project . 2020 - 2025 . On going

CANCER-RADIOMICS

Deep Learning for Automated Quantification of Radiographic Tumor Phenotypes
Open Access mandate for Publications European Commission
  • Funder: European CommissionProject code: 866504 Call for proposal: ERC-2019-COG
  • Funded under: H2020 | ERC | ERC-COG Overall Budget: 2,000,000 EURFunder Contribution: 2,000,000 EUR
  • Status: On going
  • Start Date
    01 Aug 2020
    End Date
    31 Jul 2025
  • Detailed project information (CORDIS)
  • Open Access mandate
    Research Data: No
Description
Artificial Intelligence (AI), deep-learning in particular, is propelling the field of radiology forward at a rapid pace. In oncology, AI can characterize the radiomic phenotype of the entire tumor and provide a non-invasive window into the internal growth patterns of a cancer lesion. This is especially important for patients treated with immunotherapy as, despite the remarkable success of these novel therapies, the clinical benefit remains limited to a subset. As immunotherapy is expensive and could bring unnecessary toxicity there is a direct need to identify beneficial patients, but this remains difficult in clinical practice today. Radiomic biomarkers could a...
Partners
Description
Artificial Intelligence (AI), deep-learning in particular, is propelling the field of radiology forward at a rapid pace. In oncology, AI can characterize the radiomic phenotype of the entire tumor and provide a non-invasive window into the internal growth patterns of a cancer lesion. This is especially important for patients treated with immunotherapy as, despite the remarkable success of these novel therapies, the clinical benefit remains limited to a subset. As immunotherapy is expensive and could bring unnecessary toxicity there is a direct need to identify beneficial patients, but this remains difficult in clinical practice today. Radiomic biomarkers could a...
Partners
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