Thesis

Personalized Forecast of Prostate Adenocarcinoma Growth and Proton Therapy Response

• Call:

  ProtoTera Call 2023/1

• Academic Year:

  2023

• Supervisor:

  Rui Travasso

• Co-Supervisor:

  Guillermo Lorenzo

• Host Institution:

  CFisUC – Centro de Física da Universidade de Coimbra

• Granting Degree Institution:

  Universidade de Coimbra

• Typology:

  National

• Abstract:

  Prostate cancer (PCa) is a major health burden among aging men, exhibiting high incidence and mortality rates worldwide. The current clinical management of PCa enables its classification in risk groups. However, limited individualization has led to significant overtreatment and undertreatment rates, which may adversely impact the patients’ lives and life expectancy, respectively. Proton therapy has been applied in the treatment of advanced PCa. The success of this approach is a consequence of the precision of proton therapy, which can target the cancerous lesion with accuracy. But, to take full advantage of proton therapy it is fundamental to correlate the treatment plan with the three-dimensional patient-specific tumor morphology as measured using imaging techniques (e.g., magnetic resonance imaging, MRI) and to correctly assess PCa malignancy. We propose to use personalized PCa forecasts obtained from a robust biomathematical model informed by standard, longitudinal, patient-specific imaging and clinical data. Additionally, the project features the development of a pioneering model extension to describe and predict PCa response to proton therapy on a patient-specific basis. Finally, the ultimate goal of the project is to use this model to design optimal proton therapy regimens for each individual patient. The student will join an international high-productivity interdisciplinary collaboration with researchers from Portugal (U. Coimbra, U. Porto), Italy (U. Pavia), Spain (Health Research Institute, Santiago de Compostela), and USA (U. Texas, Austin). The student’s background is perfect for the project, having a track record in simulation of biological systems, microscopy and image processing. This will enable her to integrate the different parts of this interdisciplinary project with ease. This work will provide an invaluable learning opportunity to the student and generate a vast range of international contacts guaranteeing the success of her professional future.