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A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans
OBJECTIVE: Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. METHODS: A total of 1413 coronal and sagittal magnetic resonanc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289618/ https://www.ncbi.nlm.nih.gov/pubmed/35860294 http://dx.doi.org/10.3389/fnins.2022.900519 |
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author | Feng, Tianshun Fang, Yi Pei, Zhijie Li, Ziqi Chen, Hongjie Hou, Pengwei Wei, Liangfeng Wang, Renzhi Wang, Shousen |
author_facet | Feng, Tianshun Fang, Yi Pei, Zhijie Li, Ziqi Chen, Hongjie Hou, Pengwei Wei, Liangfeng Wang, Renzhi Wang, Shousen |
author_sort | Feng, Tianshun |
collection | PubMed |
description | OBJECTIVE: Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. METHODS: A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance. RESULTS: A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI. CONCLUSION: This study highlights the potential of the CNN model for the efficient assessment of PA invasion. |
format | Online Article Text |
id | pubmed-9289618 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92896182022-07-19 A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans Feng, Tianshun Fang, Yi Pei, Zhijie Li, Ziqi Chen, Hongjie Hou, Pengwei Wei, Liangfeng Wang, Renzhi Wang, Shousen Front Neurosci Neuroscience OBJECTIVE: Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. METHODS: A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance. RESULTS: A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI. CONCLUSION: This study highlights the potential of the CNN model for the efficient assessment of PA invasion. Frontiers Media S.A. 2022-07-04 /pmc/articles/PMC9289618/ /pubmed/35860294 http://dx.doi.org/10.3389/fnins.2022.900519 Text en Copyright © 2022 Feng, Fang, Pei, Li, Chen, Hou, Wei, Wang and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Feng, Tianshun Fang, Yi Pei, Zhijie Li, Ziqi Chen, Hongjie Hou, Pengwei Wei, Liangfeng Wang, Renzhi Wang, Shousen A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title | A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title_full | A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title_fullStr | A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title_full_unstemmed | A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title_short | A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans |
title_sort | convolutional neural network model for detecting sellar floor destruction of pituitary adenoma on magnetic resonance imaging scans |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9289618/ https://www.ncbi.nlm.nih.gov/pubmed/35860294 http://dx.doi.org/10.3389/fnins.2022.900519 |
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