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

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

Summary: Background: Surveillance 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 fo...

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Bibliographic Details
Main Authors: Sumeet Hindocha, Thomas G. Charlton, Kristofer Linton-Reid, Benjamin Hunter, Charleen Chan, Merina Ahmed, Emily J. Robinson, Matthew Orton, Shahreen Ahmad, Fiona McDonald, Imogen Locke, Danielle Power, Matthew Blackledge, Richard W. Lee, Eric O. Aboagye
Format: Article
Language:English
Published: Elsevier 2022-03-01
Series:EBioMedicine
Subjects:
Non-small cell lung cancer
Radiotherapy
Machine learning
Recurrence
Overall survival
Prediction
Online Access:http://www.sciencedirect.com/science/article/pii/S2352396422000950

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http://www.sciencedirect.com/science/article/pii/S2352396422000950

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