Title
Machine Learning And Statistical Prediction Of Patient Quality-Of-Life After Prostate Radiation Therapy
Abstract
Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality of life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation doses to the bladder and rectum. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and to develop dosage thresholds for each organ region. Our findings show no statistically significant association between the bladder and quality-of-life scores. However, we found a statistically significant association between the radiation applied to posterior and anterior rectal regions and changes in quality of life. Finally, we estimated radiation therapy dose thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.
Year
DOI
Venue
2021
10.1016/j.compbiomed.2020.104127
COMPUTERS IN BIOLOGY AND MEDICINE
Keywords
DocType
Volume
Machine learning, Convolutional neural network, Radiation therapy, Organ sensitivity, Prostate cancer
Journal
129
ISSN
Citations 
PageRank 
0010-4825
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Zhijian Yang100.34
Daniel Olszewski200.34
Chujun He300.34
Giulia Pintea400.34
Jun Lian5837.32
Tom Chou613.05
Ronald Chen7162.04
Blerta Shtylla800.34