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

doi:10.1117/12.2549607

16 March 2020 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

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Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113142T (2020) https://doi.org/10.1117/12.2549607
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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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, and Dawid Schellingerhout "Advanced magnetic resonance imaging based algorithm for local grading of glioma", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113142T (16 March 2020); https://doi.org/10.1117/12.2549607

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KEYWORDS

Tumors

Biopsy

Diffusion

Magnetic resonance imaging

Tissues

Cancer

Image processing

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