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Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

PURPOSE: To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. MATERIALS AND METHODS:...

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Autores principales: Zhang, Tao, Zhang, YueHua, Liu, Xinglong, Xu, Hanyue, Chen, Chaoyue, Zhou, Xuan, Liu, Yichun, Ma, Xuelei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905094/
https://www.ncbi.nlm.nih.gov/pubmed/33643890
http://dx.doi.org/10.3389/fonc.2020.521831
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author Zhang, Tao
Zhang, YueHua
Liu, Xinglong
Xu, Hanyue
Chen, Chaoyue
Zhou, Xuan
Liu, Yichun
Ma, Xuelei
author_facet Zhang, Tao
Zhang, YueHua
Liu, Xinglong
Xu, Hanyue
Chen, Chaoyue
Zhou, Xuan
Liu, Yichun
Ma, Xuelei
author_sort Zhang, Tao
collection PubMed
description PURPOSE: To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. MATERIALS AND METHODS: A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. RESULT: Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. CONCLUSION: In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.
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spelling pubmed-79050942021-02-26 Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades Zhang, Tao Zhang, YueHua Liu, Xinglong Xu, Hanyue Chen, Chaoyue Zhou, Xuan Liu, Yichun Ma, Xuelei Front Oncol Oncology PURPOSE: To evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics. MATERIALS AND METHODS: A retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model. RESULT: Our analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively. CONCLUSION: In conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect. Frontiers Media S.A. 2021-02-11 /pmc/articles/PMC7905094/ /pubmed/33643890 http://dx.doi.org/10.3389/fonc.2020.521831 Text en Copyright © 2021 Zhang, Zhang, Liu, Xu, Chen, Zhou, Liu and Ma http://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 Oncology
Zhang, Tao
Zhang, YueHua
Liu, Xinglong
Xu, Hanyue
Chen, Chaoyue
Zhou, Xuan
Liu, Yichun
Ma, Xuelei
Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title_full Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title_fullStr Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title_full_unstemmed Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title_short Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades
title_sort application of radiomics analysis based on ct combined with machine learning in diagnostic of pancreatic neuroendocrine tumors patient’s pathological grades
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7905094/
https://www.ncbi.nlm.nih.gov/pubmed/33643890
http://dx.doi.org/10.3389/fonc.2020.521831
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