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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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