Evidence map›Paper›PMID 29112231›Full record

ArticleCancer2018

Individualized outcome prognostication for patients with laryngeal cancer.

Connor W Hoban, Lauren J Beesley, Emily L Bellile, Yilun Sun, Matthew E Spector, Gregory T Wolf, Jeremy M G Taylor, Andrew G Shuman

Abstract read
In one paragraph

Article in Cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  8. Observational
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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

8 authors.

Connor W HobanDepartment of Otolaryngology-Head and Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan.ORCID 0000-0003-2601-6193
Lauren J BeesleyDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan.
Emily L BellileDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan.
Yilun SunDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan.
Matthew E SpectorDepartment of Otolaryngology-Head and Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan.ORCID 0000-0001-7646-6075
Gregory T WolfDepartment of Otolaryngology-Head and Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan.
Jeremy M G TaylorDepartment of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan.
Andrew G ShumanDepartment of Otolaryngology-Head and Neck Surgery, University of Michigan Medical School, Ann Arbor, Michigan.ORCID 0000-0002-9305-7860

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Eric R. Fearon · 1988 to 2026
$178.2M
Therapeutic Potential and Mechanism of Action of Potent Small Molecule InhibitorsP50CA097248 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NOR, JACQUES EDUARDO · 2002 to 2013
$25.3M
Biostatistics Training Program in Cancer ResearchT32CA083654 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Veerabhadran Baladandayuthapani, Kelley McLain Kidwell · 2002 to 2026
$4.3M
NCI NIH HHS P30 CA046592NCI NIH HHS P50 CA097248NCI NIH HHS T32 CA083654
6 · The paper itself

Abstract

backgroundAccurate prognostication is essential to the optimal management of laryngeal cancer. Predictive models have been developed to calculate the risk of oncologic outcomes, but extensive external validation of accuracy and reliability is necessary before implementing them into clinical practice.

methodFour published prognostic calculators that predict 5-year overall survival for patients with laryngeal cancer were evaluated using patient information from a prospective epidemiology study cohort (n = 246; median follow-up, 60 months) with previously untreated, stage I through IVb laryngeal squamous cell carcinoma.

resultsDifferent calculators yielded substantially different predictions for individual patients. The observed 5-year overall survival was significantly higher than the averaged predicted 5-year overall survival of the 4 calculators (71.9%; 95% confidence interval [CI], 65%-78%] vs 47.7%). Statistical analyses demonstrated the calculators' limited capacity to discriminate outcomes for risk-stratified patients. The area under the receiver operating characteristic curve ranged from 0.68 to 0.72. C-index values were similar for each of the 4 models (range, 0.66-0.68). There was a lower than expected hazard of death for patients who received induction (bioselective) chemotherapy (hazard ratio, 0.46; 95% CI, 0.24-0.88; P = .024) or primary surgical intervention (hazard ratio, 0.43; 95 % CI, 0.21-0.90; P = .024) compared with those who received concurrent chemoradiation.

conclusionsSuboptimal reliability and accuracy limit the integration of existing individualized prediction tools into routine clinical decision making. The calculators predicted significantly worse than observed survival among patients who received induction chemotherapy and primary surgery, suggesting a need for updated consideration of modern treatment modalities. Further development of individualized prognostic calculators may improve risk prediction, treatment planning, and counseling for patients with laryngeal cancer. Cancer 2018;124:706-16. © 2017 American Cancer Society.

Indexed as

AgedChemoradiotherapyFemaleFollow-Up StudiesHumansInduction ChemotherapyKaplan-Meier EstimateLaryngeal NeoplasmsMaleMiddle AgedOutcome Assessment, Health CarePrognosisProspective StudiesRisk AssessmentRisk Factorscalculatorlarynx cancernomogramprognosticationrisk prediction

Identifiers

PMID29112231
PMCPMC5800991

What OpenQuestion holds

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Registered trials

None linked

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.