Background: There are numerous methods and procedures described for the preparation of cell blocks (CBs) from cytological samples. The objective of this study was to determine current practices and issues with CBs in European laboratories.
Methods: A link to an online survey, with 11 questions about CB practices, was distributed to cytology laboratories via participants of United Kingdom National External Quality Assurance Service for Cellular Pathology Techniques and national representatives in the European Federation of Cytology Societies.
Results: A total of 402 laboratories responded completely (337/402, 84%) or partially (65/402, 16%) to the survey by February 4, 2022. The most common CB practice is embedding cell pellets using plasma and thrombin (23.3%), agar (17.1%), Shandon/Epredia Cytoblock (11.4%), HistoGel (7.9%), and Cellient (3.5%). Other methods such as CytoFoam, albumin, gelatin, Cytomatrix, and collodion bags are rarely used (1.0%, 0.7%, 0.7%, 0.3%, and 0.2%, respectively). CBs are also prepared from naturally occurring clots or tissue fragments (29.5%) and cells scraped from unstained or prestained smears (4.4%). The most frequent issues with the CBs in a daily cytology practice are low cellularity (248/402, 62%) and dispersed cells (89/402, 22%), regardless of the CBs preparation method or how the samples for embedding were selected.
Conclusions: There is a great variability in CB practices in European laboratories with low cellular CBs as the main issue. Additional studies are mandatory to evaluate and improve performance and cellular yield of CBs.
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http://dx.doi.org/10.1002/cncy.22793 | DOI Listing |
Gastric Cancer
January 2025
Department of Medical Oncology, Hospital Clinico Universitario, INCLIVA, Biomedical Research Institute, University of Valencia, Avenida Menendez Pelayo nro 4 accesorio, Valencia, Spain.
Introduction: Gastric cancer (GC) burden is currently evolving with regional differences associated with complex behavioural, environmental, and genetic risk factors. The LEGACy study is a Horizon 2020-funded multi-institutional research project conducted prospectively to provide comprehensive data on the tumour biological characteristics of gastroesophageal cancer from European and LATAM countries.
Material And Methods: Treatment-naïve advanced gastroesophageal adenocarcinoma patients were prospectively recruited in seven European and LATAM countries.
BMC Prim Care
January 2025
Department of Family Medicine, Jagiellonian University Medical College, Kraków, 31-061, Poland.
Background: The COVID-19 pandemic has caused psychological distress to the population and healthcare workers. Physicians' well-being is essential and contributes significantly to overall health. This study aimed to assess the strain on Polish general practitioners from the effects of the COVID-19 pandemic and to ascertain the potential predictors of their distress.
View Article and Find Full Text PDFESMO Open
January 2025
Division of Oncology, Department of Medicine I, Medical University Vienna, Vienna, Austria. Electronic address:
Background: Ethnic diversity in cancer clinical trials is essential to ensure that therapeutic advances are equitable and broadly applicable in multicultural societies. Yet, missing consensus on the documentation of ethnic origin, partially based on the complexity of the terminology and fear of discrimination, leads to suboptimal patient management of minority populations. Additionally, eligibility criteria, such as stringent laboratory cut-offs, often fail to account for variations across ethnic groups, potentially excluding patients without evidence-based justification.
View Article and Find Full Text PDFESMO Open
January 2025
UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, USA.
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View Article and Find Full Text PDFRadiography (Lond)
January 2025
Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health, Berlin, Germany.
Background: Facial recognition technology in medical imaging, particularly with head scans, poses privacy risks due to identifiable facial features. This study evaluates the use of facial recognition software in identifying facial features from head CT scans and explores a defacing pipeline using TotalSegmentator to reduce re-identification risks while preserving data integrity for research.
Methods: 1404 high-quality renderings from the UCLH EIT Stroke dataset, both with and without defacing were analysed.
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