ArticleJournal of autism and developmental disorders2013
Predictive value of morphological features in patients with autism versus normal controls.
Article in Journal of autism and developmental disorders, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Actionable and incidental neuroradiological findings in twins with neurodevelopmental disorders.Scientific reports · 2020Trial
- Clinical and genetic findings in autism spectrum disorders analyzed using exome sequencing.Frontiers in psychiatry · 2025Article
- The Role of Structure MRI in Diagnosing Autism.Diagnostics (Basel, Switzerland) · 2022Article
- Application of pseudocontinuous arterial spin labeling perfusion imaging in children with autism spectrum disorders.Frontiers in neuroscience · 2022Article
- Deep Learning Approach for Screening Autism Spectrum Disorder in Children with Facial Images and Analysis of Ethnoracial Factors in Model Development and Application.Brain sciences · 2021Article
- Brief Report: Facial Asymmetry and Autistic-Like Traits in the General Population.Journal of autism and developmental disorders · 2021Article
- Identification of Essential, Equivocal and Complex Autism by the Autism Dysmorphology Measure: An Observational Study.Journal of autism and developmental disorders · 2021Observational
- Heritable genotype contrast mining reveals novel gene associations specific to autism subgroups.Journal of biomedical informatics · 2018Article
- Reduced Hemispheric Asymmetry of White Matter Microstructure in Autism Spectrum Disorder.Journal of the American Academy of Child and Adolescent Psychiatry · 2016Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is characteristic for the difference between autistic children and typically developing controls. The present findings showed that morphological features are significantly increased in patients with autism. Using ROC and RP, some of the morphological measures also led to strong predictive accuracy. Facial asymmetry, multiple hair whorls and prominent forehead significantly differentiated patients with autism from controls. Future research on multivariable risk prediction models may benefit from the use of morphological features.
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