Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy.
Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy.
复制标题
前列腺放射治疗后患者生活质量的机器学习和统计预测。
DOI：
10.1016/j.compbiomed.2020.104127
复制
发表时间：
2021
影响因子：
7.7
通讯作者：
Shtylla,Blerta
中科院分区：
文献类型：
--
作者：
Yang,Zhijian;Olszewski,Daniel;He,Chujun;Pintea,Giulia;Lian,Jun;Chou,Tom;Chen,RonaldC;Shtylla,Blerta
由于诊断和治疗的进步，前列腺癌患者具有较高的长期生存率。目前，一个重要的目标是在治疗期间和治疗后保持生活质量。患者接受的辐射与他随后经历的副作用之间的关系很复杂，难以建模或预测。在这里，我们使用机器学习算法和统计模型来探索放射治疗与治疗后胃肠泌尿功能之间的联系。由于目前可用的患者数据集数量有限，因此我们使用图像翻转和基于曲率的插值方法来生成更多数据以利用迁移学习。使用插值和增强数据，我们训练了卷积自动编码器网络以获得接近最佳的权重起始点。然后，卷积神经网络分析了患者报告的生活质量与膀胱和直肠辐射剂量之间的关系。我们还使用方差分析和逻辑回归来探索器官对辐射的敏感性并制定每个器官区域的剂量阈值。我们的研究结果显示膀胱与生活质量评分之间没有统计学上的显着关联。然而，我们发现直肠后区和前区的辐射与生活质量的变化之间存在统计学上显着的关联。最后，我们估计了每个器官的放射治疗剂量阈值。我们的分析将机器学习方法与器官敏感性联系起来，从而提供了一个利用患者报告的生活质量指标来指导癌症患者护理的框架。
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.