Prostate cancer is a major health problem among ageing men worldwide. The current clinical management of this pathology enables its detection at early organ-confined stages by combining regular screening and patient classification in risk groups.
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.
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.
Figure. Organ-scale, patient-specific prediction of prostate cancer growth. First, we obtain the imaging and clinical data from the patient. Then, a 3D geometric model of the prostate is created from the organ segmentation in anatomic magnetic resonance images. The tumor segmentation is projected over this geometric model. Finally, we run a personalized simulation of prostate cancer growth, estimating the parameters in the model equations from the patient’s longitudinal clinical and imaging data.