Abstract
Presently, pathologists need to stain biopsy samples with standard and antibody-based immunocytochemistry (ICC) reagents for final diagnosis. Antibody reagents take hours to days to perform staining, along with requiring specialized equipment and technical skills. We have developed an AI-based virtual ICC platform that measures individual cell morphological features in whole slide images and labels the cells as immuno-positive or negative. The platform runs on the cloud in minutes, saving pathologists significant time and cost. For this purpose, cytopathology slides were obtained from N = 100 suspected cases of canine T-cell and B-cell lymph node lymphomas through Fine Needle Aspiration (FNA). Cytopathology slides were initially stained with the standard Wright-Giemsa (WG) and then re-stained with ICC reagents, anti-CD3 or anti-PAX5 antibodies, resulting in a pair of stained slides (WG-CD3 or WG-PAX5). Prior to AI training, cytopathology slides were digitally scanned, and the resulting images underwent a comprehensive pre-processing protocol to separate stains of interest for nuclei segmentation in WG and CD3 or PAX5. Following nuclei segmentation, the cell features from processed image pairs were translated into a structured tabular features format with immuno-positive and negative labeled classes. In total, the geometrical features of 8.48 million segmented cells (4.24 million pairs) were translated into a tabular format and paired based on the Euclidean cell matching algorithm. This approach facilitated the prediction of cell labels, achieving sensitivity and specificity of 0.98 and 0.97 (0.94 and 0.99), respectively for CD3 (PAX5). Additionally, the AI-based virtual ICC has demonstrated capabilities in cell counting, cell spatial distribution, cell segmentation, and classification. It offers a rapid, accurate, and precise evaluation of FNA samples and has the potential to help advance diagnostic cellular and molecular pathology capabilities.