Application of ultrasound‐based radiomics technology in fetal‐lung‐texture analysis in pregnancies complicated by gestational diabetes and/or pre‐eclampsia

Du, Yanran; Fang, Zhou; Jiao, Bo; Xi, Guannan; Zhu, Chen; Ren, Yanping; Guo, Yi; Wang, Yuanyuan

doi:10.1002/uog.22037

Cited by 25 publications

(38 citation statements)

References 31 publications

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“…The results of these studies indicate that some gestational diseases can affect FLM. The latest research by Du et al [ 34 ] found that GDM, PE, and normal fetal lungs can be quickly and accurately classified by ultrasound-based radiomics techniques, indicating that differences in fetal lung development between gestational diseases (GDM and PE), and normal pregnancies were highly significant. Our study used DL algorithms to extract information from numerous normal fetal lung ultrasound images to establish a normal fetal lung grading model that can help to identify abnormal lung development that can result from some gestational diseases.…”

Section: Discussionmentioning

confidence: 99%

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

Xia

Tan

Liu

et al. 2021

Chinese Medical Journal

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Background: Prenatal evaluation of fetal lung maturity (FLM) is a challenge, and an effective non-invasive method for prenatal assessment of FLM is needed. The study aimed to establish a normal fetal lung gestational age (GA) grading model based on deep learning (DL) algorithms, validate the effectiveness of the model, and explore the potential value of DL algorithms in assessing FLM. Methods: A total of 7013 ultrasound images obtained from 1023 normal pregnancies between 20 and 41 + 6 weeks were analyzed in this study. There were no pregnancy-related complications that affected fetal lung development, and all infants were born without neonatal respiratory diseases. The images were divided into three classes based on the gestational week: class I: 20 to 29 + 6 weeks, class II: 30 to 36 + 6 weeks, and class III: 37 to 41 + 6 weeks. There were 3323, 2142, and 1548 images in each class, respectively. First, we performed a pre-processing algorithm to remove irrelevant information from each image. Then, a convolutional neural network was designed to identify different categories of fetal lung ultrasound images. Finally, we used ten-fold cross-validation to validate the performance of our model. This new machine learning algorithm automatically extracted and classified lung ultrasound image information related to GA. This was used to establish a grading model. The performance of the grading model was assessed using accuracy, sensitivity, specificity, and receiver operating characteristic curves. Results: A normal fetal lung GA grading model was established and validated. The sensitivity of each class in the independent test set was 91.7%, 69.8%, and 86.4%, respectively. The specificity of each class in the independent test set was 76.8%, 90.0%, and 83.1%, respectively. The total accuracy was 83.8%. The area under the curve (AUC) of each class was 0.982, 0.907, and 0.960, respectively. The micro-average AUC was 0.957, and the macro-average AUC was 0.949. Conclusions: The normal fetal lung GA grading model could accurately identify ultrasound images of the fetal lung at different GAs, which can be used to identify cases of abnormal lung development due to gestational diseases and evaluate lung maturity after antenatal corticosteroid therapy. The results indicate that DL algorithms can be used as a non-invasive method to predict FLM.

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Section: Discussionmentioning

confidence: 99%

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

Xia

Tan

Liu

et al. 2021

Chinese Medical Journal

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“…Compared to CT or MRI, radiomics on US images is a relatively new and less explored type of analysis. Nonetheless, it is gaining gound [53][54][55][56] .…”

Section: Discussionmentioning

confidence: 99%

Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study)

Nero

Ciccarone

Boldrini

et al. 2020

Sci Rep

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Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting gBRCA1/2 status in women with healthy ovaries.

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“…Besides being employed for oncology diseases, Du et al. ( 115 ) utilized the profile of gestational diabetes mellitus, pre-eclampsia, and normal pregnancies to assess fetal lung and gestational age in gestational diabetes mellitus/pre-eclampsia, supporting the research of neonatal respiratory disorders. Salvatore et al.…”

Section: Some Case Studies and Applicationsmentioning

confidence: 99%

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Zhang

et al. 2022

Front. Oncol.

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The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Application of ultrasound‐based radiomics technology in fetal‐lung‐texture analysis in pregnancies complicated by gestational diabetes and/or pre‐eclampsia

Cited by 25 publications

References 31 publications

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

Establish a normal fetal lung gestational age grading model and explore the potential value of deep learning algorithms in fetal lung maturity evaluation

Germline BRCA 1-2 status prediction through ovarian ultrasound images radiogenomics: a hypothesis generating study (PROBE study)

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

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