Imperial College London
Alternate

ProfessorEricAboagye

Faculty of Medicine, Department of Surgery & Cancer

Professor
 
 
 
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Contact

 

+44 (0)20 3313 3759eric.aboagye

 
 
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Assistant

 

Mrs Maureen Francis +44 (0)20 7594 2793

 
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Location

 

GN1Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hindocha:2022:10.1016/j.ebiom.2022.103911,
author = {Hindocha, S and Charlton, TG and Linton-Reid, K and Hunter, B and Chan, C and Ahmed, M and Robinson, EJ and Orton, M and Ahmad, S and McDonald, F and Locke, I and Power, D and Blackledge, M and Lee, RW and Aboagye, E},
doi = {10.1016/j.ebiom.2022.103911},
journal = {EBioMedicine},
title = {A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models.},
url = {http://dx.doi.org/10.1016/j.ebiom.2022.103911},
volume = {77},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundSurveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variety of health conditions. The purpose of this study was to utilise readily available patient, tumour, and treatment data to develop, validate and externally test machine learning models for predicting recurrence, recurrence-free survival (RFS) and overall survival (OS) at 2 years from treatment.MethodsA retrospective, multicentre study of patients receiving curative-intent radiotherapy for NSCLC was undertaken. A total of 657 patients from 5 hospitals were eligible for inclusion. Data pre-processing derived 34 features for predictive modelling. Combinations of 8 feature reduction methods and 10 machine learning classification algorithms were compared, producing risk-stratification models for predicting recurrence, RFS and OS. Models were compared with 10-fold cross validation and an external test set and benchmarked against TNM-stage and performance status. Youden Index was derived from validation set ROC curves to distinguish high and low risk groups and Kaplan-Meier analyses performed.FindingsMedian follow-up time was 852 days. Parameters were well matched across training-validation and external test sets: Mean age was 73 and 71 respectively, and recurrence, RFS and OS rates at 2 years were 43% vs 34%, 54% vs 47% and 54% vs 47% respectively. The respective validation and test set AUCs were as follows: 1) RFS: 0·682 (0·575–0·788) and 0·681 (0·597–0·766), 2) Recurrence: 0·687 (0·582–0·793) and 0·722 (0·635–0·81), and 3) OS: 0·759 (0·663–0·855) and 0·717 (0·634–0·8). Our models were superior to TNM stage and performan
AU  - Hindocha,S
AU  - Charlton,TG
AU  - Linton-Reid,K
AU  - Hunter,B
AU  - Chan,C
AU  - Ahmed,M
AU  - Robinson,EJ
AU  - Orton,M
AU  - Ahmad,S
AU  - McDonald,F
AU  - Locke,I
AU  - Power,D
AU  - Blackledge,M
AU  - Lee,RW
AU  - Aboagye,E
DO  - 10.1016/j.ebiom.2022.103911
PY  - 2022///
SN  - 2352-3964
TI  - A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models.
T2  - EBioMedicine
UR  - http://dx.doi.org/10.1016/j.ebiom.2022.103911
UR  - http://hdl.handle.net/10044/1/95806
VL  - 77
ER  -
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