Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

Journal: International journal of radiation oncology, biology, physics

PMID: 29502932

Abstract

PURPOSE: Late genitourinary (GU) toxicity after radiation therapy limits the quality of life of prostate cancer survivors; however, efforts to explain GU toxicity using patient and dose information have remained unsuccessful. We identified patients with a greater congenital GU toxicity risk by identifying and integrating patterns in genome-wide single nucleotide polymorphisms (SNPs).

Authors

Sangkyu Lee

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Sarah Kerns

Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14620, USA.
Harry Ostrer

Department of Pathology, Albert Einstein College of Medicine, New York, NY 10461, USA.
Barry Rosenstein

Department of Radiation Oncology, Mount Sinai School of Medicine, New York, NY 10029, USA.
Joseph O Deasy

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Jung Hun Oh

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Keywords

Androgen Antagonists Area Under Curve Brachytherapy Confidence Intervals Genome-Wide Association Study Genotyping Techniques Humans Lower Urinary Tract Symptoms Machine Learning Male Nocturia Polymorphism, Single Nucleotide Predictive Value of Tests Prostatic Neoplasms Quality of Life Regression Analysis Reproducibility of Results Risk Symptom Assessment Urinary Retention Urination Urination Disorders Urogenital System

External Resources

View on PubMed Access via DOI PubMed (29502932)

Machine Learning on a Genome-wide Association Study to Predict Late Genitourinary Toxicity After Prostate Radiation Therapy.

Abstract

Authors

Keywords

External Resources

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