AbstractsBiology & Animal Science

This Thesis consists of different studies focused in advancing towards the development of non­invasive imaging biomarkers to predict perinatal clinical outcomes. The structure of the PhD Thesis is divided in four projects to explore the development of a series of new methods based on image texture analysis allowing the analysis of medical images (i.e. ultrasound or magnetic resonance imaging) in the field of fetal medicine applications -mainly fetal lung maturity and fetal brain assessment-, to test their reproducibility and to select the best performing approach to develop an imaging biomarker predicting a clinical outcome of interest. The majority of the work was focused on developing a quantitative imaging biomarker for neonatal respiratory morbidity. In order to achieve the objectives and to explore the development of a quantitative imaging biomarker fetal thorax ultrasound images were used for the studies 1, 2 and 3 to predict neonatal respiratory morbidity. To test the transversality of the quantitative texture analysis in other pathological models, fetal brain magnetic resonance images from Small-for-Gestational Age fetuses were used in study 4. First study demonstrates that quantitative image features extracted from fetal thorax ultrasound images correlate with gestational age. This study also demonstrated that it is posible to extract information from the tissue in a non-invasive manner that correlated with the underlying physiological process, regular fetal lung maturation. In the second study the correlation between texture analyses and the existing fetal lung maturity test was tested. Thus, the second study provided evidence that the image features from lung ultrasound images correlate with fetal lung maturity test assessed by a standard test as TDx-FLM II. These findings opened the possibility to explore the introduction of non-invasive techniques into clinical practice to test fetal lung maturity. In the third study, the basic principles of a novel method to predict neonatal respiratory morbidity risk (quantusFLM™) were described, and a validation was performed to assess the ability of the method to blindly predict the risk of neonatal respiratory morbidity. Remarkably, this study provides evidence that purpose­developed software based on quantitative texture analysis of fetal lung ultrasound images predicts neonatal respiratory morbidity with a similar performance to that reported for commercial fetal lung maturity tests in amniotic fluid. Additionally, in the last study the ability of image texture analysis to detect abnormalities in different fetal brain areas was evaluated, and their association with abnormal neonatal neurobehavior was tested. This study demonstrated the potential of quantitative imaging texture analysis for other image acquisition techniques and clinical outcomes. As a final conclusion, this Thesis provides evidence that the non-invasive quantitative imaging techniques based on texture analysis extract quantitative information related to the underlying tissue that could be used…