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Updated: Jul 8, 2025
High Frequency Ultrasound for the Analysis of Fetal and Placental Development In Vivo
Published on: November 8, 2018
Development and validation of a predictive model for fetal cerebral maturation using ultrasound for fetuses with
Ruan Peng1, Xia Yin1, Yan Liu2
1Department of Ultrasonic Medicine, Fetal Medical Centre, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Background:
Investigation of fetal cerebral maturation (FCM) is necessary and important to provide crucial prognostic information for normal and high-risk fetuses. The study aimed to develop a valid and quantitative predictive model for assessing FCM using ultrasound and validate the model for fetuses with normal and restricted growth.
Methods:
This was a multicenter prospective observational study. Fetuses with normal growth recruited from a university teaching hospital (Center 1) and a municipal maternal unit (Center 2) were included in the training set and external validation set 1, respectively. The 124 growth-restricted fetuses enrolled in Center 1 were included in validation set 2. FCM was used to describe the gestational age (GA) in this study. The model was developed based on the sum of fetal cranial parameters (total fetal cranial parameters), including head circumference (HC) and depths of the insula (INS) and sylvian fissure (SF), parieto-occipital fissure (POF), and calcarine fissure (CF). A regression model, constructed based on total fetal cranial parameters and predicted GA, was established using the training set and validated using external validation set 1 and validation set 2.
Results:
The intra- and interobserver intraclass correlation coefficients for HC, and depths of the INS and SF, POF, and CF were >0.90. An exponential regression equation was used to predict FCM: predicted GA of FCM (weeks) =11.16 × exp (0.003 × total fetal cranial parameters) (P<0.001; adjusted R2=0.973), standard error of estimate, 0.67 weeks. The standard error of the predicted GA of FCM from the model was ±4.7 days. In the validation set 1, the mean standard error of the developed prediction model for FCM was 0.97 weeks. The predictive model showed that FCM was significantly delayed in validation set 2 (2.10±1.31 weeks, P<0.001), considering the GA per the last menstrual period.
Conclusions:
The predictive performance of the FCM model developed in this study was excellent, and the novel model may be a valuable investigative tool during clinical implementation.
