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

Tenorio, V; Bonet‐Carne, Elisenda; Botet, Francesc; Marqués, Ferran; Amat-Roldán, Ivan; Gratacós, E.

doi:10.7863/jum.2011.30.10.1365

Cited by 19 publications

(15 citation statements)

References 38 publications

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“…TA is a promising technique to achieve the development of biomarkers in fetal medicine. This method has shown the ability to detect brain differences in subclinical conditions such as unapparent white matter damage in preterm neonates [35] or those with minor clinical repercussion [30,33]. …”

Section: Discussionmentioning

confidence: 99%

“…The software used here is fundamentally based on wavelet analysis and conditional random fields and has been previously described in detail elsewhere [35]. This feature extraction tool provides 15,300 values or features as output.…”

Section: Methodsmentioning

confidence: 99%

“…TA-based methods have been demonstrated to differentiate healthy from pathological tissues [30,31,32,33,34]. We have reported our experience using a TA software that was initially applied on ultrasound images showing the ability to predict white matter damage in subclinical stages on preterm neonatal brain scans with a high accuracy [35]. In addition, the software has demonstrated a strong correlation between fetal lung ultrasound texture features and gestational age (GA) [36].…”

Section: Introductionmentioning

confidence: 99%

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Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term

et al. 2013

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Objectives: We tested the hypothesis whether a texture analysis (TA) algorithm applied to MRI brain images identified different patterns in small for gestational age (SGA) fetuses as compared with adequate for gestational age (AGA). Study Design: MRI was performed on 83 SGA and 70 AGA at 37 weeks' GA. Texture features were quantified in the frontal lobe, basal ganglia, mesencephalon, cerebellum and cingulum. A classification algorithm based on discriminative models was used to correlate texture features with clinical diagnosis. Results: Region of interest delineation in all areas was achieved in 61 SGA (12 vasodilated) and 52 AGA; this was the sample for TA feature extraction which allowed classifying SGA from AGA withaccuracies ranging from 90.9 to 98.9% in SGA versus AGA comparison and from 93.6 to 100% in vasodilated SGA versus AGA comparison. Conclusions: This study demonstrates that TA can detect brain differences in SGA fetuses. This supports the existence of brain microstructural changes in SGA fetuses.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term

et al. 2013

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“…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] .…”

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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“…To overcome these difficulties, some quantitative approaches have been described to improve the quality of the evaluation of the intensity of periventricular flares [16,17], but these approaches involve technology that is not widely available.…”

Section: Introductionmentioning

confidence: 99%

Persistent Periventricular Echogenicities in Preterms Are Not Related to Smaller Brains at Term-Equivalent Age

Graça¹,

Cardoso²,

Costa³

et al. 2014

Neonatology

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Background: Periventricular white matter (PWM) is particularly vulnerable in very preterm infants. Non-cystic white matter injury, known as non-cystic periventricular leukomalacia (ncPVL), is the commonest ‘lesion' affecting the preterm brain. There is no consensus about whether ncPVL can be reliably identified from cerebral ultrasound (cUS) or whether there is any reliable correlate of ncPVL on cUS at term-equivalent age (TEA). Objective: To compare brain volumes and linear measures at TEA in infants with and without a diagnosis of ncPVL. Methods: Preterm infants of ≤32 weeks' gestation without major lesions were serially assessed using cUS. ncPVL was defined as PWM echogenicity comparable to the choroid plexus on two scans at least 2 weeks apart after the first postnatal week. At TEA, infants were scanned for the estimation of brain volume and ventricular and tissue dimensions. Head circumference was measured. The data were compared between those with/without ncPVL. Observer agreement was assessed using kappa statistic. Results: Of 63 eligible infants 29% had ncPVL. Significant differences were found between those with/without ncPVL for 5 min Apgar score, CRIB score, invasive ventilation rates and chronic lung disease but not for other relevant clinical data. No significant differences were found for estimated brain volume, ventricular size, corpus callosum length/thickness or central grey matter width. Intra-observer reliability was moderate (kappa = 0.51-0.56); inter-observer reliability was poor (kappa = 0.20-0.32). Conclusions: This study indicates that an ultrasound diagnosis of ncPVL should not be used as a sole predictor of lower brain growth detectable at TEA.

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

Abstract: These data support the notion that semiautomated quantitative ultrasound analysis achieves early identification of changes in subclinical stages and warrant further investigation of the role of ultrasound texture analysis methods to improve early detection of neonatal brain damage.

Cited by 19 publications

References 38 publications

Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term

Fetal Brain MRI Texture Analysis Identifies Different Microstructural Patterns in Adequate and Small for Gestational Age Fetuses at Term

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

Persistent Periventricular Echogenicities in Preterms Are Not Related to Smaller Brains at Term-Equivalent Age

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