Patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model

TL;DR

This study develops a personalized biomechanistic model to forecast prostate cancer growth using patient-specific imaging data, enabling earlier detection of disease progression during active surveillance.

Contribution

It introduces a novel patient-specific computational model that predicts tumor growth and risk, improving personalized monitoring over standard methods.

Findings

High concordance between predictions and actual tumor burden (0.93-0.99 CC)

Identification of a tumor proliferation biomarker with statistical significance (p=0.041)

Early detection of progression by over one year using combined forecasts and risk classifier

Abstract

Active surveillance (AS) is a suitable management option for newly-diagnosed prostate cancer (PCa), which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor closely monitored via longitudinal multiparametric magnetic resonance imaging (mpMRI), serum prostate-specific antigen tests, and biopsies. Hence, the patient is prescribed treatment when these tests identify progression to higher-risk PCa. However, current AS protocols rely on detecting tumor progression through direct observation according to standardized monitoring strategies. This approach limits the design of patient-specific AS plans and may lead to the late detection and treatment of tumor progression. Here, we propose to address these issues by leveraging personalized computational predictions of PCa growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data. Our results show that our predictive technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients ranging from 0.93 to 0.99 across our cohort (n=7). Additionally, we identify a model-based biomarker of higher-risk PCa: the mean proliferation activity of the tumor (p=0.041). Using logistic regression, we construct a PCa risk classifier based on this biomarker that achieves an area under the receiver operating characteristic curve of 0.83. We further show that coupling our tumor forecasts with this PCa risk classifier enables the early identification of PCa progression to higher-risk disease by more than one year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for PCa patients.