J Korean Surg Soc.  2000 Sep;59(3):329-334.

Axillary Lymph-Node Metastases in Patients with T1 Breast Cancer

Affiliations
  • 1Department of Surgery, Masan Samsung Hospital, Sungkyunkwan University School of Medicine, Masan, Korea.

Abstract

PURPOSE: In T1 tumors, the reported incidence of lymph-node metastases ranges from 21% to 35%. We analyzed the pathological parameters of T1 tumors for their association with the likelihood of axillary lymph-node metastases. Our objectives were to determine if standard pathologic factors can predict lymph-node metastases in T1 tumors and to provide a basis for patient selection for nonradical surgery.
METHODS
Sixty-five patients with T1 unilateral invasive breast cancer were studied. All patients underwent axillary dissection from 1990 to 1999 at Masan Samsung Hospital, and the pathologic status of the nodes was reviewed. The associations between the incidence of axillary lymph-node metastases and pathologic factors, including age, size, histologic subtype, nuclear grade, hormone receptor status, and lymphatic/vascular invasion, were analyzed. RESULTS: Of the 65 patients, 21 (32.3%) had nodes that were positive for metastases. The independent predictor of lymph-node metastases in the multivariate logistic regression analyses was a tumor size larger than 1 cm (p<0.05). However, other predictors showed nonspecific findings. CONCLUSION: These results suggest that the characteristics of the primary tumor can help assess the risk for axillary lymph-node metastases. Axillary lymph-node dissection should be performed routinely for all patients with lesions with a tumor more than 1 cm in size. Although a routine axillary dissection or radiation therapy to the axilla might be spared in selected patients who are assessed to be at minimal risk, new prognostic factors for providing reliable assurance of the absences of axillary lymph-node metastases must be investigated.

Keyword

T1 breast cancer; Lymph-node metastases; Axilla

MeSH Terms

Axilla
Breast Neoplasms*
Breast*
Humans
Incidence
Logistic Models
Neoplasm Metastasis*
Patient Selection
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