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Imaging-Based Algorithm for the Local Grading of Glioma

Imaging-Based Algorithm for the Local Grading of Glioma ORIGINAL RESEARCH ADULT BRAIN E.D.H. Gates, J.S. Lin, J.S. Weinberg, S.S. Prabhu, J. Hamilton, J.D. Hazle, G.N. Fuller, V. Baladandayuthapani, D.T. Fuentes, and D. Schellingerhout ABSTRACT BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locat- ing the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic crite- rion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models. MATERIALS AND METHODS: Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic bi- opsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen k, precision, and recall. RESULTS: Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Neuroradiology American Journal of Neuroradiology

Imaging-Based Algorithm for the Local Grading of Glioma

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Publisher
American Journal of Neuroradiology
Copyright
© 2020 by American Journal of Neuroradiology
ISSN
0195-6108
eISSN
1936-959X
DOI
10.3174/ajnr.A6405
Publisher site
See Article on Publisher Site

Abstract

ORIGINAL RESEARCH ADULT BRAIN E.D.H. Gates, J.S. Lin, J.S. Weinberg, S.S. Prabhu, J. Hamilton, J.D. Hazle, G.N. Fuller, V. Baladandayuthapani, D.T. Fuentes, and D. Schellingerhout ABSTRACT BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locat- ing the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic crite- rion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models. MATERIALS AND METHODS: Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequences, followed by image-guided stereotactic bi- opsy before resection. An imaging description was developed for each biopsy, and multiclass machine learning models were built to predict the World Health Organization grade. Models were assessed on classification accuracy, Cohen k, precision, and recall. RESULTS: Twenty-three patients (with 7/9/7 grade II/III/IV gliomas) had analyzable imaging-pathologic pairs, yielding 52 biopsy

Journal

American Journal of Neuroradiology – American Journal of Neuroradiology

Published: Mar 1, 2020

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