A machine learning approach to using Quality-of-Life patient scores in guiding prostate radiation therapy dosing

TL;DR

This study employs machine learning to analyze how radiation doses affect prostate cancer patients' quality-of-life, aiming to optimize treatment while minimizing side effects.

Contribution

It introduces a novel framework combining data augmentation, transfer learning, and neural networks to relate radiation therapy to patient-reported outcomes.

Findings

No link between bladder radiation and quality-of-life scores.

Radiation to rectal regions affects quality-of-life.

Estimated dosage thresholds for different organs.

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 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.