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Radiomic Analysis and Data Insights for Enhancing Hepatocellular Carcinoma Immunotherapy Outcomes

Project description

Enhancing immunotherapy outcome for hepatocellular cancer with radiomics

Hepatocellular carcinoma (HCC) remains a leading cause of cancer death, and responses to immunotherapy vary greatly among patients. Computed tomography (CT) is a common imaging exam and advances in computational analysis can now extract quantitative features (radiomics) from these scans to uncover for diagnosis and prognostication. With the support of the Marie Skłodowska-Curie Actions programme, RADIANT-HOPE aims to harness the power of radiomics to improve treatment outcomes in HCC undergoing immunotherapy. The primary objective is to develop and validate a radiomics-based model capable of non-invasive characterisation of tumour phenotype and treatment response prediction. By integrating radiomic data from pre-treatment CT with clinical and molecular information, it is possible to gain deeper insights into tumour biology and immune response patterns.

Objective

Hepatocellular carcinoma (HCC) ranks the sixth most common cancer and third leading cause of cancer-related death worldwide. Most patients are diagnosed at an advanced stage. For advanced HCC immunotherapy has been approved as the standard-of-care first-line treatment. However, only about one third of patients respond to immunotherapy. How to predict immunotherapy efficacy and early identify HCC patients who are likely to benefit from it is an urgent clinical problem. RADIANT-HOPE aims to address this problem by developing and externally validating a radiomics model based on clinical routine computed tomography (CT) images. To achieve this, a novel comprehensive framework will be proposed to automatically segment the tumor, expand the tumor margin, and extract radiomics features from intra- and peritumor regions at pretreatment triphasic CT images. These features will be unsupervised clustered into clinically meaningful sub-phenotypes, and patients are stratified into different risk groups of immunotherapy response. Multi-omic data (e.g. mRNA) will be profiled and compared between the sub-phenotypes. Radiogenomic analysis will be performed to evaluate their genomic foundation. The model will be validated in independent international cohorts and developed as software for facilitating clinical utility. This project adopts highly interdisciplinary and intersectoral approaches, integrating oncology, immunology, radiology, artificial intelligence, and bioinformatics. The proposed radiomics model has the potential to provide a non-invasive, accurate, wide accessible imaging biomarker for predicting immunotherapy efficacy and patient stratification. The model implementation in clinic will contribute to HCC personalized treatment, optimize healthcare resources allocation and significantly reduce healthcare costs. As a hepatobiliary surgeon and clinic-oriented researcher, my extensive experience in HCC and radiomics & AI makes me an ideal candidate to conduct this project.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

FUNDACIO DE RECERCA CLINIC BARCELONA-INSTITUT D INVESTIGACIONS BIOMEDIQUES AUGUST PI I SUNYER
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 209 914,56
Address
CARRER ROSSELLO 149
08036 BARCELONA
Spain

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Region
Este Cataluña Barcelona
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Research Organisations
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Total cost

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