Abstract #3005

The nomogram of MRI-based radiomics with complementary visual features by machine learning improves stratification of glioblastoma patients

ZHENYU SHU¹, YUYUN XU¹, and YONG ZHANG²

¹Zhejiang Provincial People’s Hospital, Hangzhou, China, ²MR Research, GE healthcare (China), SHANG HAI, China

This preliminary study explored the application of radiomics MRI in overall survival(OS) of glioblastoma patients.In addition, the independent predictors of OS was analyzed, and a prediction radiomics nomogram based on independent predictors was constructed. We found that EPI, age, and radiomic signature are independent predictors of OS for glioblastoma patients. The nomogram was created by integrating the three independent predictors, had the best performance when stratifying glioblastoma patients into long- versus short-term survival, which could help clinicians develop optimal treatment plans.

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