Prostate Cancer Relapse Assessment Based on Optimised Outlier Detection

The detection of recurrent prostate cancer following external beam radiotherapy relies on persistent increases in serum prostate-specific antigen (PSA) levels. However, this biochemical recurrence may take place after an extended period, delaying secondary treatment for patients with relapsing tumours. Recent research has identified situations where anticipating PSA value relapse, often linked with tumour recurrence, is possible using personalized mechanistic models for PSA forecasting. Yet, these models lack insight into false negatives and their susceptibility to relapse. To address this problem, we propose to determine an optimal threshold for detecting PSA outliers. This research analyses patient data, particularly PSA value sequences, to determine whether the mechanistic model’s parameters representing an outlier of the parameter distribution exhibit a correlation with the characteristics of the patient’s relapse.