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A Multivariate Analysis of Pathological Factors in Prognostic Evaluation of Breast Carcinoma: a Single-Institutional
Vijay Bhaskar L1,2, Shreya S Kaundinya1, Nikitha S B1
1Department of Radiation Oncology, Apollo Hospitals, Bangalore, Karnataka India.
Abstract:
Breast carcinoma is the most common malignancy among women globally, contributing significantly to cancer-related morbidity and mortality Bray et al. CA: A Cancer J Clin 74: 229-63, (2024). Prognostic evaluation using pathological markers, such as lymphovascular space invasion (LVSI), perineural invasion (PNI), and histologic grade, helps in risk stratification of patients and guides treatment. We evaluated the prognostic significance of LVSI, PNI, and histologic grade on overall survival (OS) and disease-free survival (DFS) in patients with invasive breast carcinoma. This retrospective study was conducted over five years. The study included 207 patients who were treated for breast carcinoma. Data were analyzed using Kaplan-Meier survival curves and Cox proportional hazards regression for overall survival and disease-free survival. LVSI was identified in 116 patients and was significantly associated with lower overall survival (OS) (HR: 0.12; 95% CI: 0.03-0.40; p = 0.001). PNI was observed in 34 patients and was associated with an elevated risk of mortality (HR, 30.8; 95% CI, 10.1-93.2; p < 0.001). Positive surgical margins were also significantly associated with poorer OS (HR, 2.8; 95% CI, 1.1-7.03; p = 0.01). Histologic grade was not significantly associated with OS or DFS. Multivariate analysis did not reveal any statistically significant associations between LVSI, PNI, histologic grade, or DFS. Five-year OS and DFS rates were 74.5% and 77%, respectively. In this cohort, LVSI, PNI and surgical margin status were significantly associated with overall survival, their prognostic role must be interpreted within the context of tumor stage, surgical margin status, molecular subtype, and treatment received, all of which significantly impacted outcomes, whereas their influence on DFS was limited. Incorporating these factors into risk stratification models may improve prognostic accuracy and treatment planning.
