Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

Journal: The British journal of radiology

Published Date: Apr 1, 2021

Abstract

OBJECTIVES: Mandible osteoradionecrosis (ORN) is one of the most severe toxicities in patients with head and neck cancer (HNC) undergoing radiotherapy (RT). The existing literature focuses on the correlation of mandible ORN and clinical and dosimetric factors. This study proposes the use of machine learning (ML) methods as prediction models for mandible ORN incidence.

Authors

Laia Humbert-Vidan

Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK.
Vinod Patel

Department of Oral Surgery, Guy's and St Thomas' Hospital, London, United Kingdom.
İlkay Öksüz

İstanbul Technical University Faculty of Engineering, Department of Computer Engineering, İstanbul, Türkiye.
Andrew Peter King

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Teresa Guerrero Urbano

Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK.

Keywords

Female Head and Neck Neoplasms Humans Incidence Machine Learning Male Mandible Middle Aged Osteoradionecrosis Predictive Value of Tests Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (33684314)

Comparison of machine learning methods for prediction of osteoradionecrosis incidence in patients with head and neck cancer.

Abstract

Authors

Keywords

External Resources

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