Current Clinical Brain Tumor Imaging

Villanueva-Meyer, Javier; Mabray, Marc C.; Cha, Soonmee

doi:10.1093/neuros/nyx103

Cited by 320 publications

(263 citation statements)

References 166 publications

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“…While MRI remains the most common clinical imaging technique for evaluating CNS tumors (6), conventional MRI sequences such as T1WI and T2WI correlated poorly with tumor pathology of high-grade brain tumors because of the complicated histologic heterogeneity. For example, hyperintense regions in T2W and FLAIR images surrounding the enhancing tumor core cannot distinguish between infiltrative tumor, vasogenic edema, or immune cell (24). Gd enhancement in T1WI also could occur due to either tumor progression or radiation necrosis (25).…”

Section: Discussionmentioning

confidence: 99%

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Ye¹,

Srinivasa²,

Lin³

et al. 2020

Preprint

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High-grade pediatric brain tumors constitute the highest mortality of cancer-death in children.While conventional MRI has been widely adopted for examining pediatric high-grade brain tumor clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in the clinical management of pediatric brain tumor. We employed a novel Diffusion Histology Imaging (DHI) approach that incorporates diffusion basis spectrum imaging (DBSI) and deep neural network. DHI aims to detect, differentiate, and quantify heterogenous areas in pediatric highgrade brain tumors, which include normal white matter (WM), densely cellular tumor (DC tumor), less densely cellular tumor (LDC tumor), infiltrating edge, necrosis, and hemorrhage. Distinct diffusion metric combination would thus indicate the unique distributions of each distinct tumor histology features. DHI, by incorporating DBSI metrics and the deep neural network algorithm, classified pediatric tumor histology with an overall accuracy of 83.3%. Receiver operating analysis (ROC) analysis suggested DHI's great capability in distinguishing individual tumor histology with AUC values (95%CI) of 0.983 (0.985-0.989), 0.961 (0.957-0.964), 0.993 (0.992-0.994), 0.953 (0.947-0.958), 0.974 (0.970-0.978) and 0.980 (0.977-0.983) for normal WM, DC tumor, LDCtumor, infiltrating edge, necrosis and hemorrhage, respectively. Our results suggest that DBSI-DNN, or DHI, accurately characterized and classified multiple tumor histologic features in pediatric high-grade brain tumors. If further validated in patients, the novel DHI might emerge as a favorable alternative to the current neuroimaging techniques to better guide biopsy and resection as well as monitor therapeutic response in patients with high-grade brain tumors.

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Section: Discussionmentioning

confidence: 99%

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Ye¹,

Srinivasa²,

Lin³

et al. 2020

Preprint

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“…This is because MRI provides exquisite anatomical images and functional information with high spatial resolution in a noninvasive manner yet without harmful radiation [19,20]. MRI is therefore very important for diagnosis and preoperative imaging of brain cancer.…”

Section: Magnetic Resonance Imaging and Contrast Agentsmentioning

confidence: 99%

Dendrimer-based magnetic resonance imaging agents for brain cancer

et al. 2018

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Brain cancer is one of the most lethal and difficult-to-treat cancers because of its physical location and biological barriers. The mainstay of brain cancer treatment is surgical resection, which demands precise imaging for tumor localization and delineation. Thanks to advances in bioimaging, brain cancer can be detected earlier and resected more reliably. Magnetic resonance imaging (MRI) is the most common and preferred method to delineate brain cancer, and a contrast agent is often required to enhance imaging contrast. Dendrimers, a special family of synthetic macromolecules, constitute a particularly appealing platform for constructing MRI contrast agents by virtue of their well-defined three-dimensional structure, tunable nanosize and abundant surface terminals, which allow the accommodation of high payloads and numerous functionalities. Tuning the dendrimer size, branching and surface composition in conjunction with conjugation of MRI functionalities and targeting moieties can alter the relaxivity for MRI, overcome the blood-brain barrier and enhance tumor-specific targeting, hence improving the imaging quality and safety profile for precise and accurate imaging of brain tumors. This short review highlights the recent progress, opportunities and challenges in developing dendrimer-based MRI contrast agents for brain tumor imaging.

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“…In normal brain, DT‐MRI‐based FT can visualize the character of white matter and the complex network of nerve fibers connecting different brain areas . In diseased brain, DT‐MRI‐based tractography has been used to demonstrate different abnormalities in tumor, aging, malformations, Alzheimer's disease, multiple sclerosis, stroke, close head injury, alcoholism, and HIV infection . DT‐MRI has also been used to assess pathologies of the spinal cord and peripheral nervous system…”

Section: Novel Bioengineering and Clinical Tools For Studying The Egjmentioning

confidence: 99%

The esophagiome: integrated anatomical, mechanical, and physiological analysis of the esophago‐gastric segment

Zhao

McMahon

Fox

et al. 2018

Annals of the New York Academy of Sciences

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Esophageal diseases are highly prevalent and carry significant socioeconomic burden. Despite the apparently simple function of the esophagus, we still struggle to better understand its physiology and pathophysiology. The assessment of large data sets and application of multiscale mathematical organ models have gained attention as part of the Physiome Project. This has long been recognized in cardiology but has only recently gained attention for the gastrointestinal(GI) tract. The term "esophagiome" implies a holistic assessment of esophageal function, from cellular and muscle physiology to the mechanical responses that transport and mix fluid contents. These anatomical, mechanical, and physiological models underlie the development of a "virtual esophagus" modeling framework to characterize and analyze function and disease. Functional models incorporate anatomical details with sensory-motor responses, especially related to biomechanical functions such as bolus transport. Our review builds on previous reviews and focuses on assessment of detailed anatomical and geometric data using advanced imaging technology for evaluation of gastro-esophageal reflux disease (GERD), and on esophageal mechanophysiology assessed using technologies that distend the esophagus. Integration of mechanics- and physiology-based analysis is a useful characteristic of the esophagiome. Experimental data on pressures and geometric characteristics are useful for the validation of mathematical and computer models of the esophagus that may provide predictions of novel endoscopic, surgical, and pharmaceutical treatment options.

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Current Clinical Brain Tumor Imaging

Cited by 320 publications

References 166 publications

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Diffusion Basis Spectrum Imaging with Deep Neural Network Differentiates Distinct Histology in Pediatric Brain Tumors

Dendrimer-based magnetic resonance imaging agents for brain cancer

The esophagiome: integrated anatomical, mechanical, and physiological analysis of the esophago‐gastric segment

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