Abstract

Stochastic modeling of the influence of dormant cancer cells in prostate cancer management aims to analyze and predict the behavior of these cells, particularly their role in cancer recurrence during treatment. Dormant cancer cells are part of the tumor but are not actively dividing; they exist in a resting state and remain inactive. This characteristic allows them to escape conventional therapies that primarily target actively dividing cells. Although these cells are currently dormant, they have the potential to reactivate and trigger cancer recurrence, sometimes even years after initial treatment. Using a stochastic model for prostate cancer, we assess the patient's condition at diagnosis, the proposed treatment strategy, the probability of recurrence, and the effectiveness of treatment. In summary, our approach enables the evaluation of various strategies to manage dormant cells more effectively and to prevent or control their potential reactivation. This may improve prostate cancer treatment and reduce the likelihood of recurrence, leading to more customized and effective therapeutic options.

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