Abstract
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 non-linear 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 [PPV]/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.
Highlights
Glioblastomas are the deadliest primary brain tumors, and their initial treatment, based on clinical MRI, can miss tumor portions infiltrating to adjacent brain regions
AMT-positron emission tomography (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. These data indicate the feasibility of PET-based deep learning for enhanced pretreatment glioblastoma delineation and prognostication by clinical multimodal MRI
This study explores a novel end-to-end deep learning network to test its ability to detect high tryptophan uptake glioblastoma regions using clinical multimodal MRI
Summary
Glioblastomas are the deadliest primary brain tumors, and their initial treatment (surgery followed by radiation), based on clinical MRI, can miss tumor portions infiltrating to adjacent brain regions. Accurate non-invasive imaging of tumor-infiltrating brain is critical to optimize surgical resection and subsequent radiation therapy and prolong survival [1, 2]. Current clinical MRI, including T1-weighted images with gadolinium (T1-Gad), T2, and fluid-attenuated inversion recovery (FLAIR), has limited accuracy to detect such infiltrating regions and predict survival, since they cannot accurately differentiate regions with active tumor from vasogenic edema and necrosis [3]. Our previous studies reported that high amino acid uptake measured by alpha-[11C]-methyl-L-tryptophan (AMT)-PET can accurately detect both enhancing and non-enhancing gliomas [10,11,12,13]. Increased AMT uptake often extends beyond the contrastenhancing tumor to identify glioma-infiltrated brain [10], which is commonly underestimated based on clinical MRI. 18F-labeled amino acid PET tracers are more widely available, their use is still confined to a limited number of centers worldwide
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