Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity
© 2022. The Author(s)..
To develop a novel method for predicting neonatal respiratory morbidity (NRM) by ultrasound-based radiomics technology. In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 295 fetal lung ultrasound images (four-chamber view) in 295 single pregnancies. Images had been obtained between 28+3 and 37+6 weeks of gestation within 72 h before delivery. A machine-learning model built by RUSBoost (Random under-sampling with AdaBoost) architecture was created using 20 radiomics features extracted from the images and 2 clinical features (gestational age and pregnancy complications) to predict the possibility of NRM. Of the 295 standard fetal lung ultrasound images included, 210 in the training set and 85 in the testing set. The overall performance of the neonatal respiratory morbidity prediction model achieved AUC of 0.88 (95% CI 0.83-0.92) in the training set and 0.83 (95% CI 0.79-0.97) in the testing set, sensitivity of 84.31% (95% CI 79.06-89.44%) in the training set and 77.78% (95% CI 68.30-87.43%) in the testing set, specificity of 81.13% (95% CI 78.16-84.07%) in the training set and 82.09% (95% CI 77.65-86.62%) in the testing set, and accuracy of 81.90% (95% CI 79.34-84.41%) in the training set and 81.18% (95% CI 77.33-85.12%) in the testing set. Ultrasound-based radiomics technology can be used to predict NRM. The results of this study may provide a novel method for non-invasive approaches for the prenatal prediction of NRM.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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| Enthalten in: |
Scientific reports - 12(2022), 1 vom: 26. Juli, Seite 12747 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Du, Yanran [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 28.07.2022 Date Revised 06.10.2022 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1038/s41598-022-17129-8 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM344027325 |
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| 520 | |a © 2022. The Author(s). | ||
| 520 | |a To develop a novel method for predicting neonatal respiratory morbidity (NRM) by ultrasound-based radiomics technology. In this retrospective study, 430 high-throughput features per fetal-lung image were extracted from 295 fetal lung ultrasound images (four-chamber view) in 295 single pregnancies. Images had been obtained between 28+3 and 37+6 weeks of gestation within 72 h before delivery. A machine-learning model built by RUSBoost (Random under-sampling with AdaBoost) architecture was created using 20 radiomics features extracted from the images and 2 clinical features (gestational age and pregnancy complications) to predict the possibility of NRM. Of the 295 standard fetal lung ultrasound images included, 210 in the training set and 85 in the testing set. The overall performance of the neonatal respiratory morbidity prediction model achieved AUC of 0.88 (95% CI 0.83-0.92) in the training set and 0.83 (95% CI 0.79-0.97) in the testing set, sensitivity of 84.31% (95% CI 79.06-89.44%) in the training set and 77.78% (95% CI 68.30-87.43%) in the testing set, specificity of 81.13% (95% CI 78.16-84.07%) in the training set and 82.09% (95% CI 77.65-86.62%) in the testing set, and accuracy of 81.90% (95% CI 79.34-84.41%) in the training set and 81.18% (95% CI 77.33-85.12%) in the testing set. Ultrasound-based radiomics technology can be used to predict NRM. The results of this study may provide a novel method for non-invasive approaches for the prenatal prediction of NRM | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Jiao, Jing |e verfasserin |4 aut | |
| 700 | 1 | |a Ji, Chao |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Man |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yuanyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jianqiao |e verfasserin |4 aut | |
| 700 | 1 | |a Ren, Yunyun |e verfasserin |4 aut | |
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