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Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors

INTRODUCTION: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. METHODS: Data regarding 269 p...

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Autores principales: Kidd, Andrew C, McGettrick, Michael, Tsim, Selina, Halligan, Daniel L, Bylesjo, Max, Blyth, Kevin G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5812388/
https://www.ncbi.nlm.nih.gov/pubmed/29468073
http://dx.doi.org/10.1136/bmjresp-2017-000240
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author Kidd, Andrew C
McGettrick, Michael
Tsim, Selina
Halligan, Daniel L
Bylesjo, Max
Blyth, Kevin G
author_facet Kidd, Andrew C
McGettrick, Michael
Tsim, Selina
Halligan, Daniel L
Bylesjo, Max
Blyth, Kevin G
author_sort Kidd, Andrew C
collection PubMed
description INTRODUCTION: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. METHODS: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers D(XY) statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. RESULTS: Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean D(XY)0.332 (±0.019)). However, validation set D(XY) was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. CONCLUSIONS: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging.
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spelling pubmed-58123882018-02-21 Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors Kidd, Andrew C McGettrick, Michael Tsim, Selina Halligan, Daniel L Bylesjo, Max Blyth, Kevin G BMJ Open Respir Res Pleural Disease INTRODUCTION: Accurate prognostication is difficult in malignant pleural mesothelioma (MPM). We developed a set of robust computational models to quantify the prognostic value of routinely available clinical data, which form the basis of published MPM prognostic models. METHODS: Data regarding 269 patients with MPM were allocated to balanced training (n=169) and validation sets (n=100). Prognostic signatures (minimal length best performing multivariate trained models) were generated by least absolute shrinkage and selection operator regression for overall survival (OS), OS <6 months and OS <12 months. OS prediction was quantified using Somers D(XY) statistic, which varies from 0 to 1, with increasing concordance between observed and predicted outcomes. 6-month survival and 12-month survival were described by area under the curve (AUC) scores. RESULTS: Median OS was 270 (IQR 140–450) days. The primary OS model assigned high weights to four predictors: age, performance status, white cell count and serum albumin, and after cross-validation performed significantly better than would be expected by chance (mean D(XY)0.332 (±0.019)). However, validation set D(XY) was only 0.221 (0.0935–0.346), equating to a 22% improvement in survival prediction than would be expected by chance. The 6-month and 12-month OS signatures included the same four predictors, in addition to epithelioid histology plus platelets and epithelioid histology plus C-reactive protein (mean AUC 0.758 (±0.022) and 0.737 (±0.012), respectively). The <6-month OS model demonstrated 74% sensitivity and 68% specificity. The <12-month OS model demonstrated 63% sensitivity and 79% specificity. Model content and performance were generally comparable with previous studies. CONCLUSIONS: The prognostic value of the basic clinical information contained in these, and previously published models, is fundamentally of limited value in accurately predicting MPM prognosis. The methods described are suitable for expansion using emerging predictors, including tumour genomics and volumetric staging. BMJ Publishing Group 2018-01-30 /pmc/articles/PMC5812388/ /pubmed/29468073 http://dx.doi.org/10.1136/bmjresp-2017-000240 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
spellingShingle Pleural Disease
Kidd, Andrew C
McGettrick, Michael
Tsim, Selina
Halligan, Daniel L
Bylesjo, Max
Blyth, Kevin G
Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title_full Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title_fullStr Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title_full_unstemmed Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title_short Survival prediction in mesothelioma using a scalable Lasso regression model: instructions for use and initial performance using clinical predictors
title_sort survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors
topic Pleural Disease
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5812388/
https://www.ncbi.nlm.nih.gov/pubmed/29468073
http://dx.doi.org/10.1136/bmjresp-2017-000240
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