Speaker
Description
Active surveillance is a common management strategy for patients with low-risk prostate cancer, aiming to delay or avoid invasive treatment while monitoring disease progression. However, interpreting longitudinal observations is challenging because the prostate itself can undergo significant physiological growth due to aging or benign prostatic hyperplasia. These baseline changes may obscure early indicators of clinically relevant disease progression. We present a mathematical framework for modeling prostate organ growth as a first step toward patient-specific digital twins for monitoring prostate cancer under active surveillance. Longitudinal magnetic resonance imaging (MRI) data are processed using automated segmentation methods to extract prostate geometries and estimate organ volumes at multiple time points. These imaging-derived measurements are combined with clinical variables such as prostate-specific antigen (PSA) levels. To describe the temporal evolution of the prostate, we employ a growth model based on partial differential equations that captures the gradual expansion of prostate tissue. Model parameters are estimated from longitudinal imaging data to characterize patient-specific growth trajectories. By explicitly accounting for benign organ growth, the model provides a baseline against which potential tumor-related changes can be assessed, supporting improved interpretation of surveillance data and the development of predictive digital twins.
