Prenatal prediction and therapy for NRM are an effective way to improve the quality of life of NRM newborns. There is a consensus to study non-invasive methods to predict NRM using fetal lung ultrasound images. However, there is no unified feature set for the prenatal prediction of NRM, and the dataset collected in medical practice is often imbalanced and few-shot. To tackle these challenges, our study focuses on the design of feature sets with a strong representation of fetal lung ultrasound images and effective classification modelling methods.
4.1 The feature set for predicting NRM
Considering that the fetal lung in the ultrasound image is homogeneous, we designed radiomics features based on the image greyscale and texture, which can avoid the influence of the ROI’s size and location on feature extraction. For each fetus, 380 radiomics features were extracted from the fetal lung region of ultrasound images, and 10 of them were selected for modelling. The energy of horizontal, which characterizes the brightness in the horizontal direction of the wavelet transform, has a mean value of 1400 in normal fetal lungs, which is higher than 1200 in NRM fetal lungs. The high grey-level run emphasis of the normal fetal lung has a higher mean value of 298 than the NRM fetal lungs of 279, which means that the fetal lung region is more homogeneous in normal fetal lungs than NRM fetal lungs. For the long-run high grey-level emphasis of vertical feature, the mean value of the normal fetal lungs is 432, which is smaller than that of the NRM fetal lung of 462, which suggests that the fetal lung region is more delicate in normal fetal lungs than NRM fetal lungs. It can be concluded that the lung region of normal fetuses has a more delicate and homogeneous texture on the ultrasound image and is brighter than that of NRM fetuses. The features we selected were also stable. The radiomics features extracted from the square ROI and the manual ROI achieved similar performance outcomes with the same modelling method (the difference was less than 0.2 for each measure), as shown in Table 5.
In addition to radiomics features, GA and GDM, two clinical features identified to be strongly correlated with NRM, were also added to the feature set. Newborns with a low GA have a significantly increased risk of NRM due to immature lungs, and GDM in pregnant women leads to delayed lung development in the fetus, increasing the risk of NRM. As shown in Table 5, the combination of GDM and GA obtained an increase from 0.8 to 0.83 in the AUC and from 0.58 to 0.72 in SENS compared to the prediction by only GA. With the addition of radiomics features, the SPEC and SENS were both significantly improved. In conclusion, the feature set designed in this study that includes radiomics features, GA, and GDM is more effective for NRM prediction and is not affected by the size or location of the ROI.
4.2 Model development
Imbalance and few-shot are inevitable in medical datasets, which pose many challenges for modelling. As shown in Table 4, there is a large class bias and poor classification performance on small imbalanced datasets using the conventional SVM. The methods of data augmentation, cost-sensitive learning, and ensemble learning are commonly used on imbalanced few-shot datasets. Here, these methods were performed and analysed to find the most effective modelling method.
The cost-sensitive SVM and AdaBoost show an improvement of 0.21 and 0.36 in SENS compared with the SVM in Table 4, but there is a decrease of 0.10 and 0.15 in SPEC in the training set. As for the cost-sensitive SVM, since there are few NRM samples, a higher cost is needed, which makes the compression of boundaries more severe, and the classifier tends to sacrifice multiple normal samples to ensure that one NRM sample is correct with a sharp decline in the generalization performance. The AdaBoost has a better performance than cost-sensitive SVM, with a SENS of 0.68 and a SPEC of 0.84. The ensemble learning method's lower overfitting allows it to exhibit a better generalization performance than the individual learner SVM or the cost-sensitive SVM.
Training on the balanced training set augmented with ADASYN, the SVM and AdaBoost does not show a significant improvement compared to training on the original imbalanced dataset, with an increase of 0.35 and 0.23 in SENS and a decrease of 0.25 and 0.26 in SPEC. For better illustration, we used t-SNE  to visualize the sample distribution of the original dataset and the balanced dataset augmented by ADASYN. As shown in Figure 6, there is aliasing between normal and NRM samples, making it difficult to classify. By generating pseudo-samples around the minority class, ADASYN leads the classifier to draw more attention to the NRM samples. However, it also exacerbates aliasing and results in poor classification performance. The generated pseudo-samples also tend to introduce plenty of noise, especially when the aliasing of samples is terrible. The data augmentation method is not appropriate in our application.
The SENS of SMOTEBoost is still low because aliasing in the dataset makes SMOTE introducing considerable noise. RUSBoost shows better classification performance than other methods. It reaches a SENS of 0.72, a SPEC of 0.82, an ACC of 0.82, and an AUC of 0.83 in the training set and a SENS of 0.82, a SPEC of 0.84, ACC of 0.84, and an AUC of 0.87 in the test set. RUSBoost can reduce overfitting and improve the classification model's generalization ability by combining weak base learners and bootstrap sampling with the AdaBoost algorithm. The input dataset of each learner is obtained by bootstrap undersampling, which enriches the sample distribution that the base learners have learned and reduce the effects of imbalance. The drawback of massive sample loss of undersampling in a small dataset is compensated by ensemble learning, while random undersampling ensures that the samples are real and avoids the noise that caused by data augmentation.
4.3 Strengths and limitations
Our study has three strengths. First, to the best of our knowledge, this is the first study to incorporate GDM, GA, and radiomics features for NRM prenatal prediction. The diagnostic efficacy of the model we developed based on fetal lung ultrasound images in this study reached which are similar to those of many previous reports of amniocentesis   . Second, we developed a practical modelling approach to address the problems of imbalance and few-shot. RUSBoost shows excellent performance and generalization capabilities compared with the other methods used for comparison in this study. Third, we used radiomics features based on the image greyscale and texture for the prenatal prediction of NRM, whose performance is efficient and robust, without the influences of segmentation results.
As a retrospective study, this study has some limitations that should be acknowledged. Clinical outcome of the fetuses depends on several clinical factors. In addition to GA and GDM, more clinical information could be studied for its correlation with fetal lung development and used for NRM prediction. A comparative study on the right and left lungs to verify the generalizability of the method between the right and left lungs is also needed. As for those limitations, a multicentre study is underway.