Project description
Artificial Intelligence for Breast cancer diagnosis
Breast cancer poses a significant threat to many EU citizens. Despite European efforts to mitigate this threat through early and accurate diagnosis, the nascent stage of most precision medicine means that patients often lack precise treatment following neoadjuvant chemotherapy (NAC). With the support of the Marie Skłodowska-Curie Actions programme, the GRANITE project aims to leverage the potential of AI for digital diagnostics and enhance its capabilities. To achieve this, the project will address current challenges faced by AI technology in diagnosis. It will equip AI with state-of-the-art image analysis, healthcare data, and pre-train multiple deep learning (DL) models. Subsequently, the project will deploy these pre-trained DL models to demonstrate, fine-tune, and validate this solution. Through these efforts, GRANITE seeks to advance the field of digital diagnostics for breast cancer and improve patient outcomes.
Objective
In EU-27, it is estimated that >355,000 were diagnosed with breast cancer (BC) in 2020. Initiatives to reduce this burden in Europe involve early and precise diagnosis in the standard-of-care management to decrease unnecessary or insufficient treatment. Notably, precision medicine in BC is still in its infancy and is becoming even more critical with neoadjuvant chemotherapy (NAC), a standard of care treatment protocol in HER2-positive and triple negative subtypes. Recently, the use of digital diagnostics with AI is gaining momentum since it shows great promise towards accelerating personalized BC patients’ pre- and post-NAC predictions. While AI paves the way to next generation diagnostics, this has yet from been translated as a) it mostly operates in single data modalities that fail to capture the complex disease alterations, b) integrated AI usually suffers from data incompleteness usually leading to models trained with limited data that fail to generalize to new patients and/or that are not able to integrate partially observed multimodal information from the whole population. GRANITE focuses to address these unmet needs and goes beyond the state-of-the-art, fusing the most relevant standard of care data (radiology, pathology, clinical, demographic), leveraging novel AI and radiomics algorithms. GRANITE will deploy pre-trained deep learning models that will be fine-tuned, technically validated and clinically evaluated against pertinent clinical data of non-metastatic BC cases from the Bank of Cyprus Oncology Centre, Cyprus. We will engage with the AI4HI project to transcend FUTURE-AI guidelines (Fairness, Universality, Traceability, Usability, Robustness and Explainability; future-ai.eu) into GRANITE towards generating real-world evidence and making our AI technology clinically sound, ethically aware and technically applicable, and promoting AI trust and acceptance in BC management.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- medical and health sciencesclinical medicineradiology
- medical and health sciencesclinical medicineoncology
- medical and health sciencesbasic medicinepathology
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Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
1678 Nicosia
Cyprus