The combination of CD138/MUM1 dual-staining and artificial intelligence for plasma cell counting in the diagnosis of chronic endometritis

Abstract

Objective: To investigate the utility of combination of CD138/MUM1 dual-staining (DS) and artificial intelligence (AI) for plasma cell (PC) counting in the diagnosis of chronic endometritis (CE).

Methods: Two hundred ninety-eight infertile women underwent endometrial biopsy were included. In 100 women, three successive sections were cut from each paraffin-embedded tissue block for CD138 immunohistochemical (IHC) single-staining (SS), MUM1 SS and CD138/MUM1 DS. The prevalence of CE and the sensitivity/specificity in the diagnosis of CE with different methods was studied. These sections diagnosed as CE with DS were collected to train artificial intelligence (AI) diagnostic system. In other 198 women, their tissue sections stained with CD138/MUM1 DS were used to test the AI system in the diagnosis of CE.

Results: CD138/MUM1 DS revealed that the cell membranes and nuclei of PCs were simultaneously labelled by CD138 and MUM1, respectively. The positive rate of ECs identified by CD138/MUM1 DS (38%, 38/100) was lower than CD138 SS (52%, 52/100) and MUM1 SS (62%, 62/100) (p < .05). The sensitivity, specificity and accuracy of CD138/MUM1 DS in the diagnosis of ECs reached 100%. The sensitivity, specificity and accuracy rates of AI diagnostic system of ECs were 100%, 83.3% and 91.4%, respectively. The 17 cases over-diagnosed as EC with the AI were corrected quickly by pathologists reviewing these false PC pictures listed by the AI.

Conclusion: The combination of CD138/MUM1 DS and AI is a promising method to improve the accuracy and efficiency of CE diagnosis.

Keywords: artificial intelligence; chronic endometritis; dual-staining; immunohistochemistry; plasma cell; single-staining.