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Image-derived Prediction of Response to Chemo-radiation in Glioblastoma

Terminated
Conditions
Glioblastoma
Interventions
Radiation: Radiotherapy
Registration Number
NCT02329795
Lead Sponsor
Rigshospitalet, Denmark
Brief Summary

This study seeks to investigate if advanced image-analysis of diagnostic scans, can be used to predict how aggressive brain tumors (glioblastoma) respond to standard chemo- and radiation treatment.

Detailed Description

Generally, response prediction models seeks to predict time to an event, e.g. time-to-progression and/or overall survival. The aim of this study is to explore the feasibility of establishing an individualized response model, that, based on several morphologic, physiologic and metabolic parameters extracted from computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI), is able to predict the tumor response at the level of an imaging voxel, using machine learning techniques.

Imaging modalities include MRI, PET/CT with 18F-fluroethyltyrosine (18F-FET), and PET/MRI with 64Cu-diacetyl-bis(N4-methylthiosemicarbazone) (64Cu-ATSM).

Recruitment & Eligibility

Status
TERMINATED
Sex
All
Target Recruitment
16
Inclusion Criteria
  • Histologically confirmed, primary supratentorial glioblastoma (WHO grade IV).
Exclusion Criteria
  • No informed consent can be obtained
  • Inability to undergo MRI examination, due to metal implants, pacemaker etc.
  • Not eligible for Stupp-regimen

Study & Design

Study Type
OBSERVATIONAL
Study Design
Not specified
Arm && Interventions
GroupInterventionDescription
Standard chemoradiotherapyRadiotherapyGroup to receive 60 Gy radiotherapy in 30 fractions with concomitant and adjuvant temozolomide.
Standard chemoradiotherapyTemozolomideGroup to receive 60 Gy radiotherapy in 30 fractions with concomitant and adjuvant temozolomide.
Primary Outcome Measures
NameTimeMethod
Sensitivity and specificity of predicted response3 months post radiotherapy

Tumor response is measured as contrast-enhancing tumor on T1-weighted MRI and by metabolic active tumor using 18F-fluroethyl-tyrosine (FET)-PET. Pre-treatment risk map is constructed using machine learning methods and compared to post-treatment scans.

Secondary Outcome Measures
NameTimeMethod
DICE-similarity coefficient and percentage overlap of 64Cu-ATSM and contrast-enhanced T1-weighted MRI3 months post radiotherapy

Pre-chemoradiotherapy 64Cu-ATSM-PET is used as a surrogate marker for hypoxia and compared to treatment response, measured as contrast-enhancing tumor on T1-weighted MRI

Correlation (volume and maximum values) between lactate and hypoxia1 week before start of chemoradiotherapy

Lactate measured by MR spectroscopy is compared to metabolic uptake of 64Cu-ATSM-PET

Trial Locations

Locations (1)

Department of Oncology, Section for Radiotherapy, Rigshospitalet

🇩🇰

Copenhagen, Denmark

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