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Background: Extramedullary hematopoiesis (EMH) is usually seen in the reticuloendothelial system such as the spleen and liver; however, there have been rare case reports when EMH is seen in serous fluids (SFs). The aim of this study included analyzing the cytomorphological features of EMH in SFs in correlation with various clinicopathologic parameters and recognizing potential diagnostic pitfalls as well as their prognostic significance.

Methods: Clinicopathologic parameters and radiologic and pathologic information from the patients with a cytologic diagnosis of EMH were evaluated with cytology slides.

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Clear cell tumors of parotid gland encompass a wide spectrum of neoplasms, including benign and malignant epithelial neoplasms. Additionally, tumors from adjacent structures such as paraganglioma, and metastatic neoplasms may also show clear cells. Overlapping cytological features may cause difficulty in diagnosis.

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Article Synopsis
  • - The study focuses on the need to identify various causes of respiratory failure in myasthenia gravis (MG) patients, considering both myasthenic and non-myasthenic factors.
  • - Three case studies are presented, highlighting different respiratory failure presentations: hypercarbic respiratory failure in a long-term MG patient, glottal stenosis in another MG patient, and hypoxia due to a patent foramen ovale in an elderly MG patient.
  • - The findings stress the importance of thorough clinical evaluation, as symptoms of respiratory distress may not always be apparent in MG, and alternative causes of hypoxia should be investigated.
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Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Phys Med

November 2024

Medical Physics Department, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Via F. Gallini 2, 33081 Aviano, Italy.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).

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Article Synopsis
  • Neuroendocrine tumors of the lung can be identified through specific cytomorphologic features, but distinguishing between subtypes can be challenging due to differences in cytologic specimens.
  • A study comparing bronchial, effusion fluid, and fine-needle aspiration specimens reviewed 46 cases, highlighting key features of small cell carcinomas, carcinoids, and neuroendocrine tumors.
  • Findings indicated that small cell carcinoma typically lacks prominent nucleoli and displays certain nuclear features, while effusion fluid specimens show fewer artifacts and greater nuclear atypia, emphasizing the need to consider specimen type in diagnostic evaluation.
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