In-silico patient-specific modelling of prostate cancer: Predicting PSA dynamics and treatment response.

BACKGROUND AND OBJECTIVE

Prostate cancer remains a significant global health concern, with treatment response varying among patients. Radiotherapy, often combined with hormone therapy, is a key treatment approach, but predicting individual outcomes remains challenging. Computational models have emerged as valuable tools to simulate tumour behaviour and optimise treatment strategies. This study presents a patient-specific computational model designed to predict tumour response by associating Prostate-Specific Antigen (PSA) dynamics with tumour biological behaviour under therapy.

METHODS

The model integrates patient-specific clinical data and imaging biomarkers from a retrospective study, including apparent diffusion coefficient values from diffusion-weighted imaging to represent tumour cellularity and perfusion parameters from dynamic contrast-enhanced MRI to characterise vascular properties. Clinical data from five patients undergoing radiotherapy, hormone therapy, or combination therapy are used for model development and validation. Due to the limited availability of patient data, PSA is the only parameter used for calibration and validation. One patient is used for calibration, while six serve for validation. Model performance is evaluated by calculating the mean absolute error (MAE) between simulated and observed PSA values post-treatment. The model also estimates tumour shrinkage, though this cannot be directly validated. To assess predictive capacity, two patients are selected for additional analysis simulating different treatment strategies and their impact on PSA dynamics and tumour shrinkage.

RESULTS

The model successfully replicates PSA trends, with MAE values of 0.1, 0.08, 0.23, 0.14, 0.11 and 0.15 ng/mL and RMSE of 0.18, 0.15, 0.24, 0.18, 0.12 and 0.15 ng/mL for the six validation patients, with Patient C showing the closest correspondence to clinical data (MAE = 0.08). Overall, the MAE ranges from 0.08 ng/mL to 0.23 ng/mL, indicating the model's ability to approximate treatment response. In the two selected patients, simulated treatment variations result in distinct PSA dynamics and estimated tumour shrinkage, highlighting interpatient variability in treatment response.

CONCLUSIONS

This computational model provides a predictive framework for assessing prostate cancer treatment response based on patient-specific PSA dynamics and imaging biomarkers. While tumour shrinkage estimates cannot be validated, the model offers insights into treatment-induced PSA fluctuations. The findings support the potential of in-silico tools in personalised medicine, aiding clinical decision-making by evaluating different therapeutic strategies. Further validation with larger datasets is necessary for clinical integration.