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

Kidd, A. C., McGettrick, M., Tsim, S., Halligan, D. L., Bylesjo, M. and Blyth, K. G. (2018) Survival prediction in mesothelioma using a scalable lasso regression model: instructions for use and initial performance using clinical predictors. BMJ Open Respiratory Research, 5(1), e000240. (doi: 10.1136/bmjresp-2017-000240) (PMID:29468073) (PMCID:PMC5812388)

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Abstract

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 DXY 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 DXY0.332 (±0.019)). However, validation set DXY 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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Kidd, Andrew and Blyth, Dr Kevin and Tsim, Dr Selina
Authors: Kidd, A. C., McGettrick, M., Tsim, S., Halligan, D. L., Bylesjo, M., and Blyth, K. G.
College/School:College of Medical Veterinary and Life Sciences > Institute of Cancer Sciences
College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
Journal Name:BMJ Open Respiratory Research
Publisher:BMJ Publishing Group
ISSN:2052-4439
ISSN (Online):2052-4439
Published Online:30 January 2018

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
648131An examination of diagnostic and prognostic biomarkers in malignant pleural mesotheliomaKevin BlythChief Scientist office (CSO)ETM/285SCHOOL OF MEDICINE, DENTISTRY & NURSING
3010320PRISM - Prediction of Resistance to chemotherapy using Somatic Copy Number Variation in MesotheliomaKevin BlythBritish Lung Foundation (BLF)MPG16-7Institute of Infection, Immunity & Inflammation