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Low field MR guided response assessment during radiotherapy for glioblastoma patients for adaptive radiotherapy and response prediction

English title Low field MR guided response assessment during radiotherapy for glioblastoma patients for adaptive radiotherapy and response prediction
Applicant Andratschke Nicolaus
Number 200581
Funding scheme Project funding
Research institution Klinik für Radio-Onkologie UniversitätsSpital Zürich
Institution of higher education University of Zurich - ZH
Main discipline Clinical Cancer Research
Start/End 01.01.2022 - 31.12.2023
Approved amount 252'126.00
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Keywords (5)

reponse assessment; glioblastoma; radiotherapy; low field mri; radiomics

Lay Summary (German)

MR basierte Evaluation einer Strahlenbehandlung von bösartigen Hirntumore als Grundlage für eine adaptive Strahlentherapie und die Vorhersage des Ansprechend
Lay summary
Das Glioblastom st der häufigste Hirntumor im Erwachsenenalter und hat eine Prognose von in Mittel 15 Monate nach Diagnosenstellung. Aktuell besteht die Therapie der Wahl aus einer Operation gefolgt von einer kombinierte Radiochemotherapie und Weiterführung der Chemotherapie für sechs Monate im Anschluss daran. Eine große Herausforderung besteht in der posttherapeutischen Beurteilung des ansprechens, da im MRI nicht immer sicher zwischen einer Tumorprogression oder einer  strahlenbedingten Gewebereaktion unterschieden werden kann.
Dies kann erheblichen Einfluss auf das Therapiemanagement haben.
Ziel dieses Forschungsprojektes ist es, während einer Radiochemotherapie an einem MRI Hybrid Linearbeschleuniger mittels wiederholter MRI Bildgebung mit niedriger Feldstärke Gehirn-Veränderungen im Bereich der Tumorlokalisation zu detektieren und deren Einfluss auf das Bestrahlungvolumen und ebenso auf den weiteren Tumorverlauf zu untersuchen.
Neben konventionell in der klinische Routine angewandten MRI Sequenzen werden wir auch neuartige MRI Sequenzen, welche ohne intravenöses Kontrastmittel durchgeführt werden können, untersuchen und weiterentwickeln. 
Kombiniert mit Methoden der quantitativen Bildanalyse und Machine Learning Methoden sollen mit diesen Sequenzen neue Methoden zur Segmentierung, Zielvolumendefinition und longitudinalen Bildanalyse entwickelt werden, um eine Differenzierung Tumorprogression versus Strahlenreaktion und eine Vorhersage der Prognose zu ermöglichen.
Direct link to Lay Summary Last update: 21.10.2021

Responsible applicant and co-applicants

Project partner

Associated projects

Number Title Start Funding scheme
183568 GI-BCT - Clinical Grating Interferometry Breast Computed Tomography 01.01.2019 Sinergia
177080 MIG-ART - MRI gestützte adaptive Radio-Onkologie 01.06.2018 R'EQUIP


Background: Glioblastoma is the most common primary brain tumor in adults, and only has a median survival in the range of one year in population-based studies and between 15-16 months in clinical trial populations. After implementation of combined radio-chemotherapy strategies for glioblastoma, especially incorporating temozolomide after 2005, pseudo-progression mimicking tumor progression has been observed. The clinical question of pseudoprogression vs. progression can significantly impact clinical decision making with direct consequences in clinical practice. The idea of detection of early MRI changes during radio(chemo)therapy of glioblastoma as a potential surrogate imaging biomarker for subsequent discrimination of progression versus pseudoprogression is very appealing, but only few reports have been performed so far. Therefore, there is an urgent need to investigate and characterize imaging biomarkers and establish those for individual decision making in radiation oncology taking longitudinal changes as surrogate of response to local treatment into account. Hypothesis: Repetitive optimized low-field strength diffusion weighted MRI sequences acquired on a hybrid MR Linear accelerator with 0.35T before and during postoperative radio(chemo)therapy for newly diagnosed glioblastoma are able to detect early treatment-induced tumor/ brain changes and can be used for adaptive radiotherapy planning as well as for response and outcome prediction.Aims and Experimental design: The aim of this research project is to investigate whether repetitive low-field MRI investigations during radiation therapy allow for improved treatment monitoring and particularly provide a higher accuracy of the clinically relevant distinction between pseudoprogression and true tumor progression. We will implement a low-field strength MRI imaging workflow for glioblastoma patients at a dedicated hybrid MR linear accelerator and evaluate the value of repetitive low-field MR imaging by a novel MR Linac compatible head-coil to monitor tumor volume/ signal changes during the course of fractionated radiation treatment and correlate these to post-treatment outcome.Pre-treatment 0.35T MR images without intravenous contrast agents can be used for reliable target delineation and calculation of clinically applicable adaptive radiotherapy plans.Morphological T1/T2-weighted sequences, Diffusion-weighted Imaging (DWI) and Arterial Spin Labeling (ASL) at 0.35T allow for treatment monitoring during radiotherapy.Advanced DWI techniques such as Intravoxel Incoherent Motion (IVIM) analysis provide biomarkers for tumor response prediction and may be used for machine-learning based tissue segmentation as a basis for adaptive radiotherapy planning.The analysis of longitudinally acquired low field strength DWI sequences by machine-learning techniques can be used to discern progression from pseudo-progression.Expected value of the research project: Thus, with the proposed research project we envision an impact in two distinct areas:Repetitive non-contrast agent based morphological and functional low field MRI imaging with innovative post-processing methods as a diagnostic basis for the implementation of adaptive brain tumor radiotherapy.Application of these findings in the treatment management of glioblastoma patients to redefine target volume definition for individualized radiotherapy application and improve response assessment and outcome prediction.This approach will help us to characterize patients who will benefit most from local treatment intensification and spare patients from unnecessary intervention.