Prostate Cancer Early Detection 2.0: Prediction models and eHealth

Early detection of prostate cancer should be tailored to well informed men using a personalized strategy, supported by multivariable individual risk-stratification. Risk calculators are superior to PSA-based strategies, and their use is advised in guidelines. In this thesis, a pragmatic screening strategy is proposed, starting in the primary care setting, using the readily available and easy to use ERSPC RPCRC smartphone app. Prediction models are expected to improve through research in big-data, biomarkers, and imaging studies. This new paradigm of personalized risk assessment should stimulate individualized management and follow-up, promoting early detection of clinically significant prostate cancer, while reducing overdiagnosis and overtreatment. eHealth impact on Urology practice is expected to grow, supported by software and hardware improvements, innovative services, and original applications. In the case of prostate cancer screening, this can be materialized via decision support tools, empowering patients and assisting healthcare professionals, promoting true informed shared decisions.

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