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​Multi-modal, Multi-site, Multi-omic, Multi-AGent AI framework for the Clinical management of ILC

CORDIS · observation · Publication date unknown

rperform for this subtype. M4GIC-ILC—a Multi-modal, Multi-site, Multi-omic, Multi-AGent AI framework for the clinical management of ILC—addresses these challenges by unifying representation learning, generative AI, and supervised prediction across pathology, radiology, molecular, and clinical data. Foundation-model backbones are fine-tuned for ILC to learn batch-aware, biologically grounded embeddings, while generative AI provides calibrated proxies for missing modalities (virtual IHC, virtual MGS, and imaging views), enabling clinicians to interpret cases in familiar diagnostic settings. On top, task-specific predictors estimate diagnosis (ILC vs other; classic vs non-classic), staging (T/N, focality, laterality), relapse risk, and treatment benefit (endocrine, CDK4/6i, chemotherapy), with calibrated uncertainty and abstention when confidence is low. The framework is orchestrated by a multi-agent system that integrates these tools into interactive, natural-language reports with visual explanations and full provenance. Development and validation occur through federated learning on a

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recordType

award

status

SIGNED

region

EU

value

3997804.25

unit

EUR