However, the limited individualization of the clinical management beyond risk-group definition has led to significant overtreatment and undertreatment rates, which might adversely impact the patients’ lives and life expectancy, respectively. Thus, prostate cancer is a paradigmatic disease in which an individualized predictive technology could make a crucial difference in clinical practice, thereby separating less aggressive tumors that could be safely monitored from lethal tumors that require immediate treatment.
To address this critical need, I leverage routine patient-specific clinical and imaging data to construct and parameterize personalized mathematical models of prostate cancer growth, with which I can perform computational forecasts of the patient's tumor prognosis to improve diagnosis and clinical decision-making on a patient-specific basis.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 838786.