A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing
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 experie...
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Main Authors | , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
21.05.2020
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Subjects | |
Online Access | Get full text |
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Summary: | 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 in order 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. We also used analysis
of variance and logistic regression to explore organ sensitivity to radiation
and develop dosage thresholds for each organ region. Our findings show no
connection between the bladder and quality-of-life scores. However, we found a
connection between radiation applied to posterior and anterior rectal regions
to changes in quality-of-life. Finally, we estimated radiation therapy dosage
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. |
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DOI: | 10.48550/arxiv.2005.10951 |