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Abstract #3073

MRI-based deep learning prediction of high amino acid uptake region to improve survival prediction in patents with glioblastoma: A 3D U-net study with deeply learned inter-scanner multi-modal MRI and alpha-[11C]-methyl-L-tryptophan (AMT) PET

Jeong-Won Jeong1,2, Min-Hee Lee2,3, Flora John2,3, Sandeep Mittal4,5, and Csaba Juhasz1,2

1Pediatrics, Neurology, Translational Neuroscience Program, Wayne State University, Detroit, MI, United States, 2Translational Imaging Laboratory, PET center, Children's Hospital of Michigan, Detroit, MI, United States, 3Pediatrics and Neurology, Wayne State University, Detroit, MI, United States, 4Neurosurgery and Oncology, Wayne State University, Detroit, MI, United States, 5Karmanos Cancer Institute, Detroit, MI, United States

Previous studies found that high amino acid uptake measured by alpha-[11C]-methyl-L-tryptophan (AMT)-PET can accurately detect glioblastoma cell infiltration both in enhancing and non-enhancing tumor portions. However, AMT-PET is not widely available for clinical use. This study explores a novel U-Net which can accurately detect high tryptophan uptake glioblastoma regions using clinical multi-modal MRI data. The resulting U-Net led to 0.85±0.08 sensitivity and 0.99±0.00 specificity to predict AMT-PET tumor regions showing significant negative correlation with survival period, suggesting that an end-to-end deep learning of multi-modal MRI data may be effective for survival prediction of glioblastoma patient without the need of AMT-PET.

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