Diffusion histology imaging differentiates distinct pediatric brain tumor histology

Abstract 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 diagn...

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Autores principales:	Zezhong Ye, Komal Srinivasa, Ashely Meyer, Peng Sun, Joshua Lin, Jeffrey D. Viox, Chunyu Song, Anthony T. Wu, Sheng-Kwei Song, Sonika Dahiya, Joshua B. Rubin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/268ca202f1384572b80a32203fadf34f
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spelling	oai:doaj.org-article:268ca202f1384572b80a32203fadf34f2021-12-02T15:53:03ZDiffusion histology imaging differentiates distinct pediatric brain tumor histology10.1038/s41598-021-84252-32045-2322https://doaj.org/article/268ca202f1384572b80a32203fadf34f2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84252-3https://doaj.org/toc/2045-2322Abstract 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.Zezhong YeKomal SrinivasaAshely MeyerPeng SunJoshua LinJeffrey D. VioxChunyu SongAnthony T. WuSheng-Kwei SongSonika DahiyaJoshua B. RubinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zezhong Ye Komal Srinivasa Ashely Meyer Peng Sun Joshua Lin Jeffrey D. Viox Chunyu Song Anthony T. Wu Sheng-Kwei Song Sonika Dahiya Joshua B. Rubin Diffusion histology imaging differentiates distinct pediatric brain tumor histology
description	Abstract 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.
format	article
author	Zezhong Ye Komal Srinivasa Ashely Meyer Peng Sun Joshua Lin Jeffrey D. Viox Chunyu Song Anthony T. Wu Sheng-Kwei Song Sonika Dahiya Joshua B. Rubin
author_facet	Zezhong Ye Komal Srinivasa Ashely Meyer Peng Sun Joshua Lin Jeffrey D. Viox Chunyu Song Anthony T. Wu Sheng-Kwei Song Sonika Dahiya Joshua B. Rubin
author_sort	Zezhong Ye
title	Diffusion histology imaging differentiates distinct pediatric brain tumor histology
title_short	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_sort	diffusion histology imaging differentiates distinct pediatric brain tumor histology
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/268ca202f1384572b80a32203fadf34f
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Diffusion histology imaging differentiates distinct pediatric brain tumor histology

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