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Personalised Image-based Computational Modelling Framework to Forecast Prostate Cancer

Project description

Computational model for surveilling prostate cancer

According to the World Cancer Research Fund, prostate cancer is the second most common cancer among men and the fourth most common cancer overall. While detection of prostate cancer has improved, monitoring its progression has proven to be more complicated. Cutting-edge multiparametric magnetic resonance imaging (mpMRI) provides high-quality information about tumour anatomy and physiology. PICModForPCa is developing a predictive, personalised model of prostate cancer based on mpMRI to better exploit the wealth of data available. Comparing a patient's imaging and clinical data at two different time points will enable personalised active surveillance that improves treatment, patient quality of life, and survival rates.


Prostate cancer (PCa) is a major health problem among ageing men worldwide, especially in Europe. However, the medical management of PCa only offers limited individualisation and has led to significant overtreatment and undertreatment, which may compromise patient quality of life and survival respectively.
Active surveillance is a clinical strategy in which patients with life-threatening PCa are directed to treatment while those with indolent tumours remain closely monitored via regular clinical tests and medical imaging. Multiparametric magnetic resonance imaging (mpMRI) provides high-quality data on PCa and is increasingly used in its diagnosis and surveillance, but computationally exploiting the wealth of data in these images to obtain precise information on tumour evolution to guide clinical management is an unresolved challenge.
To address this timely issue, this project proposes to derive a personalised predictive mathematical model of PCa based on mpMRI to run organ-scale simulations that improve diagnosis and forecast the patient’s tumour evolution. The model will rely on robust biological and mechanical phenomena described via differential equations whose parameters are identified voxel-wise by solving an inverse problem using the patient’s clinical and mpMRI data at two dates. The model will then be validated by comparing simulation and actual data at a posterior date. The resulting predictive technology offers an unparalleled advance to personalise and optimise active surveillance for PCa, hence meeting many European Commission priorities for research in cancer.
The candidate has previously developed computational models and methods to study PCa growth in clinical scenarios. Building on this ideal background, this project will provide him with crucial scientific techniques and skills to become a leading independent researcher, produce high-impact oral and written communications, and start an active network of collaborations between the US and Europe.



Net EU contribution
€ 251 002,56
Strada nuova 65
27100 Pavia

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Nord-Ovest Lombardia Pavia
Activity type
Higher or Secondary Education Establishments
Other funding
€ 0,00

Partners (1)