Analysis of Radiation Therapy in Cancer Treatment using Machine Learning
Date
2021-09-24
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Abstract
Advancements in machine learning and data science have allowed researchers and clinicians to generate key insights from the vast amount of data generated in healthcare. This is currently a topic of research with great interest. With the advancement in algorithm design, and computing power, machine learning has proven to be a capable tool to augment or partially automate decision making. In this thesis, patient reported outcome surveys (PROs) for head and neck radiotherapy, and the relationship between the radiation dose distribution and breast size for whole-breast radiotherapy were investigated using statistical and machine learning methods. Two PRO measures; the M.D. Anderson symptom inventory for head and neck cancer, and the M.D. Anderson dysphagia inventory, were examined for a cohort of patients post radiotherapy for head and neck cancer. A strategy for administering a single PRO instrument is proposed which would reduce the questionnaire burden on patients, and allow physicians to identify patients who require specialized treatment for dealing with radiotherapy side-effects, such as referral to a dietician or speech language pathologist. Dosiomic features were extracted from the 3D radiation dose cloud in whole-breast radiotherapy plans. Feature reduction was achieved through hierarchical clustering and random forests were trained to stratify treated volume based on the distribution in the dose. Permutation feature importance was used to rank features’ classification utility in this task. The top 3 features were used to achieve superior performance when compared to the entire feature set. Dosiomics gives new insight into 3D dose distribution, and these features can be used in future studies to relate to treatment outcomes associated with whole breast radiotherapy for large volumes.
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Keywords
radiation therapy, MADI, MDASI, machine learning, clustering, random forest, cancer, radiotherapy, patient reported outcomes, dosiomics
Citation
Yarschenko, A. H. (2021). Analysis of Radiation Therapy in Cancer Treatment using Machine Learning (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.