Patient-specific forecasting of prostate cancer progression to higher-risk disease during active surveillance

GuillermoLorenzo

Group of Numerical Methods, Department of Mathematics, University of A Coruna, Spain

"Patient-specific forecasting of prostate cancer progression to higher-risk disease during active surveillance"

Prostate cancer (PCa) usually exhibits low or intermediate risk at diagnosis, for which active surveillance (AS) is an established clinical option. Patients in AS are monitored via serum Prostate Specific Antigen (PSA), multiparametric magnetic resonance imaging (mpMRI), and biopsies. If these exams indicate tumor progression to higher-risk disease, curative treatment is typically recommended (e.g., surgery, radiotherapy). Hence, AS combats overtreatment of indolent PCa, thereby avoiding unnecessary treatment that can induce side effects reducing quality of life (e.g., incontinence, impotence) but without prolonging longevity. However, monitoring protocols for AS rely on an observational and population-based approach that does not account for the heterogeneous nature of PCa dynamics and cannot provide an early identification of progressing patients. To address these two critical limitations, we propose using personalized predictions of tumor progression based on biomechanistic features that describe the heterogeneous disease dynamics for each patient (e.g., tumor cell density, proliferation activity). To this end, we first calculate these patient-specific features from MRI-informed, organ-scale predictions of a biomechanistic model of prostate cancer growth. Then, a generalized logistic classifier is leveraged to map these features to risk groups. Since our PCa growth predictions are spatiotemporally-resolved, we can calculate the biomechanistic features describing PCa dynamics and the tumor risk over time, thus enabling the calculation of time to progression for each patient. Here, we present a preliminary study of our approach in which we demonstrate the accuracy of our personalized growth and progression predictions in a small patient cohort. Although further improvement and testing in larger cohorts are required, we believe that our predictive technology could be leveraged to inform clinical decision-making and personalize AS protocols for PCa patients.
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