Publications by authors named "A Hassankhani"

Background: Thyroid cancer, a common endocrine malignancy, has seen increasing incidence, making lymph node metastasis (LNM) a critical factor for recurrence and survival. Radiomics and deep learning (DL) advancements offer the potential for improved LNM prediction using CT and MRI, though challenges in diagnostic accuracy remain.

Methods: A systematic review and meta-analysis were conducted per established guidelines, with searches across PubMed, Scopus, Web of Science, and Embase up to February 15, 2024.

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Background: Blunt abdominal trauma (BAT) is a significant contributor to pediatric mortality, often causing liver and spleen injuries. Contrast-enhanced computed tomography (CT), the gold standard for diagnosing solid organ injury, poses radiation risks to children. Contrast-enhanced ultrasound (CEUS) may be a promising alternative imaging modality.

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Article Synopsis
  • * A meta-analysis of five studies involving 191 patients found DECT to have a sensitivity of 88.1% and specificity of 82% for diagnosing ACL ruptures, showing strong accuracy, particularly for complete ruptures.
  • * The results indicate that DECT can be a reliable option for diagnosing ACL injuries in acute or subacute settings, supporting its use when MRI is not available or appropriate.
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Article Synopsis
  • - Head and neck cancers rank as the seventh most common worldwide, and lymph node metastasis (LNM) is a key factor that negatively impacts survival; traditional imaging methods struggle with accurate detection of LNM.
  • - This study systematically searched various databases for research on AI models diagnosing LNM in head and neck cancers, finding 23 relevant articles that primarily focused on internal validation due to a lack of external validation.
  • - The analysis showed high diagnostic accuracy for AI models, with pooled AUC scores of 91% for CT-based, 84% for MRI-based, and 92% for PET/CT-based radiomics; further, deep learning models performed similarly well, indicating a need for more extensive multicenter research for
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