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Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method
The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed usi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401675/ https://www.ncbi.nlm.nih.gov/pubmed/34442431 http://dx.doi.org/10.3390/jpm11080786 |
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author | Chen, Chaoyue Cheng, Yisong Xu, Jianfeng Zhang, Ting Shu, Xin Huang, Wei Hua, Yu Zhang, Yang Teng, Yuen Zhang, Lei Xu, Jianguo |
author_facet | Chen, Chaoyue Cheng, Yisong Xu, Jianfeng Zhang, Ting Shu, Xin Huang, Wei Hua, Yu Zhang, Yang Teng, Yuen Zhang, Lei Xu, Jianguo |
author_sort | Chen, Chaoyue |
collection | PubMed |
description | The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset. |
format | Online Article Text |
id | pubmed-8401675 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84016752021-08-29 Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method Chen, Chaoyue Cheng, Yisong Xu, Jianfeng Zhang, Ting Shu, Xin Huang, Wei Hua, Yu Zhang, Yang Teng, Yuen Zhang, Lei Xu, Jianguo J Pers Med Article The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset. MDPI 2021-08-12 /pmc/articles/PMC8401675/ /pubmed/34442431 http://dx.doi.org/10.3390/jpm11080786 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Chen, Chaoyue Cheng, Yisong Xu, Jianfeng Zhang, Ting Shu, Xin Huang, Wei Hua, Yu Zhang, Yang Teng, Yuen Zhang, Lei Xu, Jianguo Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title | Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title_full | Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title_fullStr | Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title_full_unstemmed | Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title_short | Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method |
title_sort | automatic meningioma segmentation and grading prediction: a hybrid deep-learning method |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8401675/ https://www.ncbi.nlm.nih.gov/pubmed/34442431 http://dx.doi.org/10.3390/jpm11080786 |
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