Elisenda Bonet-Carne

Performance of an automatic quantitative ultrasound analysis of the fetal lung to predict fetal lung maturity

OBJECTIVE The objective of the study was to evaluate the performance of automatic quantitative ultrasound analysis (AQUA) texture extractor to predict fetal lung maturity tests in amniotic fluid. STUDY DESIGN Singleton pregnancies (24.0-41.0 weeks) undergoing amniocentesis to assess fetal lung maturity (TDx fetal lung maturity assay [FLM]) were included. A manual-delineated box was placed in the lung area of a 4-chamber view of the fetal thorax. AQUA transformed the information into a set of descriptors. Genetic algorithms extracted the most relevant descriptors and then created and validated a model that could distinguish between mature or immature fetal lungs using TDx-FLM as a reference. RESULTS Gestational age at enrollment was (mean [SD]) 32.2 (4.5) weeks. According to the TDx-FLM results, 41 samples were mature and 62 were not. The imaging biomarker based on AQUA presented a sensitivity 95.1%, specificity 85.7%, and an accuracy 90.3% to predict a mature or immature lung. CONCLUSION Fetal lung ultrasound textures extracted by AQUA provided robust features to predict TDx-FLM results.

Correlation Between a Semiautomated Method Based on Ultrasound Texture Analysis and Standard Ultrasound Diagnosis Using White Matter Damage in Preterm Neonates as a Model

Diagnosis of white matter damage by cranial ultrasound imaging is still subject to interobserver variability and has limited sensitivity for predicting abnormal neurodevelopment later in life. In this study, we evaluated the ability of a semiautomated method based on ultrasound texture analysis to identify patterns that correlate with the ultrasound diagnosis of white matter damage.

Automatic Quantitative MRI Texture Analysis in Small-for-Gestational-Age Fetuses Discriminates Abnormal Neonatal Neurobehavior

Background We tested the hypothesis whether texture analysis (TA) from MR images could identify patterns associated with an abnormal neurobehavior in small for gestational age (SGA) neonates. Methods Ultrasound and MRI were performed on 91 SGA fetuses at 37 weeks of GA. Frontal lobe, basal ganglia, mesencephalon and cerebellum were delineated from fetal MRIs. SGA neonates underwent NBAS test and were classified as abnormal if ≥1 area was <5th centile and as normal if all areas were >5th centile. Textural features associated with neurodevelopment were selected and machine learning was used to model a predictive algorithm. Results Of the 91 SGA neonates, 49 were classified as normal and 42 as abnormal. The accuracies to predict an abnormal neurobehavior based on TA were 95.12% for frontal lobe, 95.56% for basal ganglia, 93.18% for mesencephalon and 83.33% for cerebellum. Conclusions Fetal brain MRI textural patterns were associated with neonatal neurodevelopment. Brain MRI TA could be a useful tool to predict abnormal neurodevelopment in SGA.

Feasibility and reproducibility of fetal lung texture analysis by Automatic Quantitative Ultrasound Analysis and correlation with gestational age.

Objective: To evaluate the feasibility and reproducibility of fetal lung texture analysis using a novel automatic quantitative ultrasound analysis and to assess its correlation with gestational age. Methods: Prospective cross-sectional observational study. To evaluate texture features, 957 left and right lung images in a 2D four-cardiac-chamber view plane were previously delineated from fetuses between 20 and 41 weeks of gestation. Quantification of lung texture was performed by the Automatic Quantitative Ultrasound Analysis (AQUA) software to extract image features. A standard learning approach composed of feature transformation and a regression model was used to evaluate the association between texture features and gestational age. Results: The association between weeks of gestation and fetal lung texture quantified by the AQUA software presented a Pearson correlation of 0.97. The association was not influenced by delineation parameters such as region of interest (ROI) localization, ROI size, right/left lung selected or sonographic parameters such as ultrasound equipment or transducer used. Conclusions: Fetal lung texture analysis measured by the AQUA software demonstrated a strong correlation with gestational age. This supports further research to explore the use of this technology to the noninvasive prediction of fetal lung maturity.

Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity

To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound.

Prediction of neonatal respiratory morbidity by quantitative ultrasound lung texture analysis: a multicenter study

BACKGROUND: Prediction of neonatal respiratory morbidity may be useful to plan delivery in complicated pregnancies. The limited predictive performance of the current diagnostic tests together with the risks of an invasive procedure restricts the use of fetal lung maturity assessment. OBJECTIVE: The objective of the study was to evaluate the performance of quantitative ultrasound texture analysis of the fetal lung (quantusFLM) to predict neonatal respiratory morbidity in preterm and early‐term (<39.0 weeks) deliveries. STUDY DESIGN: This was a prospective multicenter study conducted in 20 centers worldwide. Fetal lung ultrasound images were obtained at 25.0–38.6 weeks of gestation within 48 hours of delivery, stored in Digital Imaging and Communication in Medicine format, and analyzed with quantusFLM. Physicians were blinded to the analysis. At delivery, perinatal outcomes and the occurrence of neonatal respiratory morbidity, defined as either respiratory distress syndrome or transient tachypnea of the newborn, were registered. The performance of the ultrasound texture analysis test to predict neonatal respiratory morbidity was evaluated. RESULTS: A total of 883 images were collected, but 17.3% were discarded because of poor image quality or exclusion criteria, leaving 730 observations for the final analysis. The prevalence of neonatal respiratory morbidity was 13.8% (101 of 730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, positive and negative predictive values of 74.3% (75 of 101), 88.6% (557 of 629), 51.0% (75 of 147), and 95.5% (557 of 583), respectively. Accuracy was 86.5% (632 of 730) and positive and negative likelihood ratios were 6.5 and 0.3, respectively. CONCLUSION: The quantusFLM predicted neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests with the advantage of being a noninvasive technique.

