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 Jeong^1,2, Min-Hee Lee^2,3, Flora John^2,3, Sandeep Mittal^4,5, and Csaba Juhasz^1,2

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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