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Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge

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Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Objectives

Innovative physiologic MRI development focuses on depiction of heterogenous vascular and metabolic features in glioblastoma. For this feasibility study, we employed blood oxygenation level-dependent (BOLD) MRI with standardized and precise carbon dioxide (CO2) and oxygen (O2) modulation to investigate specific tumor tissue response patterns in patients with newly diagnosed glioblastoma.

Materials and methods

Seven newly diagnosed untreated patients with suspected glioblastoma were prospectively included to undergo a BOLD study with combined CO2 and O2 standardized protocol. %BOLD signal change/mmHg during hypercapnic, hypoxic, and hyperoxic stimulus was calculated in the whole brain, tumor lesion and segmented volumes of interest (VOI) [contrast-enhancing (CE) − tumor, necrosis and edema] to analyze their tissue response patterns.

Results

Quantification of BOLD signal change after gas challenges can be used to identify specific responses to standardized stimuli in glioblastoma patients. Integration of this approach with automatic VOI segmentation grants improved characterization of tumor subzones and edema. Magnitude of BOLD signal change during the 3 stimuli can be visualized at voxel precision through color-coded maps overlayed onto whole brain and identified VOIs.

Conclusions

Our preliminary investigation shows good feasibility of BOLD with standardized and precise CO2 and O2 modulation as an emerging physiologic imaging technique to detail specific glioblastoma characteristics. The unique tissue response patterns generated can be further investigated to better detail glioblastoma lesions and gauge treatment response.

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Funding

This project is funded by the Swiss Cancer League, KFS-3975-08-2016-R.

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Authors and Affiliations

  1. Department of Neurosurgery, University Hospital Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland

    Vittorio Stumpo, Martina Sebök, Christiaan Hendrik Bas van Niftrik, Luca Regli & Jorn Fierstra

  2. Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Vittorio Stumpo, Martina Sebök, Christiaan Hendrik Bas van Niftrik, Katharina Seystahl, Nicolin Hainc, Zsolt Kulcsar, Michael Weller, Luca Regli & Jorn Fierstra

  3. Department of Neurology, University Hospital Zurich, Zurich, Switzerland

    Katharina Seystahl & Michael Weller

  4. Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland

    Nicolin Hainc & Zsolt Kulcsar

Authors
  1. Vittorio Stumpo
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  2. Martina Sebök
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  3. Christiaan Hendrik Bas van Niftrik
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  4. Katharina Seystahl
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  5. Nicolin Hainc
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  6. Zsolt Kulcsar
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  7. Michael Weller
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  8. Luca Regli
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  9. Jorn Fierstra
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Contributions

Study conception and design (VS, MS, CHBN, JF), Acquisition of data (VS, MS, CHBN, JF), Analysis and interpretation of data (VS, CHBN, MS, JF), Drafting of manuscript (VS, JF), Critical revision (VS, MS, CHBN, KS, NH, ZK, MW, LR, JF).

Corresponding author

Correspondence to Vittorio Stumpo.

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Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Ethical standard

The study was approved by the cantonal ethics board of the Canton of Zurich, Switzerland (research protocol KEK-ZH-No. 2012–0427).

Informed consent

All participants signed informed consent prior to inclusion into the study.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 S1. Study Flowchart (JPG 62 KB)

10334_2021_980_MOESM2_ESM.tif

Supplementary file2 S2. Individual patient BOLD signal change during hypercapnic (A) and hypoxic and hyperoxic stimulus (B). (TIF 445 KB)

Supplementary file3 (TIF 408 KB)

10334_2021_980_MOESM4_ESM.jpg

Supplementary file4 S3. Binarized tumor map analysis workflow. A. Tumor masks are obtained and overlayed onto the hyperoxic, hypoxic and hypercapnic map. B. Only tumor voxels are then considered downstream for binarization analysis. C. For each of the 3 experimental condition (hyperoxia, hypoxia and hypercapnia) the tumor voxels with positive and negative BOLD signal change are identified. D. In positive and negative tumor voxels from each map, the mean %BOLD signal change/mmHg is calculated in the other two experimental conditions. Only the hyperoxic binarized map shows differences between positive versus negative voxels (red, left) (JPG 90 KB)

10334_2021_980_MOESM5_ESM.tif

Supplementary file5 S4. Software-based VOIs segmentation. Left, Input sequences. Right, Segmented masks output (A. Combined segmented masks; B. Edema; C. Contrast enhancing tumor; D. Necrosis) (TIF 316 KB)

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Stumpo, V., Sebök, M., van Niftrik, C.H.B. et al. Feasibility of glioblastoma tissue response mapping with physiologic BOLD imaging using precise oxygen and carbon dioxide challenge. Magn Reson Mater Phy 35, 29–44 (2022). https://doi.org/10.1007/s10334-021-00980-7

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  • DOI: https://doi.org/10.1007/s10334-021-00980-7

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