Quantitative Analysis of the Cervical Texture by Ultrasound and Correlation with Gestational Age

Objectives: Quantitative texture analysis has been proposed to extract robust features from the ultrasound image to detect subtle changes in the textures of the images. The aim of this study was to evaluate the feasibility of quantitative cervical texture analysis to assess cervical tissue changes throughout pregnancy. Methods: This was a cross-sectional study including singleton pregnancies between 20.0 and 41.6 weeks of gestation from women who delivered at term. Cervical length was measured, and a selected region of interest in the cervix was delineated. A model to predict gestational age based on features extracted from cervical images was developed following three steps: data splitting, feature transformation, and regression model computation. Results: Seven hundred images, 30 per gestational week, were included for analysis. There was a strong correlation between the gestational age at which the images were obtained and the estimated gestational age by quantitative analysis of the cervical texture (R = 0.88). Discussion: This study provides evidence that quantitative analysis of cervical texture can extract features from cervical ultrasound images which correlate with gestational age. Further research is needed to evaluate its applicability as a biomarker of the risk of spontaneous preterm birth, as well as its role in cervical assessment in other clinical situations in which cervical evaluation might be relevant.

CORRELATION OF QUANTITATIVE TEXTURE ANALYSIS OF CRANIAL ULTRASOUND WITH LATER NEUROBEHAVIOR IN PRETERM INFANTS

The purpose of the study was to evaluate the association between a quantitative texture analysis of early neonatal brain ultrasound images and later neurobehavior in preterm infants. A prospective cohort study including 120 preterm (<33 wk of gestational age) infants was performed. Cranial ultrasound images taken early after birth were analyzed in six regions of interest using software based on texture analysis. The resulting texture scores were correlated with the Neonatal Behavioural Assessment Scale (NBAS) at term-equivalent age. The ability of texture scores, in combination with clinical data and standard ultrasound findings, to predict the NBAS results was evaluated. Texture scores were significantly associated with all but one NBAS domain and better predicted NBAS results than clinical data and standard ultrasound findings. The best predictive value was obtained by combining texture scores with clinical information and ultrasound standard findings (area under the curve = 0.94). We conclude that texture analysis of neonatal cranial ultrasound-extracted quantitative features that correlate with later neurobehavior has a higher predictive value than the combination of clinical data with abnormalities in conventional cranial ultrasound.

Evaluation of semiautomated quantification of cranial ultrasound images in newborns as a predictor of Neonatal Behavioral Assessment Scale

Diagnosis of white matter damage by neonatal cranial ultrasound (CrUS) is subject to inter-observer variability and has a low sensitivity to detect late abnormal neurodevelopment in life. In the last decades there have been a significant effort reporting that statistical features of ultrasound images carry important information associated with changes of tissue microstructure. In this work we explored the ability of a semi-automated image processing method to associate ultrasound texture patterns with Neonatal Behavioral Assessment Scale (NBAS) performance in premature neonates. A total of ninety infants born at a median gestational age of 29 weeks were included. The infants underwent one CrUS scan performed at the same day that NBAS test. In this work, we developed a feature selection algorithm to identify combination of features that correlated to NBAS clusters. Our algorithm was then able to predict individual underscored NBAS clusters with accuracy higher than 80% in a “blind” sample.

Contents Vol. 41, 2017

R. Achiron, Tel Hashomer N.S. Adzick, Philadelphia, PA L. Allan, London A.A. Baschat, Baltimore, MD K.J. Blakemore, Baltimore, MD T.-H. Bui, Stockholm F.A. Chervenak, New York, NY T. Chiba, Tokyo R. Chmait, Los Angeles, CA F. Crispi, Barcelona J.E. De Lia, Milwaukee, WI J.A. Deprest, Leuven G.C. Di Renzo, Perugia J.W. Dudenhausen, Berlin N.M. Fisk, Brisbane, QLD A.W. Flake, Philadelphia, PA U. Gembruch, Bonn M.R. Harrison, San Francisco, CA J.C. Hobbins, Denver, CO L.K. Hornberger, Edmonton, AB E.R.M. Jauniaux, London M.P. Johnson, Philadelphia, PA J.-M. Jouannic, Paris P.M. Kyle, London O. Lapaire, Basel S. Lipitz, Tel Hashomer E. Llurba, Barcelona G. Malinger, Tel Aviv G. Mari, Detroit, MI M. Martinez-Ferro, Buenos Aires A. McLennan, Sydney, NSW K.J. Moise, Houston, TX F. Molina, Granada K.H. Nicolaides, London L. Otaño, Buenos Aires Z. Papp, Budapest R.A. Quintero, Miami, FL G. Ryan, Toronto, ON J. Rychik, Philadelphia, PA H. Sago, Tokyo W. Sepulveda, Santiago P. Stone, Auckland D.V. Surbek, Bern B.J. Trudinger, Westmead, NSW Y. Ville, Paris J.M.G. van Vugt, Nijmegen Clinical Advances and Basic Research