Ki67 and Lymphovascular Invasion as Histopathological Predictors of Residual Cancer Burden After Neoadjuvant...

Background: Neoadjuvant chemotherapy (NAC) is widely used in the management of stage I-III breast cancer, with tumor regression serving as an important surrogate for long-term outcome. Identifying reliable pathological biomarkers predictive of residual disease remains clinically relevant. Methods: We conducted a retrospective cohort study of 165 patients with non-metastatic breast cancer treated with neoadjuvant chemotherapy followed by surgery between 2019 and 2022. Pathological response was assessed using the Residual Cancer Burden (RCB) index. The primary study endpoint was extensive residual disease (RCB-III), defined as the poorest category of tumor regression, indicating treatment resistance. Associations between the Nottingham Score together with other histopathological parameters, immunohistochemical markers (ER, PR, HER2), Ki67 proliferation index, and RCB were analyzed using univariate and multivariable logistic regression. Results: In univariate analysis, higher Nottingham scores (OR = 1.807, p = 0.0017), negative ER expression (OR = 3.017, p = 0.0255), the absence of lymphovascular invasion (OR = 0.1877, p = 0.0069) and elevated Ki67 (OR = 1.034, p = 0.0003) were significantly associated with RCB III. In multivariable analysis, only Ki67 and lymphovascular invasion remained independent predictors of RCB III, while Nottingham score and ER expression lost statistical significance. Correlation analysis demonstrated strong associations between Nottingham score, Ki67, hormone receptor loss, and tumoral necrosis. Conclusions: Ki67 is an independent predictor of poor tumor regression following neoadjuvant chemotherapy and appears to capture much of the prognostic information traditionally attributed to histologic grade and Nottingham score. However, the absence of lymphovascular invasion remains a significant positive prognostic factor. These observations support further investigation into the integration of proliferation markers into multivariable predictive models to improve response stratification in breast cancer.