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

Bonet‐Carne, Elisenda; Palacio, Montse; Cobo, Teresa; Perez‐Moreno, Alvaro; López, Marta; Piraquive, Juan Pablo; Ramírez, José; Botet, Francesc; Marqués, Ferran; Gratacós, Eduard

doi:10.1002/uog.13441

Cited by 66 publications

(77 citation statements)

References 46 publications

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“…In a recent single-center study, we tested software based on quantitative texture analysis of the fetal lung (quantusFLM) trained to predict NRM. The software achieved a predictive accuracy similar to that commonly reported for fetal lung maturity testing of the amniotic fluid [22]. …”

Section: Introductionsupporting

confidence: 60%

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

Palacio¹,

Bonet‐Carne²,

Cobo³

et al. 2017

American Journal of Obstetrics and Gynecology

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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 this 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% (101/730). The quantusFLM predicted neonatal respiratory morbidity with a sensitivity, specificity, and positive and negative predictive values of 74.3% (75/101), 88.6% (557/629), 51.0% (75/147), and 95.5% (557/583), respectively. Accuracy was 86.5% (632/730), and the 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 non-invasive technique.

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Section: Introductionsupporting

confidence: 60%

“…Delineated ultrasound images were then analyzed automatically with quantusFLM. Features of the software used by quantusFLM have been described in detail elsewhere [22]. The software contains algorithms that analyze the textural patterns of the delineated area in the ultrasound image.…”

Section: Methodsmentioning

confidence: 99%

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

Palacio¹,

Bonet‐Carne²,

Cobo³

et al. 2017

American Journal of Obstetrics and Gynecology

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“…Recently, an algorithm which specifically predicts respiratory morbidity by quantitative texture analysis of ultrasound images of fetal lungs (quantusFLM) has been developed [8]. The quantusFLM software has been reported to predict neonatal respiratory morbidity with an accuracy similar to that previously reported for other tests, with the advantage of being a noninvasive technique [9].…”

Section: Introductionmentioning

confidence: 79%

Evaluation of Fetal Lung Maturity by Quantitative Analysis (quantusFLM) in Women with Gestational Diabetes Mellitus

Sánchez

Mateo

et al. 2018

Fetal Diagn Ther

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Objectives: The aim of the present study was to evaluate fetal lung maturity using the noninvasive method of quantitative ultrasound analysis of fetal lung texture (quantusFLM) in women with gestational diabetes mellitus (GDM). Methods: A total of 96 women at 36–38 weeks of gestation were enrolled. They were classified as follows: 33 GDM cases treated with diet, 30 GDM cases treated with diet plus insulin, and 33 normoglycemic women (control group). A quantitative analysis of lung texture was performed. Results: There were significant differences in the lung maturity results among groups (p = 0.004). These differences were established between the insulin-treated group of patients and both the control (p = 0.006) and diet-only (p = 0.003) groups. While none of the women in the control group or in the diet group had a high risk of immaturity, 16.7% of those treated with insulin (5/30) did (p = 0.003). There was no statistically significant correlation between HbA1c and the result of the test. Conclusions: Quantitative ultrasound study of fetal lung texture suggests that a significant percentage of pregnant women with GDM treated with insulin had fetal lung immaturity in the late preterm to early term.

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“…Firstly, the robustness in terms of using different scanner equipment and frequency probes was assumed. Although the robustness of these feature extractions and statistical learning procedures has already been proven in previous studies [16] , further research with this specific setting is needed. Secondly, the ROI used in this study was selected because it is an easy delineation, as the anterior lip is always identifiable.…”

Section: Discussionmentioning

confidence: 99%

“…Texture analysis by ultrasound or magnetic resonance has also been investigated in the field of foetal and perinatal medicine [13,14] . Recently, quantitative texture analysis of foetal lung ultrasound images has proven to be a predictor of neonatal respiratory morbidity [15][16][17] . The aim of this study was to evaluate the feasibility of quantitative analysis of cervical ultrasound images to evaluate cervical tissue changes throughout pregnancy.…”

Section: Introductionmentioning

confidence: 99%

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

Baños

Perez‐Moreno²,

Triginer³

et al. 2016

Fetal Diagn Ther

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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.

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

Abstract: Objective

Cited by 66 publications

References 46 publications

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

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

Evaluation of Fetal Lung Maturity by Quantitative Analysis (quantusFLM) in Women with Gestational Diabetes Mellitus

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

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