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

Abstract

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.

Deep Learning-Based Studies on Pediatric Brain Tumors Imaging: Narrative Review of Techniques and Challenges

“…The authors used the innovative diffusion histology imaging (DHI) technique in another recent study [52] which incorporates deep neural networks and diffusion base spectrum imaging (DBSI). DHI is able to classify, differentiate, and measure heterogeneous regions of pediatric high-grade brain tumors.…”

The impact of edema and fiber crossing on diffusion MRI metrics assessed in an ex vivo nerve phantom: Multi‐tensor model vs. diffusion orientation distribution function

“…22 DBSI enables tracking of pathologies such as multiple sclerosis, 23,[25][26][27] cervical spondylotic myelopathy, 28 traumatic spinal cord injury, 29,30 epilepsy 31 and brain tumor. 32,33 Importantly, DBSI simultaneously resolves the angle of crossing fibers and quantifies individual fiber diffusivity, a feature that neither DTI, DKI, QBI nor NODDI possesses.…”

“…DBSI differs from DTI and other techniques in that it models diffusion‐weighted MRI signals as a linear combination of discrete anisotropic diffusion tensors and a spectrum of isotropic diffusion tensors (allowing the discrimination between inflammation, cellularity and CSF) 22 . DBSI enables tracking of pathologies such as multiple sclerosis, 23,25–27 cervical spondylotic myelopathy, 28 traumatic spinal cord injury, 29,30 epilepsy 31 and brain tumor 32,33 . Importantly, DBSI simultaneously resolves the angle of crossing fibers and quantifies individual fiber diffusivity, a feature that neither DTI, DKI, QBI nor NODDI possesses.…”