2017年
Automated prediction of emphysema visual score using homology-based quantification of low-attenuation lung region.
PloS one
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- 巻
- 12
- 号
- 5
- 開始ページ
- e0178217
- 終了ページ
- 記述言語
- 英語
- 掲載種別
- 研究論文(学術雑誌)
- DOI
- 10.1371/journal.pone.0178217
- 出版者・発行元
- PUBLIC LIBRARY SCIENCE
OBJECTIVE: The purpose of this study was to investigate the relationship between visual score of emphysema and homology-based emphysema quantification (HEQ) and evaluate whether visual score was accurately predicted by machine learning and HEQ. MATERIALS AND METHODS: A total of 115 anonymized computed tomography images from 39 patients were obtained from a public database. Emphysema quantification of these images was performed by measuring the percentage of low-attenuation lung area (LAA%). The following values related to HEQ were obtained: nb0 and nb1. LAA% and HEQ were calculated at various threshold levels ranging from -1000 HU to -700 HU. Spearman's correlation coefficients between emphysema quantification and visual score were calculated at the various threshold levels. Visual score was predicted by machine learning and emphysema quantification (LAA% or HEQ). Random Forest was used as a machine learning algorithm, and accuracy of prediction was evaluated by leave-one-patient-out cross validation. The difference in the accuracy was assessed using McNemar's test. RESULTS: The correlation coefficients between emphysema quantification and visual score were as follows: LAA% (-950 HU), 0.567; LAA% (-910 HU), 0.654; LAA% (-875 HU), 0.704; nb0 (-950 HU), 0.552; nb0 (-910 HU), 0.629; nb0 (-875 HU), 0.473; nb1 (-950 HU), 0.149; nb1 (-910 HU), 0.519; and nb1 (-875 HU), 0.716. The accuracy of prediction was as follows: LAA%, 55.7% and HEQ, 66.1%. The difference in accuracy was statistically significant (p = 0.0290). CONCLUSION: LAA% and HEQ at -875 HU showed a stronger correlation with visual score than those at -910 or -950 HU. HEQ was more useful than LAA% for predicting visual score.
- リンク情報
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- DOI
- https://doi.org/10.1371/journal.pone.0178217
- PubMed
- https://www.ncbi.nlm.nih.gov/pubmed/28542398
- PubMed Central
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444793
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000402062800056&DestApp=WOS_CPL
- ID情報
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- DOI : 10.1371/journal.pone.0178217
- ISSN : 1932-6203
- PubMed ID : 28542398
- PubMed Central 記事ID : PMC5444793
- Web of Science ID : WOS:000402062800056