We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue.
We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sørensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sørensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue. Abbreviations PN Plexiform neurofibroma NF1Neurofibromatosis type 1 MSNN Multispectral neural network IVIM Intravoxel incoherent motion Neurofibromatosis type 1 (NF1) is a common genetic neurocutaneous disease, affecting 1 in 3000 newborn infants 1 . There are over 1000 identified mutations of the NF1 gene which produces a tumor suppressor protein called neurofibromin 2 . A prototypical NF1 tumor is the plexiform neurofibroma (PN), a neurofibroma variant with tumor cells that spread along multiple nerve fascicles, resulting in an extensive mass of thickened nerve bundles in a proteinaceous matrix. These PNs affect 25-50% of NF1 patients and can occur anywhere there are nerve fibers leading to significant morbidity and mortality depending on the size and location adjacent to vital structures 3 . Approximately 10% of PNs can transform to become malignant peripheral nerve sheath tumors over the lifetime of the patient 3 . Currently, surgical resection is the only available therapy, but is infrequently curative due to extensive invasion, making complete resection difficult 3 . No effective medical therapy currently exists; however, clinical trials are underway to evaluate chemotherapy which shrink tumor or limit growth. Therefore, accurate measurements of tumor burden are necessary to assess for tumor response. Two dimensional measurements are limited in assessing treatment change because of the common irregularity and
This chapter discusses six rare tumors of the central nervous system in adults: medulloblastoma in adults, cerebellar liponeurocytoma, diffuse leptomeningeal glioneuronal tumor (DLGNT), ganglioglioma, pineal parenchymal tumors, and pleomorphic xanthoastrocytoma (PXA). The incidence, clinical characteristics, histology, radiographic details, treatment, and outcomes are outlined for each disease. Magnetic resonance imaging (MRI) characteristics are provided for each tumor type. Importantly, following the 2016 World Health Organization (WHO) classification of brain tumors, the molecular profile of each tumor type is provided. Current knowledge on molecular advancements within the study of each of these tumors is reviewed and is a key theme as new molecular discoveries are incorporated into evolving treatment approaches. Multidisciplinary management, often at centers with specialized neuro-oncology providers, is recommended for these rare tumors. References are provided for more detailed information.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.