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Melanoma-specific mortality and competing mortality in patients with non-metastatic malignant melanoma: a population-based analysis

Overview of attention for article published in BMC Cancer, July 2016
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Title
Melanoma-specific mortality and competing mortality in patients with non-metastatic malignant melanoma: a population-based analysis
Published in
BMC Cancer, July 2016
DOI 10.1186/s12885-016-2438-3
Pubmed ID
Authors

Weidong Shen, Naoko Sakamoto, Limin Yang

Abstract

The objectives of this study were to evaluate and model the probability of melanoma-specific death and competing causes of death for patients with melanoma by competing risk analysis, and to build competing risk nomograms to provide individualized and accurate predictive tools. Melanoma data were obtained from the Surveillance Epidemiology and End Results program. All patients diagnosed with primary non-metastatic melanoma during the years 2004-2007 were potentially eligible for inclusion. The cumulative incidence function (CIF) was used to describe the probability of melanoma mortality and competing risk mortality. We used Gray's test to compare differences in CIF between groups. The proportional subdistribution hazard approach by Fine and Gray was used to model CIF. We built competing risk nomograms based on the models that we developed. The 5-year cumulative incidence of melanoma death was 7.1 %, and the cumulative incidence of other causes of death was 7.4 %. We identified that variables associated with an elevated probability of melanoma-specific mortality included older age, male sex, thick melanoma, ulcerated cancer, and positive lymph nodes. The nomograms were well calibrated. C-indexes were 0.85 and 0.83 for nomograms predicting the probability of melanoma mortality and competing risk mortality, which suggests good discriminative ability. This large study cohort enabled us to build a reliable competing risk model and nomogram for predicting melanoma prognosis. Model performance proved to be good. This individualized predictive tool can be used in clinical practice to help treatment-related decision making.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 43 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 16%
Other 5 12%
Student > Ph. D. Student 5 12%
Student > Master 5 12%
Student > Bachelor 3 7%
Other 4 9%
Unknown 14 33%
Readers by discipline Count As %
Medicine and Dentistry 11 26%
Biochemistry, Genetics and Molecular Biology 5 12%
Neuroscience 2 5%
Agricultural and Biological Sciences 1 2%
Economics, Econometrics and Finance 1 2%
Other 7 16%
Unknown 16 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 July 2016.
All research outputs
#20,335,423
of 22,880,230 outputs
Outputs from BMC Cancer
#6,507
of 8,325 outputs
Outputs of similar age
#308,657
of 355,364 outputs
Outputs of similar age from BMC Cancer
#180
of 255 outputs
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We're also able to compare this research output to 255 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.