Project description
AI models of prostate cancer diagnosis
In Europe, prostate cancer (PC) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices lead to overdiagnosis and overtreatment, necessitating more effective tools for discriminating between aggressive and non-aggressive disease. The EU-funded ProCAncer-I project proposes to develop advanced artificial intelligence models to address unmet clinical needs: diagnosis, metastases detection and prediction of response to treatment. To achieve this, partners will generate a large interoperable repository of health images, and a scalable high performance computing platform hosting the largest collection of PC Magnetic Resonance Images used for developing robust PC AI models. To ensure the rapid clinical implementation of the models developed, the project's partners will robustly monitor performance, accuracy and reproducibility.
Objective
In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the third most lethal. Current clinical practices, often leading to overdiagnosis and overtreatment of indolent tumors, suffer from lack of precision calling for advanced AI models to go beyond SoA by deciphering non-intuitive, high-level medical image patterns and increase performance in discriminating indolent from aggressive disease, early predicting recurrence and detecting metastases or predicting effectiveness of therapies. To date efforts are fragmented, based on single–institution, size-limited and vendor-specific datasets while available PCa public datasets (e.g. US TCIA) are only few hundred cases making model generalizability impossible.
The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (>17,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and -neutral AI models for addressing 8 PCa clinical scenarios.
To accelerate clinical translation of PCa AI models, we focus on improving the trust of the solutions with respect to fairness, safety, explainability and reproducibility. Metrics to monitor model performance and a causal explainability functionality are developed to further increase clinical trust and inform on possible failures and errors. A roadmap for AI models certification is defined, interacting with regulatory authorities, thus contributing to a European regulatory roadmap for validating the effectiveness of AI-based models for clinical decision making.
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Funding Scheme
RIA - Research and Innovation actionCoordinator
70013 Irakleio
Greece
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Participants (20)
1400-038 Lisboa
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Participation ended
6525 XZ Nijmegen
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46026 Valencia
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56126 Pisa
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13273 Marseille
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06800 Cankaya Ankara
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17190 Girona
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4900 421 Viana Do Castelo
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08660 Vilnius
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11522 Athina
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SW3 6JJ London
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28010 Madrid
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10060 Candiolo To
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00185 Roma
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02114 Boston Ma
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W1H 7PE LONDON
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
17123 Athens
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
46021 Valencia
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
1010 Wien
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6525 GA Nijmegen
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