Abstract #3071

Grading of glioma using a machine learning framework based on optimized features obtained from quantitative DCE-MRI and SWI

Banasmita Kar¹, Anirban Sengupta¹, Rupsa Bhattacharjee^1,2, Neha Vats¹, Virendra Yadav¹, Dinil Sasi¹, Rakesh Kumar Gupta³, and Anup Singh^1,4

¹Center for Biomedical Engineering, Indian Institute of Technology, Delhi, NEW DELHI, India, ²Philips Health Systems, Philips India Limited, Gurugram, India, ³Department of Radiology, Fortis Memorial Research Institute, Gurugram, India, ⁴Department of Biomedical Engineering, All India Institute of Medical Science, New Delhi, India, New Delhi, India

Potential of quantitative dynamic-contrast-enhanced(DCE) MRI parameters in glioma is well reported. However, in some glioma cases, biological behavior of tumors overlap between grades, therefore, correct grading becomes necessary for true classification and further treatment planning. In such cases histopathological glioma grading doesn’t necessarily correlate with DCE-MRI parameters based grading. Objective of this study is to improve the accuracy of grading of glioma using multi-parametric analysis i.e. combining Intra-Tumoral-Susceptibility-Signal(ITSS) volume generated from SWI with DCE-MRI parameters. Using a supervised-machine-learning based approach glioma grading can be improved particularly for the cases where DCE-MRI parameters underperform or vice-versa.

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