Abstract #3496

A Comparative and Summative Study of Radiomics-Based Overall Survival Prediction in Glioblastoma Patients

Zhuoying Ruan¹, Nan Mei², Yiping Lu², Ji Xiong³, Xuanxuan Li², Yajing Zhao², Pu-Yeh Wu⁴, Li Liu⁵, and Bo Yin²

¹Shanghai Institute of Medical Imaging, Fudan University, Shanghai, China, ²Department of Radiology, Huashan Hospital Fudan University, Shanghai, China, ³Department of Pathology, Huashan Hospital Fudan University, Shanghai, China, ⁴GE Healthcare, MR Research China, Shanghai, China, ⁵Department of Radiology, Shanghai Cancer Center, Fudan University, Shanghai, China

The prediction of overall survival in glioblastoma patients can provide great aid to clinical treatment. Therefore, we extracted clinical, VASARI and radiomic features from pre-operative MRI scannings and compared the predictive capacity of Cox algorithm and random forest based on these features. According to our results, tumor without cortical involvement had longer overall survival than those involved; the random forest models outperformed Cox regression models in general and the random forest model consisting of radiomic features was the best one. Ten radiomic features were reproducible in our and other’s studies exhibiting promising value in overall survival prediction of glioblastoma.

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