Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis

Valdes, Gilmer; Chang, Albert J.; Interian, Yannet; Owen, Kenton; Jensen, Shane T.; Ungar, Lyle H.; Cunha, Adam; Solberg, Timothy D.; Hsu, I-Chow

doi:10.1016/j.ijrobp.2018.03.001

DOE OSTI · 23065571

Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis

Valdes, Gilmer·Chang, Albert J.·Interian, Yannet·Owen, Kenton·Jensen, Shane T.·Ungar, Lyle H.·Cunha, Adam·Solberg, Timothy D.·Hsu, I-Chow

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Abstract

Salvage high-dose-rate brachytherapy (sHDRB) is a treatment option for recurrences after prior radiation therapy. However, only approximately 50% of patients benefit, with the majority of second recurrences after salvage brachytherapy occurring distantly. Therefore, identification of characteristics that can help select patients who may benefit most from sHDRB is critical. Machine learning may be used to identify characteristics that predict outcome following sHDRB. We aimed to use machine learning to identify patient characteristics associated with biochemical failure (BF) following prostate sHDRB.

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62 RADIOLOGY AND NUCLEAR MEDICINE APPROXIMATIONS BRACHYTHERAPY DOSE RATES HYPOTHESIS MACHINE LEARNING PATIENTS PROSTATE TESTING

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Valdes, Gilmer, Chang, Albert J., Interian, Yannet, Owen, Kenton, Jensen, Shane T., Ungar, Lyle H., Cunha, Adam, Solberg, Timothy D., Hsu, I-Chow. 2018-07-15. Salvage HDR Brachytherapy: Multiple Hypothesis Testing Versus Machine Learning Analysis. https://doi.org/10.1016/j.ijrobp.2018.03.001

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