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Diffusion histology imaging differentiates distinct pediatric brain tumor histology

High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, sur...

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Autores principales: Ye, Zezhong, Srinivasa, Komal, Meyer, Ashely, Sun, Peng, Lin, Joshua, Viox, Jeffrey D., Song, Chunyu, Wu, Anthony T., Song, Sheng-Kwei, Dahiya, Sonika, Rubin, Joshua B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910493/
https://www.ncbi.nlm.nih.gov/pubmed/33637807
http://dx.doi.org/10.1038/s41598-021-84252-3
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author Ye, Zezhong
Srinivasa, Komal
Meyer, Ashely
Sun, Peng
Lin, Joshua
Viox, Jeffrey D.
Song, Chunyu
Wu, Anthony T.
Song, Sheng-Kwei
Dahiya, Sonika
Rubin, Joshua B.
author_facet Ye, Zezhong
Srinivasa, Komal
Meyer, Ashely
Sun, Peng
Lin, Joshua
Viox, Jeffrey D.
Song, Chunyu
Wu, Anthony T.
Song, Sheng-Kwei
Dahiya, Sonika
Rubin, Joshua B.
author_sort Ye, Zezhong
collection PubMed
description High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular 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 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, 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 these results could be 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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spelling pubmed-79104932021-03-02 Diffusion histology imaging differentiates distinct pediatric brain tumor histology Ye, Zezhong Srinivasa, Komal Meyer, Ashely Sun, Peng Lin, Joshua Viox, Jeffrey D. Song, Chunyu Wu, Anthony T. Song, Sheng-Kwei Dahiya, Sonika Rubin, Joshua B. Sci Rep Article High-grade pediatric brain tumors exhibit the highest cancer mortality rates in children. While conventional MRI has been widely adopted for examining pediatric high-grade brain tumors clinically, accurate neuroimaging detection and differentiation of tumor histopathology for improved diagnosis, surgical planning, and treatment evaluation, remains an unmet need in their clinical management. We employed a novel Diffusion Histology Imaging (DHI) approach employing diffusion basis spectrum imaging (DBSI) derived metrics as the input classifiers for deep neural network analysis. DHI aims to detect, differentiate, and quantify heterogeneous areas in pediatric high-grade brain tumors, which include normal white matter (WM), densely cellular tumor, less densely cellular 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 85.8%. Receiver operating analysis (ROC) analysis suggested DHI’s great capability in distinguishing individual tumor histology with AUC values (95% CI) of 0.984 (0.982–0.986), 0.960 (0.956–0.963), 0.991 (0.990–0.993), 0.950 (0.944–0.956), 0.977 (0.973–0.981) and 0.976 (0.972–0.979) for normal WM, densely cellular tumor, less densely cellular tumor, 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 these results could be 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. Nature Publishing Group UK 2021-02-26 /pmc/articles/PMC7910493/ /pubmed/33637807 http://dx.doi.org/10.1038/s41598-021-84252-3 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ye, Zezhong
Srinivasa, Komal
Meyer, Ashely
Sun, Peng
Lin, Joshua
Viox, Jeffrey D.
Song, Chunyu
Wu, Anthony T.
Song, Sheng-Kwei
Dahiya, Sonika
Rubin, Joshua B.
Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_full Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_fullStr Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_full_unstemmed Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_short Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_sort diffusion histology imaging differentiates distinct pediatric brain tumor histology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910493/
https://www.ncbi.nlm.nih.gov/pubmed/33637807
http://dx.doi.org/10.1038/s41598-021-84252-3
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