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Predicting Regions of Local Recurrence in Glioblastomas Using Voxel-Based Radiomic Features of Multiparametric

Santiago Cepeda1, Luigi Tommaso Luppino2, Angel Pérez-Núñez3,4,5

  • 1Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain.

Cancers
|March 29, 2023
PubMed
Summary

This study developed a predictive model using MRI radiomics to identify glioblastoma recurrence areas. The model accurately predicts future tumor recurrence, potentially guiding treatment and improving patient survival.

Keywords:
MRIartificial intelligenceglioblastomamachine learningradiomicsrecurrence

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Area of Science:

  • Neuro-oncology
  • Radiology
  • Artificial Intelligence in Medicine

Background:

  • Glioblastoma surgery aims to remove enhancing tumors, but recurrence often stems from the peritumoral region.
  • Identifying these infiltration areas is crucial for improving patient outcomes and treatment strategies.

Purpose of the Study:

  • To develop and evaluate a predictive model for glioblastoma recurrence using voxel-based radiomics analysis of MRI data.
  • To identify specific peritumoral regions at high risk for future tumor recurrence.

Main Methods:

  • Retrospective analysis of 55 glioblastoma patients with complete enhancing tumor resection.
  • Voxel-based radiomics feature extraction from multiparametric MRI, coupled with machine learning classifiers (Categorical Boosting).
  • Deformable coregistration and segmentation to define peritumoral and enhancing tumor regions for recurrence prediction.

Main Results:

  • The Categorical Boosting classifier achieved an AUC of 0.81 ± 0.09 and accuracy of 0.84 ± 0.06 in predicting recurrence regions.
  • A strong visual correlation was observed between predicted and actual tumor recurrence areas.
  • The developed method accurately identifies regions of future glioblastoma recurrence on MRI scans.

Conclusions:

  • A novel MRI-based radiomics model can accurately predict glioblastoma recurrence hotspots.
  • This predictive capability may allow for tailored surgical and radiotherapy planning to target recurrence areas.
  • The findings suggest a potential to improve glioblastoma treatment and prolong patient survival.