Description du projet
Un modèle informatique de surveillance du cancer de la prostate
Selon le Fonds mondial de recherche contre le cancer, le cancer de la prostate est le deuxième cancer en importance chez les hommes et le quatrième au niveau global. Bien que la détection du cancer de la prostate se soit améliorée, la surveillance de son évolution s’est avérée plus compliquée. L’imagerie par résonance magnétique multiparamétrique (IRMmp) de pointe fournit des informations de haute qualité sur l’anatomie et la physiologie de la tumeur. Le projet PICModForPCa développe un modèle prédictif et personnalisé du cancer de la prostate basé sur l’IRMmp pour mieux exploiter la richesse des données disponibles. La comparaison des données d’imagerie et des données cliniques d’un patient à deux moments différents permettra une surveillance active personnalisée qui améliorera le traitement, la qualité de vie du patient et ses chances de survie.
Objectif
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.
Champ scientifique
- medical and health sciencesclinical medicineoncologyprostate cancer
- natural sciencesmathematicspure mathematicsmathematical analysisdifferential equations
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
- natural sciencesmathematicsapplied mathematicsmathematical model
Mots‑clés
Programme(s)
Régime de financement
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinateur
27100 Pavia
Italie