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PICModForPCa

Financiado

Cerrado

Personalised Image based Computational Modelling Framework to Forecast Prostate...

Personalised Image based Computational Modelling Framework to Forecast Prostate Cancer 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 undertreat... ver más

31/08/2023

UNIPV

251K€

Presupuesto del proyecto: 251K€

Líder del proyecto

UNIVERSITA DEGLI STUDI DI PAVIA No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-08-31

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

UNIPV

251.00K€ | Lider

THE UNIVERSITY OF TEXAS SYST...

N.D. | Participante

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Duración del proyecto: 53 meses Fecha Inicio: 2019-03-20
Fecha Fin: 2023-08-31

Líder del proyecto

UNIVERSITA DEGLI STUDI DI PAVIA No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 251K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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