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[Prognostic values of grading system for lymph vessel tumor emboli in patients with invasive breast carcinomas of no special type].
Zhonghua Zhong Liu za Zhi [Chinese Journal of Oncology] 2017 October 24
Objective: To investigate the grading system for lymph vessel tumor emboli and its prognostic value in patients with invasive carcinomas of no special type (ICNST) of the breast. Methods: Clinical data of 466patients with ICNST were collected from January 2006 to December 2008 in the Fourth Hospital of Hebei Medical University. The expression levels of D2-40, estrogen receptor(ER), progesterone receptor(PR) and human epidermal growth factor receptor 2 (HER-2) were analyzed using immunohistochemical staining. Grades for lymph vessel tumoremboli were classified based on the number of mitotic and apoptotic figures in tumor cells under a high-power field. Correlation analysis was performed using Spearman rank correlation test. Kaplan-meier curves and Log-rank tests were used to analyze the survival rate. Multivariate Cox proportional hazard model was used to analyze the prognostic factors. Results: Among the 466 patients, grades for lymph vessel tumor emboli were categorized as follows: 280 cases were grade 0 (60.1%); 112 cases were grade 1 (24.0%); 58 cases were grade 2 (12.5%); 16 cases were grade 3 (3.4%). Correlation analyses showed that lymph vessel tumor emboli grading system was positively correlated with lymph node metastasis ( r =0.365, P <0.001). Kaplan-Meier univariant analysis showed that histological grading, lymph vessel tumor emboli grading system, lymph node metastasis, the expression levels of ER, PR and HER-2 and molecular typing were associated with prognosis of patients ( P <0.05 for all). Multivariate analysis of Cox proportional hazard model showed that lymph vessel tumor emboli grading system and lymph node metastasis were independent prognostic factors in patients with ICNST( P <0.05 for all). Conclusion: Grading system for lymph vessel tumor emboli canpredict the clinical outcome of patients with ICNST.
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