Browsing by Author "Sun, Lingyue"

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    Application of Machine Learning in Prostate Cancer External Beam Radiation Therapy to Improve Biochemical Failure-Free Survival Prediction
    (2022-08-24) Sun, Lingyue; Smith, Wendy; Kirkby, Charles; Brown, Jo-Anne;; Quon, Harvey; McGeachy, Philip
    Curative external beam radiation therapy (EBRT) is a common treatment option for localized prostate cancer. The reported biochemical failure-free survival (BFFS) for prostate cancer patients post-EBRT varies significantly. The current, widely implemented clinical models that are used to predict treatment outcome for these patients have limited reliability. This thesis aims to examine the possible sources of influence on BFFS and improve EBRT treatment outcome prediction in terms of BFFS in particular using machine learning (ML) algorithms.Radiotherapy clinical trial protocols are adopted by different institutions due to their promising outcome(s) and/or superior efficiency. In this thesis, we first quantified the plan quality differences across four institutions in Alberta for PROstate Fractionated Irradiation Trial (PROFIT) plans. Despite the common guiding protocol, we observed statistically significant differences in dosimetric parameters between institutions; however, outcome modeling suggested such differences were of minimal clinical consequence.There is a need for more detailed understanding of the relationships between EBRT treatment planning and treatment delivery-related features and tumor control outcomes. We investigated the EBRT-related features that may be prognostic of BFFS using a random survival forest model. We found that the clinical target volume D99, pelvic irradiation, IGRT frequency, and planning target volume V98 were prognostic in addition to a set of tumor features.We then investigated whether ML-based models that incorporated additional EBRT treatment planning and delivery related features could perform better than the clinical models that were exclusively based on patient demographic and tumor features. We found that the two ML-based models outperformed the two clinical models on both the training and validation datasets, although all models’ performances deteriorated against the validation dataset.To further improve BFFS prediction, we investigated whether adding dosiomic features, which contains spatial information about the planned 3D dose distribution, could enhance the model performance. We did find improvement, and such improvement although relatively small was maintained when the model was validated with patients from other institutions. Additionally, we evaluated the impact of dose calculation variations on dosiomic features and found that most dosiomic features were stable against variations in dose calculation algorithms, versions, and dose grid.
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    Investigation of Volumetric Modulated Arc Therapy Dosimetry: from Planning to Delivery
    (2018-09-10) Sun, Lingyue; Smith, Wendy Lani; Kirkby, Charles; Quon, Harvey C.; Schinkel, Colleen
    Volumetric modulated arc therapy (VMAT) for cancer treatment uses high-energy radiation to kill malignant cells. The general goal is to deliver a high and conformal dose to the target while minimizing dose to organs-at-risk. This thesis focuses on two areas in VMAT: developing high quality treatment plans and assessing the dosimetric impact of delivering pre-designed plans to patients whose anatomy and position change during treatment. Firstly, a quantitative assessment was made by comparing prostate plans optimized with and without a planning target volume upper dose constraint. It was found that removing the target dose uniformity requirement resulted in better rectum sparing and improved tumor control probability. Secondly, the impact of body contour changes on dose distributions for prostate and head and neck VMAT plans was examined. Rules of thumb for dose percentage change and isodose line shift were developed and body contour changes unlikely to require further investigations were addressed.