Personalized PSA Kinetic Model for Early Prostate Cancer Relapse Prediction
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Solution Overview
Problem
Current methods for detecting biochemical relapse in prostate cancer patients after external beam radiation therapy (EBRT) are delayed, as they rely on sustained PSA rises, which can take years to occur, thereby delaying secondary treatments and reducing chances of successful disease control.
Innovation Solution
A method and system using patient-specific PSA data to fit personalized mechanistic models, predicting PSA values and identifying relapse earlier by calculating model parameters such as proliferation and death rates of prostate cancer cells, enabling early detection and treatment of tumor recurrence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard clinical criteria requiring sustained PSA rises are used to detect biochemical relapse, then detection accuracy is improved, but detection time is delayed
Solution Approach 1:
The patent applies preliminary action by using mechanistic models to predict future PSA trajectories and identify patients at risk of relapse before actual PSA rises occur. The models analyze current PSA dynamics to forecast future relapse events, enabling early intervention before the sustained PSA increase that defines biochemical relapse actually happens.
Solution Approach 2:
The patent inverts the traditional approach by not waiting for PSA to rise and then detecting relapse, but rather using PSA dynamics to predict and prevent relapse before it occurs. The mechanistic models work backward from expected relapse patterns to identify at-risk patients in the current moment, reversing the temporal sequence of detection.
2Loss of time
If early detection methods using mechanistic models are implemented, then detection time is reduced, but device complexity increases
Solution Approach 1:
The patent applies parameter changes by transforming routine PSA measurements into predictive parameters through mechanistic modeling. Instead of simply monitoring absolute PSA values, the system extracts kinetic parameters (growth rates, decay rates, doubling times) from PSA trajectories, converting static measurements into dynamic predictive indicators that enable early relapse detection.
Solution Approach 2:
The patent uses mechanistic models as intermediaries between routine PSA measurements and relapse prediction. The models serve as a computational layer that translates standard clinical data into predictive insights, bridging the gap between simple blood tests and complex relapse risk assessment without requiring direct complex measurements.
3Measurement precision
If mechanistic models are used to predict PSA values, then prediction accuracy is improved, but calculation complexity increases
Solution Approach 1:
The patent applies self-service by using the PSA data itself to generate the predictions it needs. The mechanistic models are calibrated using the patient's own PSA measurements and trajectories, allowing the system to self-adjust and predict future values without requiring external complex inputs or frequent recalibration with additional tests.
Solution Approach 2:
The patent applies dynamics by using dynamic mechanistic models that continuously adapt to changing PSA trajectories. Rather than static thresholds, the system employs differential equations that capture the evolving nature of PSA kinetics, allowing predictions to automatically adjust as the disease progresses or responds to treatment.
Data Source
AI summary
Methods of predicting relapse of a prostate cancer patient treated by radiation therapy, associated software, and systems for implementing such methods. Patient-specific PSA data are collected including np measurements of serum prostate specific antigen (PSA) at np dates for a newly-diagnosed prostate cancer patient before and after an external beam radiation therapy (EBRT) regimen. Patient-specific parameters P0, ρn, ρs, ρd, and RD are identified that render an optimal fit of the patient-specific PSA data to a personalized model. Using the personalized model and the patient-specific parameters, a set of one or more predicted PSA values are calculated at one or more time horizons from 0 to 2 years after the last PSA measurement. If and/or when a relapse of the prostate cancer patient will occur is predicted based on the one or more predicted PSA values.