Objective To obtain quantitative pathological immunohistochemistry (IHC) data using artificial intelligence (AI) technology to investigate the differential diagnosis and prognostic significance of immunophenotypic expression in mantle cell lymphoma (MCL) and chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL). Methods Retrospective collection of pathological sections from 52 cases diagnosed with MCL and 14 cases of CLL/SLL at our hospital between April 2017 and April 2022, together with the data of patients. The Kaplan-Meier method was used for survival function estimation. Results A diagnostic cut-off value of ≤35.02% for CD23 (AUC=0.904; P<0.0001) and >24.26% for Cyclin D1 (AUC=0.995; P<0.0001) was advocated for MCL. The model consisting of CD20, CD5, CD23, and Cyclin D1 achieved 100% accuracy in distinguishing MCL from CLL/SLL. Within the MCL cohort, the 3-year overall survival (OS) rates were (90.0±9.5) % and (60.2±11.8) % (P=0.027), and the 3-year progression-free survival (PFS）rates were (64.5±19.9) % and (23.6±12.7) % (P=0.026) in the Cyclin D1 low and high expression groups, respectively. SLL patients with high CD5 expression exhibit prolonged OS compared to the low expression group (64.7 months vs. 48.0 months, P=0.048). Conclusion The application of AI in IHC enhances the precision and efficiency of MCL and CLL/SLL identification. The identified risk factors hold promise for guiding improved treatment and prognosis for these conditions.

Disclosures

No relevant conflicts of interest to declare.

This content is only available as a PDF.
2024
Sign in via your Institution