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.

Computers in biology and medicine. 2020 Nov 28 [Epub ahead of print]

Zhijian Yang, Daniel Olszewski, Chujun He, Giulia Pintea, Jun Lian, Tom Chou, Ronald C Chen, Blerta Shtylla

New York University, New York, NY, 10012, USA; Applied Mathematics and Computational Science Program, University of Pennsylvania, Philadelphia, PA, 19104, USA., Carroll College, Helena, MT, 59625, USA; Computer, Information Science and Engineering Department, University of Florida, Gainesville, FL, 32611, USA., Smith College, Northampton, MA, 01063, USA., Simmons University, Boston, MA, USA; Department of Psychology, Tufts University, Boston, MA, 02111, USA., Department of Radiation Oncology, The University of North Carolina, Chapel Hill, NC, 27599, USA., Depts. of Computational Medicine and Mathematics, UCLA, Los Angeles, CA, 90095-1766, USA., Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS, 66160, USA., Department of Mathematics, Pomona College, Claremont, CA, 91711, USA; Early Clinical Development, Pfizer Worldwide Research, Development, and Medical, Pfizer Inc, San Diego, CA, 92121, USA. Electronic address: .

PubMed http://www.ncbi.nlm.nih.gov/pubmed/33333364

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Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. - UroToday