Share Email Print
cover

Proceedings Paper

Advanced magnetic resonance imaging based algorithm for local grading of glioma
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper 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.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142T (16 March 2020); doi: 10.1117/12.2549607
Show Author Affiliations
Evan D. H. Gates, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Jonathan S. Lin, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Baylor College of Medicine (United States)
Rice Univ. (United States)
Jeffrey S. Weinberg, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Sujit S. Prabhu, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Jackson Hamilton, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Radiology Partners, Inc. (United States)
John D. Hazle, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Gregory N. Fuller, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Veera Baladandayuthapani, Univ. of Michigan School of Public Health (United States)
David T. Fuentes, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)
Dawid Schellingerhout, The Univ. of Texas M.D. Anderson Cancer Ctr. (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

© SPIE. Terms of Use
Back to Top
PREMIUM CONTENT
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?
close_icon_gray