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bioRxiv · 10.1101/2020.02.28.969493

New Radiological Classification of Glioma and validation with the survival analysis

Kamble, A. N.·Agrawal, N. K.·Koundal, S.·Bhargava, S.
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Abstract

Radiology based classification of glioma independent of histological or genetic markers predicting survival of patients is an unmet need. Until now radiology is chasing these markers rather than focussing directly on the clinical outcome. Our study is first of its kind to come up with the independent new radiological classification of gliomas encompassing both low-and high-grade gliomas under single classification system. TCGA-LGG and REMBRANDT public domain dataset of glioma were analyzed as training and testing dataset respectively. Based on MRI images, gliomas were classified into six types in detailed classification & three types in simplified classification system. Survival analysis using Kaplan Meier and Cox regression was done. Secondary objective was to evaluate the sensitivity and specificity of novel signs with existing histological and genetic markers. The study predicted survival in both training and testing dataset independent of genetic or histological information. Novel signs, "Ball on Christmas tree" sign(highly specific), Type-4 lineage sign(highly sensitive) identifies IDH-wild and high-grade gliomas (grade-III and IV) while Type-2 lineage sign showed good specificity in identifying 1p19q non co-deleted IDH-mutated, ATRX del/mutated, Grade-II gliomas. There is a substantial interobserver agreement for the classification and novel signs. New radiological classification of glioma predicts the survival of patients independent of genetic or histological information. This can act as a scaffolding to formulate and streamline the treatment guidelines for glioma patients. This classification has potential of improving the quality of care of glioma patients by predicting the survival without the need of invasive biopsy.

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Kamble, A. N., Agrawal, N. K., Koundal, S., Bhargava, S.. 2020-02-28. New Radiological Classification of Glioma and validation with the survival analysis. https://doi.org/10.1101/2020.02.28.969493

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