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A deep belief network-based clinical decision system for patients with osteosarcoma

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based appr...

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Autores principales: Li, Wenle, Dong, Youzheng, Liu, Wencai, Tang, Zhiri, Sun, Chenyu, Lowe, Scott, Chen, Shuya, Bentley, Rachel, Zhou, Qin, Xu, Chan, Li, Wanying, Wang, Bing, Wang, Haosheng, Dong, Shengtao, Hu, Zhaohui, Liu, Qiang, Cai, Xintian, Feng, Xiaowei, Zhao, Wei, Yin, Chengliang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716099/
https://www.ncbi.nlm.nih.gov/pubmed/36466868
http://dx.doi.org/10.3389/fimmu.2022.1003347
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author Li, Wenle
Dong, Youzheng
Liu, Wencai
Tang, Zhiri
Sun, Chenyu
Lowe, Scott
Chen, Shuya
Bentley, Rachel
Zhou, Qin
Xu, Chan
Li, Wanying
Wang, Bing
Wang, Haosheng
Dong, Shengtao
Hu, Zhaohui
Liu, Qiang
Cai, Xintian
Feng, Xiaowei
Zhao, Wei
Yin, Chengliang
author_facet Li, Wenle
Dong, Youzheng
Liu, Wencai
Tang, Zhiri
Sun, Chenyu
Lowe, Scott
Chen, Shuya
Bentley, Rachel
Zhou, Qin
Xu, Chan
Li, Wanying
Wang, Bing
Wang, Haosheng
Dong, Shengtao
Hu, Zhaohui
Liu, Qiang
Cai, Xintian
Feng, Xiaowei
Zhao, Wei
Yin, Chengliang
author_sort Li, Wenle
collection PubMed
description Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.
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spelling pubmed-97160992022-12-03 A deep belief network-based clinical decision system for patients with osteosarcoma Li, Wenle Dong, Youzheng Liu, Wencai Tang, Zhiri Sun, Chenyu Lowe, Scott Chen, Shuya Bentley, Rachel Zhou, Qin Xu, Chan Li, Wanying Wang, Bing Wang, Haosheng Dong, Shengtao Hu, Zhaohui Liu, Qiang Cai, Xintian Feng, Xiaowei Zhao, Wei Yin, Chengliang Front Immunol Immunology Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making. Frontiers Media S.A. 2022-11-18 /pmc/articles/PMC9716099/ /pubmed/36466868 http://dx.doi.org/10.3389/fimmu.2022.1003347 Text en Copyright © 2022 Li, Dong, Liu, Tang, Sun, Lowe, Chen, Bentley, Zhou, Xu, Li, Wang, Wang, Dong, Hu, Liu, Cai, Feng, Zhao and Yin 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 Immunology
Li, Wenle
Dong, Youzheng
Liu, Wencai
Tang, Zhiri
Sun, Chenyu
Lowe, Scott
Chen, Shuya
Bentley, Rachel
Zhou, Qin
Xu, Chan
Li, Wanying
Wang, Bing
Wang, Haosheng
Dong, Shengtao
Hu, Zhaohui
Liu, Qiang
Cai, Xintian
Feng, Xiaowei
Zhao, Wei
Yin, Chengliang
A deep belief network-based clinical decision system for patients with osteosarcoma
title A deep belief network-based clinical decision system for patients with osteosarcoma
title_full A deep belief network-based clinical decision system for patients with osteosarcoma
title_fullStr A deep belief network-based clinical decision system for patients with osteosarcoma
title_full_unstemmed A deep belief network-based clinical decision system for patients with osteosarcoma
title_short A deep belief network-based clinical decision system for patients with osteosarcoma
title_sort deep belief network-based clinical decision system for patients with osteosarcoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9716099/
https://www.ncbi.nlm.nih.gov/pubmed/36466868
http://dx.doi.org/10.3389/fimmu.2022.1003347
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