Feasibility of Multimodal MRI-Based Deep Learning Prediction of High Amino Acid Uptake Regions and Survival in Patients With Glioblastoma - Jeong, Jeong-Won; Lee, Min-Hee; John, Flóra; Robinette, Natasha L.; Amit-Yousif, Alit J.; Barger, Geoffrey R.; Mittal, Sandeep; Juhász, Csaba (Frontiers in Neurology, 2019)
Purpose: Amino acid PET has shown high accuracy for the diagnosis and prognostication of malignant gliomas, however, this imaging modality is not widely available in clinical practice. This study explores a novel end-to-end deep learning framework (“U-Net”) for its feasibility to detect high amino acid uptake glioblastoma regions (i.e., metabolic tumor volume) using clinical multimodal MRI sequences. Methods: T2, fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient map, contrast-enhanced T1, and alpha-[11C]-methyl-L-tryptophan (AMT)-PET images were analyzed in 21 patients with newly-diagnosed glioblastoma. U-Net system with data augmentation was implemented to deeply learn nonlinear voxel-wise relationships between intensities of multimodal MRI as the input and metabolic tumor volume from AMT-PET as the output. The accuracy of the MRI- and PET-based volume measures to predict progression-free survival was tested. Results: In the augmented dataset using all four MRI modalities to investigate the upper limit of U-Net accuracy in the full study cohort, U-Net achieved high accuracy (sensitivity/specificity/positive predictive value/negative predictive value [NPV]: 0.85/1.00/0.81/1.00, respectively) to predict PET-defined tumor volumes. Exclusion of FLAIR from the MRI input set had a strong negative effect on sensitivity (0.60). In repeated hold out validation in randomly selected subjects, specificity and NPV remained high (1.00), but mean sensitivity (0.62) and PPV (0.68) were moderate. AMT-PET-learned MRI tumor volume from this U-net model within the contrast-enhancing volume predicted 6-month progression-free survival with 0.86/0.63 sensitivity/specificity. Conclusions: These data indicate the feasibility of PET-based deep learning for enhanced pretreatment glioblastoma delineation and prognostication by clinical multimodal MRI.