Advanced magnetic resonance imaging based algorithm for local grading of glioma

Evan D.H. Gates, Jonathan S. Lin, Jeffrey S. Weinberg, Sujit S. Prabhu, Jackson Hamilton, John D. Hazle, Gregory N. Fuller, Veera Baladandayuthapani, David T. Fuentes, Dawid Schellingerhout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The purpose of this work is to determine the strength of correlations between imaging data and local tumor grade using spatially specific tumor samples to validate against a histologic gold-standard. This improves our understanding of diagnostic imaging by correlating with underlying biology. Glioma patients were enrolled in an IRB approved prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic (T1, T2, FLAIR, T1 post-contrast, and susceptibility), diffusion tensor, dynamic susceptibility and dynamic contrast sequences. During surgery stereotactic biopsy were collected prior to resection along with image space coordinates of the samples. A random forest were built to predict the grade of each sample using preoperative imaging data. The model was assessed based on classification accuracy, Cohen's kappa, and sensitivity to higher grade disease Twenty-three patients with fifty-two total biopsy samples were analyzed. The Random Forest method predicted tumor grade at 94% accuracy using four inputs (T2, ADC, CBV and Ktrans). Using conventional imaging only, the overall accuracy decreased (89% overall, κ = 0.78) and 71% of high grade samples were misclassified as lower grade disease. We found that pathologic features can be predicted to high accuracy using clinical imaging data. Advanced imaging data contributed significantly to this accuracy, adding value over accuracies obtained using conventional imaging only. Confirmatory imaging trials are justified.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityHouston
Period2/16/202/19/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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