demonstrate lower OS. STUDY

designThe Surveillance, Epidemiology, and End Result (SEER) database was queried for all patients diagnosed with unilateral WT from 2004 to 2015. Patient and disease characteristics were collected, and Poisson regression was used to identify characteristics correlated with LN yield. LND was calculated for LN-positive patients, and multivariable survival analysis was performed, including patient demographics and LND as variables.

results1489 patients with unilateral WT were identified for analysis, 231 (15.51%) of whom were LN-positive. Median patient age at diagnosis was three years (IQR 1-5). On Poisson regression, the year of diagnosis, patient age, tumor size and laterality, and stage were found to impact LN yield. For patients with positive LNs, five-year OS of patients with LNDs above 0.4 was worse than those below 0.4 (76.1% vs 89.6%, p = 0.041). On multivariable analysis, tumor size and LND remained significant predictors of OS. DISCUSSION: Administrative databases such as SEER provide an excellent resource for studying conditions where large patient numbers for analysis are difficult to obtain. Unfortunately, the SEER database is unable to account for every factor that could affect LN sampling patterns. Additionally, favorable vs unfavorable histology is not available in SEER, and SEER utilizes its own staging system, which makes comparison to Children's Oncology Group staging difficult. Despite these limitations, the findings of this study are similar to those previously published using administrative databases analyzing LN sampling patterns and the effect of LND on OS in WT.

conclusionsAnalysis of the SEER database confirms that there are several patient- and disease-specific factors that affect the number of LNs sampled during nephrectomy for WT, and that LND may be a predictor of OS. These findings highlight the need for standardization of LN sampling patterns for pediatric renal tumors and support the investigation of LND in future studies to further risk-stratify WT patients to tailor therapy intensity.

Indexed as

Child, PreschoolDatabases, FactualFemaleHumansInfantKidney NeoplasmsLymph NodesMaleSEER ProgramSpecimen HandlingSurvival AnalysisWilms Tumorlymph node densitynephroblastomapediatricWilms tumor

Identifiers

PMID31791906
PMCPMC7477887

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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.