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  • Prognostic survival model for people diagnosed with invasive cutaneous melanoma

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    BaadePUB371.pdf (818.2Kb)
    Author(s)
    Baade, Peter D
    Royston, Patrick
    Youl, Philipa H
    Weinstock, Martin A
    Geller, Alan
    Aitken, Joanne F
    Griffith University Author(s)
    Aitken, Joanne
    Youl, Philippa
    Baade, Peter D.
    Year published
    2015
    Metadata
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    Abstract
    Background: The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Methods: Data from the Queensland Cancer Registry for people (20–89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive ...
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    Background: The ability of medical practitioners to communicate risk estimates effectively to patients diagnosed with melanoma relies on accurate information about prognostic factors and their impact on survival. This study reports the development of one of the few melanoma prognostic models, called the Melanoma Severity Index (MSI), based on population-based cancer registry data. Methods: Data from the Queensland Cancer Registry for people (20–89 years) diagnosed with a single invasive melanoma between 1995 and 2008 (n = 28,654; 1,700 melanoma deaths). Additional clinical information about metastasis, ulceration and positive lymph nodes was manually extracted from pathology forms. Flexible parametric survival models were combined with multivariable fractional polynomial for selecting variables and transformations of continuous variables. Multiple imputation was used for missing covariate values. Results: The MSI contained the variables thickness (transformed, explained 40.6% of variation in survival), body site (additional 1.9% in variation), metastasis (1.8%), positive nodes (0.7%), ulceration (1.3%), age (1.1%). Royston and Sauerbrei’s D statistic (measure of discrimination) was 1.50 (95% CI = 1.44, 1.56) and the corresponding RD2 (measure of explained variation) was 0.47 (0.45, 0.49), demonstrating strong explanatory performance. The Harrell-C statistic was 0.88 (0.88, 0.89). Lacking an external validation dataset, we applied internal-external cross validation to demonstrate the consistency of the prognostic information across geographically-defined subsets of the cohort. Conclusions: The MSI provides good ability to predict survival for melanoma patients. Beyond the immediate clinical use, the MSI may have important public health and research applications for evaluations of public health interventions aimed at reducing deaths from melanoma.
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    Journal Title
    BMC Cancer
    Volume
    15
    Issue
    1
    DOI
    https://doi.org/10.1186/s12885-015-1024-4
    Copyright Statement
    © 2015 Baade et al.; licensee BioMed Central. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
    Note
    Page numbers are not for citation purposes. Instead, this article has the unique article number of 27.
    Subject
    Oncology and carcinogenesis
    Oncology and carcinogenesis not elsewhere classified
    Publication URI
    http://hdl.handle.net/10072/101291
    Collection
    • Journal articles

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