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Application of radiography of computed tomography in non-small cell lung cancer using prognosis model
OBJECTIVE: Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients. METHOD: CT scan data of NSCLC 200 pati...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105650/ https://www.ncbi.nlm.nih.gov/pubmed/32256167 http://dx.doi.org/10.1016/j.sjbs.2020.02.016 |
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author | Jin, Yifeng Lu, Tao |
author_facet | Jin, Yifeng Lu, Tao |
author_sort | Jin, Yifeng |
collection | PubMed |
description | OBJECTIVE: Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients. METHOD: CT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared. RESULTS: 30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher. CONCLUSION: The prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC. |
format | Online Article Text |
id | pubmed-7105650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-71056502020-03-31 Application of radiography of computed tomography in non-small cell lung cancer using prognosis model Jin, Yifeng Lu, Tao Saudi J Biol Sci Article OBJECTIVE: Studying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients. METHOD: CT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared. RESULTS: 30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher. CONCLUSION: The prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC. Elsevier 2020-04 2020-03-04 /pmc/articles/PMC7105650/ /pubmed/32256167 http://dx.doi.org/10.1016/j.sjbs.2020.02.016 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Jin, Yifeng Lu, Tao Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title | Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title_full | Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title_fullStr | Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title_full_unstemmed | Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title_short | Application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
title_sort | application of radiography of computed tomography in non-small cell lung cancer using prognosis model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7105650/ https://www.ncbi.nlm.nih.gov/pubmed/32256167 http://dx.doi.org/10.1016/j.sjbs.2020.02.016 |
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