Abstract #0940

Creating a radiomic signature for H3K27M mutation in midline glioma on multimodal MRI

Manali Balasaheb Jadhav¹, Richa Singh Chauhan², Priyanka Tupe Waghmare³, Archit Rajan⁴, Abhilasha Indoria², Jitender Saini⁵, Vani Santosh⁶, Madhura Ingalhalikar⁴, and Subhas Konar⁷

¹Symbiosis Center for Medical Image Analysis, Pune, India, ²Radiology, National Institute of Mental Health and Neuroscieces, Bengaluru, India, ³Symbiosis Institute on Technology, Pune, India, ⁴Symbiosis Centre for Medical Image Analysis, Pune, India, ⁵Radiology, National Institute of Mental Health and Neuroscieces, Pune, India, ⁶Neuropathology, National Institute of Mental Health and Neuroscieces, Bengaluru, India, ⁷Neurosurgery, National Institute of Mental Health and Neuroscieces, Bengaluru, India

H3K27M mutation in diffuse midline glioma is an independent predictor of overall survival however has a very poor prognosis. Identification of the mutation using conventional radiological analysis is complicated while the deep location of the tumors in the brain makes biopsy challenging with substantial risk of morbidity. To alleviate these issues, our work employs radiomics based machine learning framework to predict the H3K27M mutation from multi-modal MRI on 46 subjects. Results revealed 91% cross validation accuracy illustrating its future potential in clinical use.

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