Predicting clinical outcomes of radiotherapy for head and neck squamous cell carcinoma patients using machine learning algorithms

Gangil, Tarun and Shahabuddin, Amina Beevi and Rao, Dinesh B and Sharan, Krishna (2022) Predicting clinical outcomes of radiotherapy for head and neck squamous cell carcinoma patients using machine learning algorithms. Journal of Big Data, 9. pp. 1-19. ISSN 2196-1115

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Background: Radiotherapy is frequently used to treat head and neck Squamous cell carcinomas (HNSCC). Treatment outcomes being highly uncertain, there is a signifcant need for robust predictive tools to improvise treatment decision-making and better understand HNSCC by recognizing hidden patterns in data. We conducted this study to identify if Machine Learning (ML) could accurately predict outcomes and identify new prognostic variables in HNSCC.Method: Retrospective data of 311 HNSCC patients treated with radiotherapy between 2013 and 2018 at our center and having a follow-up of at least three months’duration were collected. Binary-classifcation prediction models were developed for:Choice of Initial Treatment, Residual disease, Locoregional Recurrence, Distant Recurrence, and Development of New Primary. Clinical data were pre- processed using Imputation, Feature selection, Minority Oversampling, and Feature scaling algorithms.A method to retain original characteristics of dataset in testing samples while performing minority oversampling is illustrated. The classifcation comparison was performed using Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost classifcation algorithms for each model.Results: For the choice of the initial treatment model, the testing accuracy was 84.58% using RF. The distant recurrence, locoregional recurrence, new-primary, and residual models had a testing accuracy (using KSVM) of 95.12%, 77.55%, 98.61%, and 92.25%, respectively. The important clinical determinants were identifed using Shapely Values for each classifcation model, and the mean area under the curve (AUC) for the receiver operating curve was plotted.Conclusion: ML was able to predict several clinically relevant outcomes, and with additional clinical validation, could facilitate recognition of novel prognostic factors in HNSCC

Item Type: Article
Uncontrolled Keywords: Squamous cell head and neck cancer; Machine learning; Shapely values; Prognosis; Recurrence pattern; Feature selection; Missing value imputation
Subjects: Medicine > KMC Manipal > Radiotherapy and Oncology
Depositing User: KMC Library
Date Deposited: 18 Jul 2022 03:54
Last Modified: 18 Jul 2022 03:54

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