Machine Learning Framework to Reduce Patient-Reported Dysphagia in Head and Neck Radiotherapy

Paetkau, D. Owen

Machine Learning Framework to Reduce Patient-Reported Dysphagia in Head and Neck Radiotherapy

dc.contributor.advisor	Kirkby, Charles
dc.contributor.advisor	Tchistiakova, Ekaterina
dc.contributor.author	Paetkau, D. Owen
dc.contributor.committeemember	Gomes da Rocha, Claudia
dc.contributor.committeemember	Quon, Harvey Charles
dc.contributor.committeemember	Shinkel, Colleen Gayle
dc.date.accessioned	2024-06-13T17:01:31Z
dc.date.available	2024-06-13T17:01:31Z
dc.date.issued	2024-06-13
dc.description.abstract	Radiotherapy treatment has become more effective in the 21st century due to advancements in treatment technologies such as image-guided radiotherapy and volumetric modulated arc therapy. Machine learning algorithms can provide advanced analytics based on the clinical, imaging, and dosimetric data available through radiotherapy treatments. Head and neck tumours are a site of specific interest, as these tumours often lie near many organs at risk which may cause radiation-induced acute or late toxicities while damaging the tumour. Among these organs are the muscles involved in the swallowing process that may cause dysphagia, or difficulty swallowing, after treatment. In this work, we investigated factors correlated with late dysphagia evaluated using patient-reported outcomes, with the goal of reducing this significant toxicity after head and neck radiotherapy. We examined anatomical changes to the pharyngeal constrictor muscles hypothesized to be correlated with dysphagia or cause significant dosimetric changes outside the random error associated with radiotherapy treatments. We found a significant increase in pharyngeal constrictors thickness and identified high dose gradients were closer to the pharyngeal constrictors in patients with dysphagia. Both features may be useful for identifying patients at risk of late patient-reported dysphagia. We then examined treatment planning dose constraints used to develop radiotherapy treatment plans. The mean dose constraints currently used within the literature did not classify patients well into the patient-reported symptom groups. Metrics were identified for the pharyngeal constrictor muscles and their substructures. The results suggest additional dose constraints to the pharyngeal constrictor muscles could reduce late patient-reported dysphagia. The final study examined advanced imaging features to develop predictive machine learning models for late patient-reported dysphagia. Current models used by speech language pathologists are limited to acute models. We successfully created models to predict late patient-reported dysphagia with sufficient accuracy and sensitivity, which may improve the identification and follow-up of patients at risk. Through this work, we were able to create recommendations based on several forms of intervention: adaptive planning, treatment planning, and predictive modelling. Further work, including prospective studies, may be required prior to clinical implementation of treatment planning or predictive modelling recommendations.
dc.identifier.citation	Paetkau, D. O. (2024). Machine learning framework to reduce patient-reported dysphagia in head and neck radiotherapy (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118947
dc.identifier.uri	https://doi.org/10.11575/PRISM/46543
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	dysphagia
dc.subject	radiotherapy
dc.subject	head and neck
dc.subject	machine learning
dc.subject.classification	Physics--Radiation
dc.subject.classification	Speech Pathology
dc.subject.classification	Biophysics--Medical
dc.title	Machine Learning Framework to Reduce Patient-Reported Dysphagia in Head and Neck Radiotherapy
dc.type	doctoral thesis
thesis.degree.discipline	Physics & Astronomy
thesis.degree.grantor	University of Calgary
thesis.degree.name	Doctor of Philosophy (PhD)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.