SOURCE-LINKED INTELLIGENCE
Infrared Nanoprobes and TissuE mimickinG tumoR shApe identificaTION
functions. INTEGRATION addresses this challenge by combining three cutting-edge approaches into a single preclinical platform: near-infrared-emitting nanoprobes, 3D-bioprinted brain-like models, and machine learning algorithms. By operating in the underexplored third biological transparency window (NIR-III, 1550-1850 nm), where tissue scattering is reduced and autofluorescence disappears, INTEGRATION will deliver sharper, higher-contrast images of tumor boundaries than currently possible. To validate this strategy, custom fluorescent nanoprobes will be engineered for stability, biocompatibility, and strong NIR-III emission. These will be tested in lifelike tissue phantoms created with 3D bioprinting, where healthy and tumoral regions are reproduced with tunable shape and optical properties. Fluorescence images collected from these models will train a convolutional neural network (U-Net) to precisely segment tumor margins in three dimensions. Finally, the approach will be validated ex vivo in mouse brain tissues, generating a proof-of-concept pipeline that is rigorous, ethical, and s
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 209914.56
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T04:21:15.460Z. This is not the publication date.