Publications by authors named "A Amirabadi"
Background: Few studies have evaluated predictive factors of isolated pituitary stalk thickening (iPST) in children.
Methods: In this retrospective study, radiology, endocrinology, and neuro-oncology databases were interrogated to identify patients with iPST between January 2000 and June 2019. A blinded, longitudinal assessment of MRIs was performed using quantitative, semi-quantitative, and qualitative metrics.
Article Synopsis
- A study was conducted to assess the effectiveness of a specific MRI technique (delayed 3D IR FLASH) in evaluating the lower airways of children during routine cardiovascular magnetic resonance imaging.
- The study included 180 pediatric patients, with a high image quality rating and visibility of lower airways; around 21% of patients showed lower airway abnormalities, particularly among those with congenital heart disease.
- The results suggest that this MRI method is highly reliable and effective for viewing lower airway anatomy, enhancing the potential for identifying issues in children with heart conditions.
Background: Neuroangiography represents a critical diagnostic and therapeutic imaging modality whose associated radiation may be of concern in children. The availability of in vivo radiation damage markers would represent a key advancement for understanding radiation effects and aid in the development of radioprotective strategies.
Objective: Determine if biomarkers of cellular damage can be detected in the peripheral blood mononuclear cells (PBMC) of children undergoing neuroangiography.
Background: Trastuzumab is a humanized monoclonal antibody against the human epidermal growth factor receptor 2 (HER2). This post-marketing surveillance evaluates the safety of a trastuzumab biosimilar (AryoTrust), produced by AryoGen Co. Iran in Iranian women with HER2-positive non-metastatic breast cancer (BC).
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- Scoliosis is a spinal deformity that affects functionality and appearance, and its severity is typically assessed using the Cobb angle, which can be inaccurately measured by humans.
- The study developed a machine learning model to automatically calculate Cobb angles from standing spine X-rays of children and adolescents with suspected scoliosis, comparing it with manual measurements from radiologists.
- The model demonstrated promising results with a Symmetric Mean Absolute Percentage Error (SMAPE) of around 11.8%, indicating its potential for clinical application, though further research is needed for validation before it can be widely implemented.