Investigation of adaptive radiation therapy including deformable image registration, treatment planning modification strategies, machine learning & deep learning
| dc.contributor.author | Siciarz, Pawel | |
| dc.contributor.examiningcommittee | VanUytven, Eric (CancerCare Manitoba) Rivest, Ryan (CancerCare Manitoba) Fiege, Jason (Physics and Astronomy) Thomas, Gabriel (Electrical and Computer Engineering) Eugene Wong (Western University) | en_US |
| dc.contributor.supervisor | McCurdy, Boyd (Physics and Astronomy) | en_US |
| dc.date.accessioned | 2021-11-08T15:20:33Z | |
| dc.date.available | 2021-11-08T15:20:33Z | |
| dc.date.copyright | 2021-10-22 | |
| dc.date.issued | 2021-10-22 | en_US |
| dc.date.submitted | 2021-10-23T03:41:00Z | en_US |
| dc.degree.discipline | Physics and Astronomy | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | The goal of this research was to propose and evaluate solutions to four important aspects of adaptive radiation therapy in order to make it more reliable, accurate, and efficient in clinical environment. The first study focused on the evaluation of several deformable image registration algorithms. Results demonstrated that the Dense Anatomical Block Matching registration outperformed the other methods making it a very promising alternative to the existing registration methods for challenging CT-to-CBCT registration and its applications for radiation dose calculation, dose mapping and contour propagation in adaptive radiation therapy (ART) of the pelvic region. The second study focused on the quantitative evaluation of eight proposed adaptive radiation therapy approaches for prostate cancer patients treated with hypofractionated VMAT. The ART strategies included online and offline methods. The comprehensive analysis showed that daily on-line adaptation approaches were the most impactful. The findings of this study provided applicable insights into the selection of the optimal ART strategy, improving the quality of the decision-making process based on the quantitatively evaluated dosimetric benefits. The third study aimed to utilize a deep learning network to automatically contour critical organs on the computed tomography (CT) scans of head and neck cancer patients. Proposed model achieved expert level accuracy and was able to segment 25 critical organs on unseen CT images in approximately 7 seconds per patient. High accuracy and short contouring time allow for the implementation of the model within a clinical ART workflow, which would lead to a significant decrease in the time required to create a new adapted treatment plan. The objective of the fourth study was to use artificial intelligence methods to build a decision making support system that would classify previously delivered plans of brain tumor patients into those that met treatment planning objectives and those for which objectives were not met due to the priority given to one or more organs-at-risk. Among evaluated machine learning algorithms, the Logistic Regression model achieved the highest accuracy and can be used by radiation oncologists to support their decision-making process in terms of treatment plan adaptations and plan approvals in a data-driven quality assurance program. | en_US |
| dc.description.note | February 2022 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/36113 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Adaptive Radiation Therapy | en_US |
| dc.subject | Deformable Image Registration | en_US |
| dc.subject | Artificial Intelligence | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Deep Learning | en_US |
| dc.subject | Medical Image Segmentation | en_US |
| dc.title | Investigation of adaptive radiation therapy including deformable image registration, treatment planning modification strategies, machine learning & deep learning | en_US |
| dc.type | doctoral thesis | en_US |