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Multiparametric tumor imaging and beyond Towards understanding in vivo signals

Non-invasive preclinical and clinical imaging is a powerful tool and has a huge potential, specifically in the realm of oncology. Recently, our laboratory developed a novel multimodality imaging system, which combines positron emi... ver más

31/05/2018

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

EBERHARD KARLS UNIVERSITAET TUEBINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2018-05-31 No tenemos la información de la convocatoria

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1 Participantes

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Líder del proyecto

EBERHARD KARLS UNIVERSITAET TUEBINGEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Non-invasive preclinical and clinical imaging is a powerful tool and has a huge potential, specifically in the realm of oncology. Recently, our laboratory developed a novel multimodality imaging system, which combines positron emission tomography (PET) and magnetic resonance imaging (MRI), yielding temporally and spatially matched data. However, the molecular PET and functional MRI signals are very complex and are often not fully understood. Thus, we will cross-validate the complementary PET/MRI information with proteomics and metabolomics data to gain a better understanding of the in vivo image data and yield finally an accurate holistic tumor profile. The cross-validation will be supported by image-guided accurately dissected tumor substructures. Tumor metabolism, receptor status, hypoxia, perfusion, apoptosis and angiogenesis will be investigated by established PET tracers. In the same imaging session, functional parameters of the tumor, such as perfusion, oxygenation and morphology will be assessed by MRI. Beyond this, novel imaging ligands for senescence, tumor stroma, and fatty acid synthase, which have been recently recognized as emerging key-players in tumor progression and therapy resistance, will be developed. The individual in vivo and in vitro parameters will be fed into a data mining utilizing a computer learning approach with regression and classification methods to detect common patterns and the related pharmacokinetics behind the in vivo imaging parameters. Analysis of the dynamic PET data will be performed by compartment analysis and kinetic modelling. Overall aim is to gain a better understanding of imaging data, provide an accurate holistic in vivo tumor profile to support prognostic parameters for tumor progression and therapy response. Finally, the revealed information will lead to a more accurate selection of imaging biomarkers for diagnosis and therapy control and will provide input for new strategies in tumor-specific tracer development.

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