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209P Adaptive recurrence risk prediction to optimise prostate cancer follow-up using updateable machine learning
0
Zitationen
7
Autoren
2025
Jahr
Abstract
A large number of prostate cancer survivors start follow-up after radical prostatectomy (RARP), requiring regular prostate-specific antigen (PSA) testing to detect biochemical recurrence (BCR). Current fixed schedules are not evidence-based and place a high burden on both patients and healthcare systems. More efficient strategies, such as risk stratification, could lower this burden. Existing risk-stratification tools lack accuracy, potentially because they solely provide static predictions that cannot be adapted over time.
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