Cardiac evaluation of patients with juvenile dermatomyositis.

BACKGROUND: The present study aims to evaluate possible cardiac involvement in juvenile dermatomyositis (JDM) patients by conventional methods and cardiac magnetic resonance imaging (MRI) along with a systematic review of the literature on cardiac features in JDM. METHODS: The study group consisted of JDM patients who underwent cardiac MRI. We conducted a systematic review of the published literature involving JDM patients with cardiac involvement. RESULTS: In the present study, although baseline cardiologic evaluations including electrocardiography and echocardiography were within normal limits, we showed late gadolinium enhancement on cardiac MRI in 3 of 11 JDM patients. In the literature review, we identified 25 articles related to cardiac involvement in JDM. However, none of them, except one case report, included cardiac MRI of JDM patients. CONCLUSION: Cardiac abnormalities have been reported among the less frequent findings in patients with JDM. Cardiovascular complications during the long-term disease course are a leading cause of morbidity and mortality in these patients. Early detection of cardiac involvement by cardiac MRI in patients with JDM and aggressive treatment of them may improve the clinical course of these patients. IMPACT: The myocardium in patients with JDM may be involved by inflammation. Myocardial involvement may be evaluated by using contrast-enhanced cardiac MRI. This is the first study evaluating cardiac involvement by cardiac MRI in JDM patients. MRI may show early cardiac involvement in patients whose baseline cardiologic evaluations are within normal limits. Early detection of cardiac involvement by cardiac MRI may improve the long-term prognosis of patients with JDM.

Compound Heterozygous ROBO3 Mutation in Two Siblings Presenting with Horizontal Gaze Palsy without Scoliosis: Case-Based Review.

Horizontal gaze palsy with progressive scoliosis (HGPPS) is a rare, autosomal recessively inherited disorder characterized by a congenital absence of conjugated horizontal eye movements with progressive scoliosis developing in childhood and adolescence. HGPPS is caused by mutations of the ROBO3 gene that disrupts the midline crossing of the descending corticospinal and ascending lemniscal sensory tracts in the medulla. We present two siblings, 5-year-old and 2-year-old boys with HGPPS, from non-consanguineous parents. The older brother was brought for the evaluation of moderate psychomotor retardation. He had bilateral horizontal gaze palsy with preserved vertical gaze and convergence. Scoliosis was absent. Cranial MRI showed brainstem abnormalities, and diffusion tensor imaging showed absent decussation of cortico-spinal tracts in the medulla. Clinical diagnosis of HGPPS was confirmed by sequencing of ROBO3 gene, IVS4-1G > A (c.767-1G > A) and c.328_329delinsCCC (p.Asp110Profs*57) compound heterozygous variations were found, and segregated in parents. The younger boy was first reported at 16 months of age and had the same clinical and neuroradiological findings, unlike mild psychomotor retardation. ROBO3 gene analysis showed the same variants in his brother. Our cases show the importance of evaluating eye movements in children with neurodevelopmental abnormalities and looking for brainstem abnormalities in children with bilateral horizontal gaze palsy.

Evaluation of mental performance and cognitive functions of children and adolescents diagnosed with radiologically isolated syndrome.

BACKGROUND: Radiologically isolated syndrome (RIS) is a condition characterized by asymptomatic, incidentally detected demyelinating plaques in the CNS in a patient without typical clinical findings of multiple sclerosis (MS). This study aimed to compare the mental status and cognitive functions of child and adolescent RIS cases with healthy controls and to investigate the relationship between psychometric test results and the demyelinating lesion characteristics. METHODS: The mental status and cognitive functions of 12 RIS cases and 12 healthy controls were compared. Semi-structured interviews, behavioral evaluations, depression and anxiety scales, neuropsychological test battery, and an intelligence test were applied for the evaluation of mental state and cognitive functions. These results were compared with the number and localization of demyelinating lesions. RESULTS: Sustained attention, visual-motor coordination, short-term memory skills, and ability to use visual-spatial information were found worse in the RIS group. There was no correlation between mental state and cognitive functions, and the number and localization of demyelinating lesions. CONCLUSION: Our study showed that pediatric RIS cases may have worse cognitive performance than healthy controls, but no correlation was found between the number and location of demyelinating lesions and psychiatric findings. Although it is controversial whether psychiatric disorders and cognitive disabilities have predictive value in terms of MS conversion in pediatric RIS cases, these subjects were not included in the scope of this study.

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts.

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.