Application of ultrasound-based radiomics technology in the evaluation of fetal lung maturity

doi:10.16150/j.1671-2870.2022.03.006

Abstract

Abstract:

Objective: To establish a model with ultrasound-based radiomics technology for evaluating fetal lung maturity and verify its efficacy in predicting risk of newborn respiratory disease. Methods: A total of 295 singleton pregnancies were enrolled, and fetal lung ultrasound images (four-chamber view) of each fetal were obtained within 72 hours before delivery. The 295 images were divided into 2 groups according to gestational age (GA) of the day fetal lung ultrasound images collected on examination day: Group 1(GA <36 weeks) and Group 2(GA 36-37 weeks). Images of Group 1 （66） and Group 2 （229） were further grouped into training set（40, 26） and validation set（95, 134）,respectively. High throughput radiomics features were extracted from each fetal lung ultrasound image by fetal lung texture analysis based on ultrasound-based radiomics technology. Based on outcomes of fetus, diagnostic models by Group 1 or 2 for predicting risk of newborn respiratory disease were established combined with pregnancy complications using training set of Group 1 or Group 2, respectively, and the predictive efficacy were verified in correspondingly validation set. Results: The diagnostic performance of models by Group1 and 2 were as follows: sensitivity were 83.3%(Group 1) and 75.0%, respectively; specificity were 84.6% and 78.3%,respectively; accuracy were 80.8% and 77.2%, respectively. Conclusions: The fetal lung maturity evaluation model based on ultrasonic-based radiomics technology is a new method for noninvasive evaluation of fetal lung maturity, Which have certain efficacy in predicting newborn respiratory disease.

Key words: Radiomics, Fetal lung maturity, Neonatal respiratory morbidity, Fetal lung ultrasound, Pregnancy complications

CLC Number:

R541.7

DU Yanran, JIAO Jing, REN Yunyun, ZHOU Jianqiao. Application of ultrasound-based radiomics technology in the evaluation of fetal lung maturity[J]. Journal of Diagnostics Concepts & Practice, 2022, 21(03): 326-330.

Figures/Tables 5

References 22

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分组	截点	发生新生儿呼吸系统疾病	无新生儿呼吸系统疾病
组1
训练集	0.5	0.881（0.049~0.986）	0.159（0.033~0.621）
验证集		0.813（0.213~0.981）	0.346（0.078~0.630）
组2
训练集	0.5	0.538（0.290~0.748）	0.414（0.182~0.763）
验证集		0.543（0.324~0.733）	0.403（0.206~0.742）

分组	发生新生儿呼吸系统疾病		无新生儿呼吸系统疾病
分组	高危组	低危组	高危组	低危组
组1
训练集	20（83.3%）	4（16.7%）	1（6.3%）	15（93.7%）
验证集	10（76.9%）	3（23.1%）	2（15.4%）	11（84.6%）
组2
训练集	13（81.2%）	3（18.8%）	30（25.4%）	88（74.6%）
验证集	12（75.0%）	4（25.0%）	18（22.8%）	61（77.2%）

分组	新生儿呼吸系统疾病例数（n）	灵敏度（%）	特异度（%）	准确度（%）	阳性预测值（%）	阴性预测值（%）	阳性似然比	阴性似然比
组1（n=26）	13	83.3	84.6	80.8	76.9	84.6	5.4	0.2
组2（n=95）	16	75.0	78.3	77.2	40.0	93.8	3.5	0.3