Integrated AI Diagnostics in Breast Cancer

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.