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Personalized forecasting of prostate cancer growth through advanced nonlinear biomechanical model supported by locally adaptive, locking-free isogeometric methods

Project description

Precision in prostate cancer predictions

Prostate cancer is one of the most common in men but is still not diagnosed or treated with accuracy. Many patients receive unnecessary interventions or do not receive timely treatment due to generic clinical pathways. Supported under the Marie Skłodowska-Curie Actions programme, the PADLOCK project will develop an effective, personalised tool to predict tumour growth. Through the combination of state-of-the-art imaging with state-of-the-art computation, such as isogeometric analysis and adaptive modelling, the project advances the accuracy of cancer growth modelling. PADLOCK introduces innovations to overcome limitations within state-of-the-art models to pave the way for digital twins that enable personalised treatment options. The project has the potential to provide better prostate cancer treatment.

Objective

Prostate cancer (PCa) is the second most diagnosed cancer and the fifth leading cause of cancer-related deaths among men worldwide in 2022. However, current clinical practice often remains inadequate with limited individualization, leading to overdiagnosis, overtreatment, and undertreatment. A spatiotemporal biomechanistic model informed by multiparametric magnetic resonance imaging offers a powerful tool to address these challenges. Isogeometric analysis (IGA) and local h-adaptivity further contribute to accurately represent the complex biostructures and capture tumor dynamics. Hence, this technology enables personalized forecasting of PCa growth following its first diagnosis, leading to improved clinical and therapeutic decision-making. However, the existing models of PCa growth present fundamental limitations in predictive accuracy and computational efficiency. In this project, we will address three issues underlying those limitations: a suboptimal adaptive scheme for IGA, susceptibility to numerical locking due to the near-incompressible mechanical behavior of biostructures, and the assumption of linear elasticity. Towards this end, this project will follow a multifaceted approach that includes the integration of truncated hierarchical B-splines to enhance the adaptive IGA scheme, the use of mixed formulations to circumvent the adverse effects of volumetric locking while modeling near-incompressible biological tissues, and the incorporation of geometric and material nonlinearities to improve the accuracy of displacement and stress fields. Through these transformative strategies, the proposed model will represent a substantial step forward in the development of clinically viable predictive digital twins for PCa growth. This project aligns closely with the objectives of the European Commission’s cancer mission and could positively impact individuals with PCa. My expertise in IGA, locking, and biomechanics, uniquely equips me to achieve the project's objectives.

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Topic(s)

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

UNIVERSITA DEGLI STUDI DI PAVIA
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 209 483,28
Address
STRADA NUOVA 65
27100 Pavia
Italy

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Region
Nord-Ovest Lombardia Pavia
Activity type
Higher or Secondary Education Establishments
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Total cost

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Partners (2)

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