Browsing by Subject "Digital pathology"

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    Colecciones de casos: una valiosa herramienta docente en Anatomía Patológica
    (Universidad de Murcia. Servicio de publicaciones, 2024) Fernández Pérez, Juan; Sánchez Gutiérrez, David
    La especialidad de Anatomía Patológica (AP) se basa en el estudio histológico demuestras de tejidos para obtener diagnósticos clínico-patológicos. La revisión de las láminas demicroscopía generadas en la actividad asistencial puede ser una herramienta de interés docente.Estas “colecciones de casos”, que podemos definir como “conjuntos cerrados de casos seleccionados por su interés docente”, permiten al residente o estudiante de AP familiarizarse con las características histológicas de los tejidos normales, de las distintas entidades patológicas y otros hallazgos histológicos. En esta revisión narrativa describimos las características de estas colecciones de casos, su tipología, formas de uso, ventajas e inconvenientes; además, describimos el impacto dela digitalización de los servicios de Anatomía Patológica en la elaboración de colecciones de casos.
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    Open Access
    Comparison of manual and automated digital image analysis systems for quantification of cellular protein expression
    (Universidad de Murcia, Departamento de Biologia Celular e Histiologia, 2022) Jagomast, T.; Idel, C.; Klapper, L.; Kuppler, P.; Proppe, L.; Beume, S.; Falougy, M.; Steller, D.; Hakim, S.G.; Offermann, A.; Roesch, M.C.; Bruchhage, K.L.; Perner, S.; Ribbat Idel, J.
    Objective. Quantifying protein expression in immunohistochemically stained histological slides is an important tool for oncologic research. The use of computer-aided evaluation of IHC-stained slides significantly contributes to objectify measurements. Manual digital image analysis (mDIA) requires a userdependent annotation of the region of interest (ROI). Others have built-in machine learning algorithms with automated digital image analysis (aDIA) and can detect the ROIs automatically. We aimed to investigate the agreement between the results obtained by aDIA and those derived from mDIA systems. Methods. We quantified chromogenic intensity (CI) and calculated the positive index (PI) in cohorts of tissue microarrays (TMA) using mDIA and aDIA. To consider the different distributions of staining within cellular subcompartments and different tumor architecture our study encompassed nuclear and cytoplasmatic stainings in adenocarcinomas and squamous cell carcinomas. Results. Within all cohorts, we were able to show a high correlation between mDIA and aDIA for the CI (p<0.001) along with high agreement for the PI. Moreover, we were able to show that the cell detections of the programs were comparable as well and both proved to be reliable when compared to manual counting. Conclusion. mDIA and aDIA show a high correlation in acquired IHC data. Both proved to be suitable to stratify patients for evaluation with clinical data. As both produce the same level of information, aDIA might be preferable as it is time-saving, can easily be reproduced, and enables regular and efficient output in large studies in a reasonable time period.