Project description
Automating and optimising proton therapy treatment workflows
Proton therapy offers high-precision tumour targeting, but the treatment planning process is complex and time-consuming. Conventional treatment planning systems only adjust beam fluences and address broad uncertainty margins, while operators must manually select beam orientations and energy layers. This leads to heavy operator reliance, lengthy computation times and inconsistent plan quality. Supported by the Marie Skłodowska-Curie Actions programme, the PRODIGIO project aims to address these limitations by creating an AI-driven dose engine trained on Monte Carlo simulations that can compute highly precise dose maps in milliseconds. This dose engine will then be integrated into an optimisation pipeline that synchronises optimal fluences, energies and orientations using gradient-based algorithms that explicitly address uncertainties.
Objective
PRODIGIO (PROton therapy DIfferentiable Gradient-based Inverse Optimization) aims to develop the first uncertainty-aware auto-planning framework for Intensity-Modulated Proton Therapy (IMPT).
This project addresses a critical gap in current treatment planning systems, which optimise only beam fluences while keeping orientations and energy layers fixed and manually chosen by the operator, and account for uncertainties through simplistic global margins. These limitations restrict plan quality, increase computation times, and lead to strong operator dependence.
At its core, PRODIGIO will develop a deep-learning dose engine trained on Monte Carlo simulations, capable of producing millisecond-accurate dose maps. Differentiable estimates of motion- and density-related uncertainties will be incorporated using advanced methods such as polynomial chaos expansion. This engine will be embedded in a novel optimisation pipeline where fluences, energies, and orientations are simultaneously optimised with gradient-based algorithms that explicitly account for uncertainties.
The project is highly interdisciplinary, positioned at the intersection of medical physics, numerical optimisation, and clinical practice. It involves three world-leading centres: the host institution (UniOvi), under the supervision of Pietro Vischia, an expert in differentiable optimisation and particle physics; a secondment at TU/e on differentiable uncertainty quantification; and a secondment at MAASTRO Clinic for clinical IMPT practice and validation.
In summary, PRODIGIO will deliver a significant impact on both the researcher’s career and on cancer care. On the one hand, it will provide extensive multidisciplinary training, placing the researcher in a leading position in the field. On the other, it will deliver the first uncertainty-aware, operator-independent auto-planning system for proton therapy, with the potential to transform radiotherapy treatment planning in Europe and worldwide.
Fields of science (EuroSciVoc)
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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(opens in new window) HORIZON-MSCA-2025-PF
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33003 OVIEDO
Spain
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