Histopathology最新文献

Friedrich Wegener was the author of a comprehensive description of a disease that was named Wegener's granulomatosis. In 2010, its name was changed to granulomatosis with polyangiitis (GPA), the reason being that Wegener's links to Nazism were revealed. The research was conducted between May 2021 and August 2023 and was based on the historical method (understood as defining the subject of research, critical analysis of collected historical source materials, and historiographical synthesis) using the methods of direct and indirect inference ascribed to the historical method. Wegener and his wife were members of numerous Nazi organizations. He was rewarded for his stance by the German Reich. Together with his family, he lived in very comfortable conditions for the standards of the time, unattainable for the average person. He described himself as a frontline combatant in the Nazi invasion of Poland. Friedrich Wegener's links with Nazism were more extensive than has hitherto been known. The links involved his professional and private life. They testify to his membership in numerous Nazi organizations, as well as his active involvement in the activities of various institutions and communities of the German Reich and his overt expression of support for Hitler. Wegener's numerous accolades from the German Reich providing him with professional, material and personal benefits were proof of his links with the Nazi movement. Wegener's association with Nazism helped him to advance his career, and to attain a high social status and quality of life, both before the outbreak and during the Second World War.

Aim: Manual detection and scoring of Ki67 hotspots is difficult and prone to variability, limiting its clinical utility. Automated hotspot detection and scoring by digital image analysis (DIA) could improve the assessment of the Ki67 hotspot proliferation index (PI). This study compared the clinical performance of Ki67 hotspot detection and scoring DIA algorithms based on virtual dual staining (VDS) and deep learning (DL) with manual Ki67 hotspot PI assessment.

Methods: Tissue sections of 135 consecutive invasive breast carcinomas were immunohistochemically stained for Ki67. Two DIA algorithms, based on VDS and DL, automatically determined the Ki67 hotspot PI. For manual assessment; two independent observers detected hotspots and calculated scores using a validated scoring protocol.

Results: Automated hotspot detection and assessment by VDS and DL could be performed in 73% and 100% of the cases, respectively. Automated hotspot detection by VDS and DL led to higher Ki67 hotspot PIs (mean 39.6% and 38.3%, respectively) compared to manual consensus Ki67 PIs (mean 28.8%). Comparing manual consensus Ki67 PIs with VDS Ki67 PIs revealed substantial correlation (r = 0.90), while manual consensus versus DL Ki67 PIs demonstrated high correlation (r = 0.95).

Conclusion: Automated Ki67 hotspot detection and analysis correlated strongly with manual Ki67 assessment and provided higher PIs compared to manual assessment. The DL-based algorithm outperformed the VDS-based algorithm in clinical applicability, because it did not depend on virtual alignment of slides and correlated stronger with manual scores. Use of a DL-based algorithm may allow clearer Ki67 PI cutoff values, thereby improving the clinical usability of Ki67.