18F-FET PET radiomics-based survival prediction in glioblastoma patients receiving radio(chemo)therapy

Radiation Oncology

Research

Katia Parodi¹  Marco Riboldi¹  Tun Wiltgen²  Nathalie Albert³  Adrien Holzgreve³  Peter Bartenstein³  Lena Kaiser³  Marcus Unterrainer³  Ilinca Popp⁴  Anca L. Grosu⁴  Guillaume Landry⁵  Claus Belka⁶  Stefanie Corradini⁶  Maximilian Niyazi⁶  Daniel F. Fleischmann⁷  Michael Ingrisch⁸ 

[1] Department of Medical Physics, LMU Munich, Garching, Germany;Department of Medical Physics, LMU Munich, Garching, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;Department of Neurology, School of Medicine, Technical University of Munich, Munich, Germany;Department of Nuclear Medicine, University Hospital, LMU Munich, Munich, Germany;Department of Radiation Oncology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;German Cancer Consortium (DKTK), Partner Site, Munich, Germany;Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany;German Cancer Consortium (DKTK), Partner Site, Munich, Germany;German Cancer Research Center (DKFZ), Heidelberg, Germany;Department of Radiology, University Hospital, LMU Munich, Munich, Germany;

关键词: Quantitative image analysis;    Radiomics;    Survival analysis;    Glioblastoma;    Radiotherapy;   

DOI  :  10.1186/s13014-022-02164-6

 received in 2022-04-09, accepted in 2022-10-07,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundQuantitative image analysis based on radiomic feature extraction is an emerging field for survival prediction in oncological patients. 18F-Fluorethyltyrosine positron emission tomography (18F-FET PET) provides important diagnostic and grading information for brain tumors, but data on its use in survival prediction is scarce. In this study, we aim at investigating survival prediction based on multiple radiomic features in glioblastoma patients undergoing radio(chemo)therapy.MethodsA dataset of 37 patients with glioblastoma (WHO grade 4) receiving radio(chemo)therapy was analyzed. Radiomic features were extracted from pre-treatment 18F-FET PET images, following intensity rebinning with a fixed bin width. Principal component analysis (PCA) was applied for variable selection, aiming at the identification of the most relevant features in survival prediction. Random forest classification and prediction algorithms were optimized on an initial set of 25 patients. Testing of the implemented algorithms was carried out in different scenarios, which included additional 12 patients whose images were acquired with a different scanner to check the reproducibility in prediction results.ResultsFirst order intensity variations and shape features were predominant in the selection of most important radiomic signatures for survival prediction in the available dataset. The major axis length of the 18F-FET-PET volume at tumor to background ratio (TBR) 1.4 and 1.6 correlated significantly with reduced probability of survival. Additional radiomic features were identified as potential survival predictors in the PTV region, showing 76% accuracy in independent testing for both classification and regression.Conclusions18F-FET PET prior to radiation provides relevant information for survival prediction in glioblastoma patients. Based on our preliminary analysis, radiomic features in the PTV can be considered a robust dataset for survival prediction.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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