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Translational Orthotopic Models of Glioblastoma Multiforme
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Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients.

Anna Sophia McKenney1,2, Emily Weg3, Tejus A Bale4,5

  • 1Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.

Advances in Radiation Oncology
|January 30, 2023
PubMed
Summary

Radiomic analysis can predict pseudoprogression in isocitrate dehydrogenase wild type glioblastoma patients. Adding O6-methylguanine-DNA methyltransferase status improved prediction accuracy, aiding prompt diagnosis and treatment decisions.

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Area of Science:

  • Neuro-oncology
  • Radiology
  • Artificial Intelligence

Background:

  • Pseudoprogression can mimic recurrent glioblastoma, complicating treatment decisions.
  • Accurate differentiation is crucial for timely intervention and clinical trial eligibility.

Purpose of the Study:

  • To develop a radiomic classifier for predicting pseudoprogression in isocitrate dehydrogenase wild type glioblastoma.
  • To assess the impact of O6-methylguanine-DNA methyltransferase status on classifier performance.

Main Methods:

  • Retrospective analysis of 74 glioblastoma patients with pre-operative MRI including dynamic contrast-enhanced T1 perfusion.
  • Development of a random forest classifier using recursive feature elimination and nested cross-validation.
  • Evaluation of classifier performance with and without O6-methylguanine-DNA methyltransferase status.

Main Results:

  • The radiomic classifier achieved an 81% area under the receiver operating curve for pseudoprogression prediction.
  • Inclusion of O6-methylguanine-DNA methyltransferase status improved prediction accuracy to 89%.
  • Radiomic features from contrast-enhanced T1-weighted and perfusion MRI were key predictors.

Conclusions:

  • Radiomic analysis of MRI perfusion and contrast-enhanced T1-weighted images aids in the prompt diagnosis of pseudoprogression.
  • The developed classifier shows potential for informing clinical treatment decisions.
  • External validation is required to confirm the generalizability of these findings.