Nucleolus assessment in invasive breast carcinoma, an objective parameter for histological grading.

Background: The nucleolus has the potential to provide insight into how many types of cancer will progress. In this study, we examined the evaluation of the nucleolus with a microscope in widespread breast cancer tumors and whether this value contributes to tumor grading as an objective clinicopathological parameter.

Methods: In our study, the nucleolus was evaluated retrospectively in resections with a diagnosis of invasive breast carcinoma of the cases between January 2010 and April 2021. In total, the tumor nucleolus of 377 cases of invasive breast carcinoma was evaluated. Nucleolus evaluation was performed with light microscopy using four different modes (modified Helpap method, in 1, 5, and 10 high power fields at 40x magnification). The relationship between nucleolar scores and clinicopathological parameters was examined separately. Regrading was performed by replacing nuclear pleomorphism with the nucleolar score in the classically used histological grading system and utilizing the nucleolus score as the fourth parameter in this grading system.

Results: There was no significant correlation between the prognosis of the patients and the nucleolar score. When nuclear pleomorphism and nucleolar score were replaced in the classical grading system, disease-free and overall survival were correlated with the new grading system. In addition, a relationship was found between high nucleolus score and other clinicopathological parameters (such as estrogen receptor negativity, progesterone receptor negativity, high Ki-67, triple negative, and human epidermal growth factor receptor-2 status).

Discussion: The presence of nucleolus is associated with disease-free survival and overall survival of patients, and it can be evaluated with a light microscope at no extra cost and time. Therefore, in the classical grading, using it instead of nuclear pleomorphism with low reproducibility among pathologists may provide more objective results in predicting patient prognosis.