| Autor: |
Yanran Du, Jing Jiao, Aili Cao, Chao Ji, Man Li, Chenli Ji, Yang Wu, Yi Guo, Yuanyuan Wang, Jianqiao Zhou, Yunyun Ren |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Prenatal diagnosisREFERENCES. 42(11) |
| ISSN: |
1097-0223 |
| Popis: |
To establish a classification model for the evaluation of rat fetal lung maturity (FLM) using radiomics technology.A total of 430 high-throughput features were extracted per fetal lung image from 134 fetal lung ultrasound images (four-cardiac-chamber views) of 67 Sprague-Dawley (SD) fetal rats with a gestational age of 16-21 days. The detection of fetal lung tissues included histopathological staining and the expression of surface proteins SP-A, SP-B, and SP-C. A machine learning classification model was established using a support vector machine based on histopathological results to analyze the relationship between fetal lung texture characteristics and FLM.The rat fetal lungs were divided into two groups: terminal sac period (SD1) and canalicular period (SD2). The mRNA transcription and protein expression level of SP-C protein were significantly higher in the SD1 group than in the SD2 group (p 0.05). The diagnostic performance of the rat FLM classification model was measured as follows: area under the receiver operating characteristic curve (AUC), 0.93 (training set) and 0.89 (validation set); sensitivity, 89.26% (training set) and 87.10% (validation set); specificity, 85.87% (training set) and 79.17% (validation set); and accuracy, 87.79% (training set) and 83.64% (validation set).Ultrasound-based radiomics technology can be used to evaluate the FLM of rats, which lays a foundation for further research on this technology in human fetal lungs. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
Plný text