Projektbeschreibung
Molekulare Magnetresonanztomographie für die Bildgebung von Hirntumoren bei Kindern
Die Magnetresonanztomographie (MRT) stellt den Goldstandard für die Diagnose und Überwachung von Hirntumoren dar. Die Standard-MRT-Daten sind allerdings qualitativ, es fehlt ihnen an Präzision und an der Fähigkeit, das Ansprechen auf die Behandlung zu überwachen. Ziel des vom Europäischen Forschungsrat finanzierten Projekts BabyMagnet ist es, dieses Problem durch eine molekulare MRT-Technologie zu lösen, die eine schnelle Überwachung der pädiatrischen Hirntumorbehandlung ohne den Einsatz von Kontrastmitteln ermöglicht. Die Technologie stützt sich auf Veränderungen des pH-Werts und der Proteinkonzentration im Gehirn als Krebsbiomarker mithilfe von künstlicher Intelligenz (KI). Diese Forschung birgt das Potenzial, die Krebsbildgebung zu revolutionieren, indem sie die Optimierung von MRT-Protokollen automatisiert, Scans beschleunigt und Präzisionsmedizin für die pädiatrische Krebsbehandlung bietet.
Ziel
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.
Wissenschaftliches Gebiet
- medical and health sciencesbasic medicinepharmacology and pharmacydrug discovery
- medical and health sciencesclinical medicineoncology
- medical and health scienceshealth sciencespersonalized medicine
- engineering and technologymedical engineeringdiagnostic imagingmagnetic resonance imaging
- medical and health sciencesbasic medicineimmunologyimmunotherapy
Schlüsselbegriffe
Programm/Programme
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thema/Themen
Finanzierungsplan
HORIZON-ERC - HORIZON ERC GrantsGastgebende Einrichtung
69978 Tel Aviv
Israel