Chirle de Oliveira Raphaelli, Mario Renato de Azevedo Júnior, Helen Gonçalves, Pedro Curi Hallal
Cross-sectional study on work and health risk behaviors among school students in a rural area of Rio Grande do Sul State, Brazil, 2010
Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil
evaluate intracranial structures brain parenchyma radiomics surrounding occipital horn lateral ventricle normal fetuses nfs fetuses ventriculomegaly fvs well predict postnatally enlarged lateral ventricle alterations fvs january 2014 august 2023 141 nfs 101 fvs underwent 15 balanced steadystate free precession bssfp including 68 fvs resolved lateral ventricles fvmresolved 33 fvs stable lateral ventricles fvmstable demographic data intracranial structures analyzed predict enlarged ventricle alterations fvs postnatally logistic regression models 5fold crossvalidation developed based lateral ventricle morphology blendedcortical orand subcortical radiomics characteristics validation models performance conducted using receiver operating characteristic curve roc calibration curve decision curve analysis dca significant alterations cerebral structures observed nfs fvs p 005 excluding maximum frontal horn diameter fd however notable distinction fvmresolved fvmstable groups p 005 based subcorticalradiomics aberrant sides fvs approach exhibited high efficacy distinguishing nfs fvs trainingvalidation set yielding impressive auc 10992 auc value 08220743 trainingvalidation set subcorticalradiomics model demonstrated ability predict lateral ventricle alterations fvs greatest predictive advantages indicated dca microstructural alterations subcortical parenchyma associated ventriculomegaly serve predictive indicators postnatal lateral ventricle variations fvs critical gain pertinent information solitary fetal mri anticipate postnatal lateral ventricle alterations fetuses ventriculomegaly approach holds potential diminish necessity recurrent prenatal ultrasound mri examinations fetal ventriculomegaly dynamic condition affects postnatal neurodevelopment machine learning subcorticalradiomics predict postnatal alterations lateral ventricle machine learning applied singlefetal mri might reduce required antenatal testing