Descripción del proyecto
Resonancia magnética molecular para imagenología de tumores cerebrales infantiles
La imagen por resonancia magnética (IRM) es la modalidad de referencia para el diagnóstico y seguimiento de los tumores cerebrales. Sin embargo, los datos de la IRM estándar son cualitativos, carecen de precisión y de la capacidad de supervisar las respuestas al tratamiento. El equipo del proyecto BabyMagnet, financiado por el Consejo Europeo de Investigación, pretende resolver este asunto mediante una tecnología de IRM molecular que permite monitorizar rápidamente el tratamiento del cáncer cerebral pediátrico sin necesidad de agentes de contraste. La tecnología se basa en los cambios del pH y la concentración de las proteínas en el encéfalo como los biomarcadores del cáncer con ayuda de la IA. Esta investigación puede revolucionar el diagnóstico por imagen del cáncer al automatizar la optimización de los protocolos de la IRM, acelerar las exploraciones y ofrecer medicina de precisión para el tratamiento del cáncer pediátrico.
Objetivo
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.
Ámbito científico
- 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
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
69978 Tel Aviv
Israel