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Machine learning application in personalised lung cancer recurrence and survivability prediction

Yang, Yang; Xu, Li; Sun, Liangdong; Zhang, Peng; Farid, Suzanne S; (2022) Machine learning application in personalised lung cancer recurrence and survivability prediction. Computational and Structural Biotechnology Journal , 20 pp. 1811-1820. 10.1016/j.csbj.2022.03.035. Green open access

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Abstract

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

Type: Article
Title: Machine learning application in personalised lung cancer recurrence and survivability prediction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.csbj.2022.03.035
Publisher version: https://doi.org/10.1016/j.csbj.2022.03.035
Language: English
Additional information: Copyright © 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords: Machine learning, Decision tree, Lung cancer, Personalized diagnosis and prognosis
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10147012
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