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Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate

Yvonka van Wijk1, Bram Ramaekers2, Ben G L Vanneste3

  • 1The D-Lab, Department of Precision Medicine, GROW-School for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands.

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Summary

This study developed a decision support system (DSS) for prostate cancer treatment selection, recommending radical prostatectomy (RP) or external beam radiotherapy (EBRT). The DSS demonstrated cost savings and improved quality-adjusted life years (QALYs) compared to random selection.

Keywords:
cost-effectivenessdecision support systemexternal beam radiotherapyin silico trialprostate cancerradical prostatectomy

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Area of Science:

  • Oncology
  • Medical Informatics
  • Health Economics

Background:

  • Selecting between radical prostatectomy (RP) and external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer requires careful consideration of efficacy and toxicity.
  • Existing decision-making processes may not fully optimize patient outcomes or resource allocation.

Purpose of the Study:

  • To develop and evaluate a decision support system (DSS) for guiding treatment selection between RP and EBRT in prostate cancer patients.
  • To assess the cost-effectiveness and impact on quality-adjusted life years (QALYs) of the DSS compared to randomized treatment assignment.

Main Methods:

  • An individual state-transition model was employed, incorporating predictive models for tumor control and toxicity probabilities.
  • A synthetically generated dataset of 1000 patients was used for analysis, with external validation against randomized clinical trials.
  • An in silico clinical trial was conducted for elderly patients to evaluate treatment decision factors and quality of life (QoL).

Main Results:

  • The DSS recommended RP for 47.8% and EBRT for 52.2% of synthetic patients.
  • External validation showed minimal differences (≤2%) in predicted late toxicity and biochemical failure compared to clinical trial data.
  • In silico trials indicated toxicity significantly influences decisions for elderly patients, and predicted QoL is linked to baseline erectile function.

Conclusions:

  • The developed DSS effectively guides treatment selection for prostate cancer, balancing efficacy and toxicity.
  • The DSS is projected to be cost-effective, yielding significant cost savings and improved QALYs over randomized selection.
  • The system provides personalized treatment recommendations, considering patient-specific factors like age and baseline function, particularly for elderly populations.