Effect of capsule invasion on recurrence and survival in axillary lymph node metastases of breast cancer.

OBJECTIVE

The aim of this study was to investigate the effect of capsule invasion on recurrence and survival in breast cancer patients with axillary lymph node metastases.

METHODS

This retrospective study included 135 breast cancer patients with axillary lymph node involvement who underwent surgery between 2009 and 2018. The relationships between capsule invasion and various clinicopathological factors-including demographic parameters, tumor stage, surgical technique, histological type, number of involved lymph nodes, tumor size, estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 index-were analyzed using chi-square, Kaplan-Meier, and Fisher's exact tests. Additionally, multivariate Cox regression analysis was performed to assess the independent prognostic value of capsule invasion for recurrence. A p<0.05 was considered statistically significant.

RESULTS

Capsular invasion was observed in 64 of 135 patients (47.4%). Recurrence occurred in six patients with capsule invasion compared to only one patient without capsule invasion. Multivariate Cox regression analysis, controlling for positive lymph node count, tumor size, grade, and receptor status, confirmed that capsule invasion was an independent predictor of recurrence [HR 3.45, 95%CI 1.12-10.65, p=0.032]. No significant association was found between tumor size and capsule invasion (p>0.05). During follow-up, 20 patients died (9 with and 11 without capsule invasion), with no significant difference in 5-year survival or mean survival time between groups (p=0.972, Kaplan-Meier analysis). Grading of capsular invasion showed a significant correlation with recurrence (p=0.026).

DISCUSSION: Although lymph node capsule invasion in breast cancer with axillary lymph node involvement does not significantly impact overall survival, it independently and significantly elevates the risk of recurrence, as demonstrated by multivariate analysis.