[The use of intralaboratory and external quality control of immunohistochemical studies to improve the detection of estrogen and progesterone receptors]. / Primenenie vnutrilaboratornogo i vneshnego kontrolya kachestva immunogistokhimicheskikh issledovanii dlya uluchsheniya vyyavleniya retseptorov estrogenov i progesteronov.

Immunohistochemical study of breast cancer has been practiced for several decades; however, the standardization of this process has not yet been achieved, despite substantial advances in methodology. The paper presents practical guidelines and standards for testing breast carcinomas, which can be used as day-to-day control of the work of an immunohistochemistry laboratory. It considers the concept of external quality control in the immunohistochemical detection of estrogen (ER) and progesterone (PR) receptors, as well as the problems of harmonization in the immunohistochemical analysis of breast carcinomas, by using the nuclear biomarkers, such as ER and PR, as an example. The agreed standard-based external control in determining the optimal result may yield reproducible data for use in clinical practice.

[Assessment of PD-L1 expression using the neural network analysis algorithm in non-small cell lung carcinoma biopsy specimens]. / Otsenka v bioptatakh nemelkokletochnykh kartsinom legkogo ekspressii PD-L1 s primeneniem algoritma neirosetevogo analiza.

Neural network analysis of digital copies of histological micropreparations is one of the methods used to standardize quantitative continuous data. PD-L1 (22C3) biomarker expression in metastatic non-small cell lung carcinomas without mutations in the EGFR, ALK, and ROS1 genes serves as an indication for the use of pembrolizumab for the first-line therapy. OBJECTIVE: To quantify PD-L1 biomarker expression in non-small cell lung carcinomas using the neural network analysis of digital copies of histological micropreparations. MATERIAL AND METHODS: Immunohistochemical study of PD-L1 (22C3) expression was performed on 96 non-small cell lung carcinoma biopsy specimens. The digital copies of histological micropreparations were processed by the QuPath software neural network analysis module. RESULTS: The neural network analysis module segmented tumor, stroma, and artifacts in the micropreparations, showing a sufficient level of agreement with a visual assessment. Digital image analysis quantified stained tumor cells in the high PD-L1 expression group and showed 96% agreement rate versus visual assessment. However, the group of tumors without PD-L1 expression versus visual assessment showed a low (58%) agreement rate. CONCLUSION: The neural network analysis algorithm is applicable to the study of digital copies of histological micropreparations containing tumor, stroma, and artifacts. The algorithm allows for quantitative immunohistochemical assessment of PD-L1 expression in tumor cells. The algorithm can quantify the immunohistochemically detected expression of PD-L1 in tumor cells.

[Comparison of digital and visual methods for Ki-67 assessment in invasive breast carcinomas]. / Sravnenie tsifrovogo i vizual'nogo metodov dlia otsenki indeksa Ki-67 v invazivnykh kartsinomakh molochnoi zhelezy.

AIM: to compare two methods for quantitative assessment of the proliferative activity index (PAI): a visual estimation method by several investigators and digital image analysis (DIA). The use of the Ki-67 index in the daily clinical practice of a Morbid Anatomy Department is associated with the problem of reproducibility of quantitative assessment of the Ki-67 PAI. Due to the development of digital imaging techniques in morphology, new methods for PAI evaluation using the DIA are proposed. MATERIAL AND METHODS: The Ki-67 PAI data obtained during visual assessment and digital image analysis were compared in 104 cases of grades 2-3 breast carcinoma. The histological sections were scanned using a Panoramic III scanner (3D Histech, Hungary) and digital images were obtained. DIA was carried out using the software 3D Histech QuantCenter (3D Histech, Hungary), by marking 3-10 zones. Evaluation of the obtained sections was done independently by two investigators engaged in cancer pathology. RESULTS: The level of agreement between visual and digital methods did not differ significantly (p>0.001). The authors selected a gray area in the range of 10-35% IPA, where the Ki-67 index showed a weak relationship between the analyzed groups (ICC, 0.47). The Ki67 index below 10% and above 35% showed a sufficient reproducibility in the same laboratory. CONCLUSION: The authors consider that the scanned digital form of a histological section, which can be evaluated using automated software analysis modules, is an independent and objective method to assess proliferative activity for Ki-67 index validation.