A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance...

Area of Science:

• Urology
• Computational Biology
• Medical Imaging

Background:

• Active surveillance (AS) is a standard management strategy for low-to-intermediate risk prostate cancer.
• Current AS relies on population-based monitoring, potentially delaying detection of tumor progression and limiting personalized management.
• There is a need for patient-specific tools to optimize AS protocols and improve early detection of disease progression.

Purpose of the Study:

• To develop and validate a personalized computational model for predicting prostate cancer growth.
• To identify novel biomarkers for higher-risk prostate cancer within the AS cohort.
• To assess the potential of the predictive model to enable earlier detection of disease progression and inform personalized AS plans.

Main Methods:

• A spatiotemporal biomechanistic model was developed and personalized using longitudinal multiparametric MRI (mpMRI) data from prostate cancer patients.
• The model's ability to forecast global tumor burden was evaluated using concordance correlation coefficients.
• A novel biomarker, mean tumor proliferation activity, was identified and used to develop a risk classifier via logistic regression.

Main Results:

• The personalized biomechanistic model accurately represented and forecasted global tumor burden in individual patients (CCC: 0.93-0.99).
• A model-based biomarker (mean proliferation activity) was identified as indicative of higher-risk prostate cancer (P = 0.041).
• The risk classifier achieved an AUC of 0.83, and coupling forecasts with the classifier enabled early identification of progression to higher-risk disease by over one year.

Conclusions:

• Personalized biomechanistic modeling of prostate cancer using mpMRI data can accurately predict tumor progression.
• A novel biomarker and risk classifier show promise for identifying higher-risk disease.
• This predictive technology offers a potential clinical decision-making tool for designing personalized AS plans, improving management of prostate cancer.