Towards personalized PTV margins for external beam radiation therapy of the prostate




Coathup, Andrew

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External Beam Radiation Therapy (EBRT) is a common treatment option for patients with prostate cancer. When treating the prostate with EBRT, a geometric volume (PTV margin) is added around the prostate to account for uncertainties in treatment planning and delivery. Current methods for estimating PTV margins rely on the analysis of population-based inter- and intra-fraction motion data. These methods do not consider the patient-to-patient differences in demographic or clinical presentation of patient specific factors (PSFs), such as age, weight, body-mass index, health and performance status, prostate-specific antigen levels, Gleason scores, presence of bowel problems, or other health conditions. The purpose of this thesis is to investigate the feasibility using regression-based predictive algorithms to predict the extent of prostate motion for the purpose of personalizing the PTV margin using PSFs as inputs. Benchmarking simulations of Linear, Ridge, LASSO, SVR, kNN, and MLP algorithms were performed by simulating prostate intra-fraction motion and realistic variations in PSFs. Sample sizes ranged from n=20 to 800, with varying levels of noise into the motion data (0-10mm). Leave-one-out cross validation was used to train and validate algorithm performance. The results suggest that algorithm performance improves significantly within the first 50 – 100 patients, and this rate of improvement is independent of noise in prostate motion. The Ridge regression algorithm predicted intra-fraction motion to the lowest mean absolute error in simulated motion, performing especially well in small datasets. To evaluate the clinical utility of this approach, pre- and post-treatment prostate motion data, treatment time data, and rectal distension data was recorded in 21 patients, along with a variety of PSFs. In the analysis of patient data, the LASSO algorithm out-performed the Ridge algorithm, predicting the mean and standard deviation of an individual prostate cancer patient’s intra-fraction motion to within 0.8mm and 0.4 mm mean absolute error, respectively. However, prostate motion predictions did not correlate with PSFs, possibly due to the small sample size. This work demonstrates the feasibility of using regression-based algorithms for predicting prostate motion, and hence the opportunity to personalize PTV margins in prostate cancer patients.



Radiation Therapy, Intra-fraction motion, Personalization, Prostate, PTV Margin, Machine Learning