An <scp>AI</scp> ‐based <scp>IHC</scp> quantification technique for assisting in the differentiation of <scp>MCL</scp> from <scp>CLL</scp> / <scp>SLL</scp>

This study explores the application of artificial intelligence technology for the quantitative analysis of immunohistochemical markers to differentiate between mantle cell lymphoma and chronic lymphocytic leukaemia/small lymphocytic lymphoma. Utilizing an AI-based platform, the research analysed the expression of CD3, CD5, CD10, CD20, CD23, Cyclin D1, BCL-2, BCL-6 and Ki-67 in 91 samples from 84 patients. The findings demonstrate that, compared to manual interpretation, the AI system provides more objective, reproducible and accurate measurement results. Additionally, this study introduces a virtual dual immunohistochemical labelling technique for simultaneous antigen visualization. Although limited by its single-centre retrospective design, the research establishes a promising AI-assisted framework that enhances the accuracy, standardization and diagnostic efficiency in distinguishing between these two clinically distinct lymphomas.