Gilmer Valdes1, Albert J Chang2, Yannet Interian3, Kenton Owen2, Shane T Jensen4, Lyle H Ungar5, Adam Cunha2, Timothy D Solberg2, I-Chow Hsu2. 1. Radiation Oncology Department, University of California San Francisco, San Francisco, California. Electronic address: gilmer.valdes@ucsf.edu. 2. Radiation Oncology Department, University of California San Francisco, San Francisco, California. 3. Data Analytic Program, University of San Francisco, San Francisco, California. 4. Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania. 5. Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania.
Abstract
PURPOSE: 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. METHODS AND MATERIALS: We analyzed data for 52 patients treated with sHDRB for locally recurrent prostate cancer after previous definitive radiation therapy between 1998 and 2009. Following pathologic confirmation of locally recurrent disease without evidence of metastatic disease, 36 Gy in 6 fractions was administered to the prostate and seminal vesicles. BF following sHDRB was defined using the Phoenix definition. Sixteen different clinical risk features were collected, and machine learning analysis was executed to identify subpopulations at higher risk of BF. Decision tree-based algorithms including classification and regression trees, MediBoost, and random forests were constructed. RESULTS: Patients were followed up for a minimum of 5 years after sHDRB. Those with a fraction of positive cores ≥0.35 and a disease-free interval <4.12 years after their initial radiation treatment experienced a higher failure rate after sHDRB of 0.75 versus 0.38 for the rest of the population. CONCLUSIONS: Using machine learning, we have identified that patients with a fraction of positive cores ≥0.35 and a disease-free interval <4.1 years might be associated with a high risk of BF following sHDRB.
PURPOSE: 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. METHODS AND MATERIALS: We analyzed data for 52 patients treated with sHDRB for locally recurrent prostate cancer after previous definitive radiation therapy between 1998 and 2009. Following pathologic confirmation of locally recurrent disease without evidence of metastatic disease, 36 Gy in 6 fractions was administered to the prostate and seminal vesicles. BF following sHDRB was defined using the Phoenix definition. Sixteen different clinical risk features were collected, and machine learning analysis was executed to identify subpopulations at higher risk of BF. Decision tree-based algorithms including classification and regression trees, MediBoost, and random forests were constructed. RESULTS:Patients were followed up for a minimum of 5 years after sHDRB. Those with a fraction of positive cores ≥0.35 and a disease-free interval <4.12 years after their initial radiation treatment experienced a higher failure rate after sHDRB of 0.75 versus 0.38 for the rest of the population. CONCLUSIONS: Using machine learning, we have identified that patients with a fraction of positive cores ≥0.35 and a disease-free interval <4.1 years might be associated with a high risk of BF following sHDRB.
Authors: Ana Barragán-Montero; Umair Javaid; Gilmer Valdés; Dan Nguyen; Paul Desbordes; Benoit Macq; Siri Willems; Liesbeth Vandewinckele; Mats Holmström; Fredrik Löfman; Steven Michiels; Kevin Souris; Edmond Sterpin; John A Lee Journal: Phys Med Date: 2021-05-09 Impact factor: 2.685