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Abstract #2128

Multiparametric MRI-based fusion radiomics for preoperatively predicting TERT promoter mutation status and survival in glioblastoma patients

Hongbo Zhang1, Hanwen Zhang2, Beibei Zhou3, Yuze Zhang1, Lei Wu1, Yi Lei2, and Biao Huang1
1Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China, 2Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People's Hospital, Shenzhen, China, 3Department of Radiology, Department of Radiology,The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China

Synopsis

Keywords: Tumors, RadiomicsRadiomics uses computer software to mine massive quantitative image features from medical imaging images and then screens the most valuable radiomics features using statistical and/or machine learning methods. Furthermore, it is used to parse clinical information for disease characterisation, tumour grading and staging, efficacy evaluation, and prognosis prediction. In our study, we demonstrated that multiparametric MRI-based fusion radiomics model is an effective preoperative non-invasive method to predict telomerase reverse transcriptase promoter mutations and progression-free survival in glioblastoma patients.

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