Dataset Information

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

ABSTRACT: 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.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.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.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.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.

SUBMITTER: Palacio M

PROVIDER: S-EPMC5625293 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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

Palacio Montse M Bonet-Carne Elisenda E Cobo Teresa T Perez-Moreno Alvaro A Sabrià Joan J Richter Jute J Kacerovsky Marian M Jacobsson Bo B García-Posada Raúl A RA Bugatto Fernando F Santisteve Ramon R Vives Àngels À Parra-Cordero Mauro M Hernandez-Andrade Edgar E Bartha José Luis JL Carretero-Lucena Pilar P Tan Kai Lit KL Cruz-Martínez Rogelio R Burke Minke M Vavilala Suseela S Iruretagoyena Igor I Delgado Juan Luis JL Schenone Mauro M Vilanova Josep J Botet Francesc F Yeo George S H GSH Hyett Jon J Deprest Jan J Romero Roberto R Gratacos Eduard E

American journal of obstetrics and gynecology 20170323 2

<h4>Background</h4>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.<h4>Objective</h4>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 ...[more]

PMID: 28342715

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Project description:PurposeWe develop a multi-centric response predictive model using QUS spectral parametric imaging and novel texture-derivate methods for determining tumour responses to neoadjuvant chemotherapy (NAC) prior to therapy initiation.Materials and methodsQUS Spectroscopy provided parametric images of mid-band-fit (MBF), spectral-slope (SS), spectral-intercept (SI), average-scatterer-diameter (ASD), and average-acoustic-concentration (AAC) in 78 patients with locally advanced breast cancer (LABC) undergoing NAC. Ultrasound radiofrequency data were collected from Sunnybrook Health Sciences Center (SHSC), University of Texas MD Anderson Cancer Center (MD-ACC), and St. Michaels Hospital (SMH) using two different systems. Texture analysis was used to quantify heterogeneities of QUS parametric images. Further, a second-pass texture analysis was applied to obtain texture-derivate features. QUS, texture- and texture-derivate parameters were determined from both tumour core and a 5-mm tumour margin and were used in comparison to histopathological analysis for developing a response predictive model to classify responders versus non-responders. Model performance was assessed using leave-one-out cross-validation. Three standard classification algorithms including a linear discriminant analysis (LDA), k-nearest-neighbors (KNN), and support vector machines-radial basis function (SVM-RBF) were evaluated.ResultsA combination of tumour core and margin classification resulted in a peak response prediction performance of 88% sensitivity, 78% specificity, 84% accuracy, 0.86 AUC, 84% PPV, and 83% NPV, achieved using the SVM-RBF classification algorithm. Other parameters and classifiers performed less well running from 66% to 80% accuracy.ConclusionsA QUS-based framework and novel texture-derivative method enabled accurate prediction of responses to NAC. Multi-centric response predictive model provides indications of the robustness of the approach to variations due to different ultrasound systems and acquisition parameters.

Dataset Information

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

Publications

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

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