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Transformative Pediatric Brain Cancer Imaging using Integrated Biophysics-AI Mol...

Transformative Pediatric Brain Cancer Imaging using Integrated Biophysics-AI Molecular MRI Despite vast drug development efforts, brain tumors remain the leading cause of pediatric cancer deaths. Noninvasive monitoring of treatment response is crucial to reveal the mechanisms behind tumor-drug interactions and optimize... see more

31/03/2029

TAU

1M€

Project Budget: 1M€

Project leader

TEL AVIV UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-04-01

ERC-2023-STG: ERC STARTING GRANTS Scope:Objectives

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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Project duration: 59 months Date Start: 2024-04-01
End date: 2029-03-31

Project leader

TEL AVIV UNIVERSITY No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 1M€

participation deadline Sin fecha límite de participación.

Project description Despite vast drug development efforts, brain tumors remain the leading cause of pediatric cancer deaths. Noninvasive monitoring of treatment response is crucial to reveal the mechanisms behind tumor-drug interactions and optimize patient care. However, standard magnetic resonance imaging (MRI) methods involve injecting metals, have severe difficulties in differentiating treatment response from tumor progression, are qualitative, and mandate prolonged anesthesia due to the lengthy acquisition. I propose to develop a transformative molecular MRI technology, based on the chemical exchange saturation transfer (CEST) contrast mechanism that will enable specific, quantitative, rapid, contrast-material free, treatment monitoring of pediatric brain cancer. Recently I revealed that a combination of mathematical CEST models and AI can generate quantitative biomarker maps of pH and protein concentration changes across the brain, two known hallmarks of cancer. Inspired by these results, I now propose to adopt a previously unconsidered perspective and to represent the underlying physics of CEST MRI as a computational graph, enabling an automatic AI-based optimization of molecular imaging. I hypothesize that the combination of biophysical models with a new AI framework, and their synergetic integration throughout the entire imaging pipeline will provide accurate noninvasive treatment monitoring. First, I will establish a method for automated optimization of MRI protocols for early determination of the tumor response to mainstream chemotherapy. Next, I will shorten the 3D scan time by an order of magnitude and quantify the response to next generation immunotherapy. Third, I will translate the method to clinical scanners and validate it in a human pediatric pilot study. This research will yield a fundamental understanding of the molecular mechanisms underlying treatment response and establish an innovative precision medicine methodology that will transform pediatric cancer imaging.

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