Abstract #0897

Radiogenomics of 154 WHO grade 2 and 3 gliomas via machine learning and the impact of texture analysis

Manabu Kinoshita^1,2, Hideyuki Arita², Atsushi Kawaguchi³, Masamichi Takahashi⁴, Yoshitaka Narita⁴, Yuzo Terakawa², Naohiro Tsuyuguchi², Yoshiko Okita², Masahiro Nonaka², Shusuke Moriuchi², Junya Fukai², Shuichi Izumoto², Kenichi Ishibashi², Yoshinori Kodama², Kanji Mori², Koichi Ichimura⁵, and Yonehiro Kanemura^2,6

¹Osaka International Cancer Institute, Osaka, Japan, ²Kansai Molecular Diagnosis Network for CNS Tumors, Osaka, Japan, ³Saga University, Saga, Japan, ⁴National Cancer Center Hospital, Tokyo, Japan, ⁵National Cancer Center Research Institute, Tokyo, Japan, ⁶Osaka National Hospital, Osaka, Japan

In this research, the authors performed radiomics for 154 LrGG and attempted to build a MRI based predictive model to classify clinically relevant 3 LrGG subgroups using machine learning algorithm. The impact of texture analysis such as GLCM and GLRLM on building the model was also investigated. Accuracy for predicting 3 molecular subgroups were 0.587 without and 0.546 with texture analysis. Although radiomics was shown to be a powerful tool to identify genetic subgroups of LrGG, little improvement is expected from texture analysis.

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