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Dynamic Susceptibility Contrast-MRI Quantification Software Tool: Development and Evaluation.

Panagiotis Korfiatis1, Timothy L Kline1, Zachary S Kelm1

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An automated tool for calculating relative cerebral blood volume (rCBV) in glioblastoma patients reduces variability. This method, including leakage correction and automated white matter selection, shows good agreement with FDA-approved software.

Keywords:
atlas segmentationdynamic susceptibility contrastglioblastomawhite matter

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Area of Science:

  • Neuroimaging
  • Oncology
  • Medical Physics

Background:

  • Relative cerebral blood volume (rCBV) is a key MRI biomarker for differentiating glioblastoma progression from pseudoprogression.
  • Current rCBV calculation methods are software-dependent and prone to user-induced variability, impacting clinical decisions.
  • Automation is crucial for consistent and accurate rCBV assessment in neuro-oncology.

Purpose of the Study:

  • To develop and validate an automated tool for calculating rCBV from dynamic susceptibility contrast-MRI.
  • To incorporate leakage correction and automated white matter selection into the rCBV calculation pipeline.
  • To compare the performance of the automated tool against FDA-approved software packages.

Main Methods:

  • An automated tool was developed for rCBV computation using dynamic susceptibility contrast-MRI.
  • Wavelet-based detection was employed for automatic calculation of bolus entrance and exit time points.
  • The automated tool was validated against three FDA-approved software packages (one automatic, two manual) using data from 43 patients.
  • Manual and automated white matter (WM) selection methods were evaluated for normalization.

Main Results:

  • The automated rCBV calculation tool demonstrated good agreement with two out of three FDA-approved software packages.
  • High intraclass correlation coefficients (>0.880) were observed between repeated measurements using the same software, except for one specific comparison.
  • Minimal variability in agreement was found between software tools when employing different WM selection techniques.

Conclusions:

  • The developed automated algorithm for rCBV calculation, including leakage correction and automated WM selection, provides reliable results.
  • This automated approach offers a consistent and potentially more accurate method for rCBV assessment in glioblastoma patients.
  • The tool's good agreement with established software supports its potential clinical utility in neuro-oncology decision-making.