Fetal diagnosis and therapy · 2013
Fetal brain MRI texture analysis identifies different microstructural patterns in adequate and small for gestational age fetuses at term.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Sanz-Cortés M, Figueras F, Bonet-Carne E, Padilla N, Tenorio V, Bargalló N, Amat-Roldan I, Gratacós Eduard
Аннотация
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 with accuracies 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.
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 with accuracies 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.
Предметные рубрики MeSH
BrainChemical PhenomenaCohort StudiesDevelopmental DisabilitiesFeasibility StudiesFemaleFetal Growth RetardationFollow-Up StudiesHumansInfant, NewbornInfant, Small for Gestational AgeMagnetic Resonance ImagingMalePlacental InsufficiencyPregnancyPregnancy Trimester, ThirdPrenatal DiagnosisProspective StudiesTerm BirthVasodilation