Abstract
Adenomyosis and leiomyomas are common benign uterine disorders characterized by abnormal cellular proliferation. The BCL6 protein, a transcriptional repressor implicated in cell proliferation and oncogenesis, has been linked to the pathogenesis of endometriosis. This study investigates BCL6 expression in adenomyosis, leiomyomas, and normal myometrium using immunohistochemistry and deep learning neural networks. We analyzed paraffin blocks from total hysterectomies performed between 2009 and 2017, confirming diagnoses through pathological review. Immunohistochemistry was conducted using an automated system, and BCL6 expression was quantified using Fiji-ImageJ software. A supervised deep learning neural network was employed to classify samples based on DAB staining. Our results show that BCL6 expression is significantly higher in leiomyomas compared to adenomyosis and normal myometrium. No significant difference in BCL6 expression was observed between adenomyosis and controls. The deep learning neural network accurately classified samples with a high degree of precision, supporting the immunohistochemical findings. These findings suggest that BCL6 plays a role in the pathogenesis of leiomyomas, potentially contributing to abnormal smooth muscle cell proliferation. The study highlights the utility of automated immunohistochemistry and deep learning techniques in quantifying protein expression and classifying uterine pathologies. Future studies should investigate the expression of BCL6 in adenomyosis and endometriosis to further elucidate its role in uterine disorders.