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A data science approach for early-stage prediction of patient’s susceptibility to acute side effects of advanced radiotherapy

Aldraimli, Mahmoud, Soria, Daniele, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Oliveira, Jorge, Samuel, Robert, Shelley, Leila E.A., Osman, Sarah, and others. (2021) A data science approach for early-stage prediction of patient’s susceptibility to acute side effects of advanced radiotherapy. Computers in Biology and Medicine, . Article Number 104624. ISSN 0010-4825. E-ISSN 1879-0534. (doi:10.1016/j.compbiomed.2021.104624) (KAR id:89070)

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The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventynine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations

from the international study of RT related toxicity “REQUITE”. We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers’ attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged between 0.50 and 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in

producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.

Item Type: Article
DOI/Identification number: 10.1016/j.compbiomed.2021.104624
Uncontrolled keywords: Classification; REQUITE; Machine Learning; Imbalanced Learning; Radiotherapy; Early Toxicities.; SMOTE; Meta-Learning; Desquamation.
Subjects: Q Science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Daniel Soria
Date Deposited: 07 Jul 2021 14:05 UTC
Last Modified: 05 Jul 2022 23:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
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