• Jianhua Qin, Yu Tang, and Bao Wang.
• School of Medicine, Qingdao University, Qingdao, P. R. China.
• Medicine (Baltimore). 2022 Jul 29; 101 (30): e29572e29572.

AbstractGenerated 18F-fluoromisonidazole (18F-FMISO) positron emission tomography (PET) images for glioblastoma are highly sought after because 18F-FMISO can be radioactive, and the imaging procedure is not easy. This study aimed to explore the feasibility of using advanced magnetic resonance (MR) images to generate regional 18F-FMISO PET images and its predictive value for survival. Twelve kinds of advanced MR images of 28 patients from The Cancer Imaging Archive were processed. Voxel-by-voxel correlation analysis between 18F-FMISO images and advanced MR images was performed to select the MR images for generating regional 18F-FMISO images. Neural network algorithms provided by the MATLAB toolbox were used to generate regional 18F-FMISO images. The mean square error (MSE) was used to evaluate the regression effect. The prognostic value of generated 18F-FMISO images was evaluated by the Mantel-Cox test. A total of 299 831 voxels were extracted from the segmented regions of all patients. Eleven kinds of advanced MR images were selected to generate 18F-FMISO images. The best neural network algorithm was Bayesian regularization. The MSEs of the training, validation, and testing groups were 2.92E-2, 2.9E-2, and 2.92E-2, respectively. Both the maximum Tissue/Blood ratio (P = .017) and hypoxic volume (P = .023) of the generated images were predictive factors of overall survival, but only hypoxic volume (P = .029) was a predictive factor of progression-free survival. Multiple advanced MR images are feasible to generate qualified regional 18F-FMISO PET images using neural networks. The generated images also have predictive value in the prognostic evaluation of glioblastoma.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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