Improved Model for Predicting Axillary Response to Neoadjuvant Chemotherapy in Patients with Clinically Node-Positive Breast Cancer.
10.4048/jbc.2017.20.4.378
- Author:
Hyung Suk KIM
1
;
Man Sik SHIN
;
Chang Jong KIM
;
Sun Hyung YOO
;
Tae Kyung YOO
;
Yong Hwa EOM
;
Byung Joo CHAE
;
Byung Joo SONG
Author Information
1. Division of Breast Surgery, Department of Surgery, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Axilla;
Breast neoplasms;
Lymph nodes;
Neoadjuvant therapy
- MeSH:
Axilla;
Biopsy;
Breast Neoplasms*;
Breast*;
Drug Therapy*;
Humans;
Lymph Nodes;
Multivariate Analysis;
Neoadjuvant Therapy;
Odds Ratio;
Polymerase Chain Reaction;
Prospective Studies;
ROC Curve
- From:Journal of Breast Cancer
2017;20(4):378-385
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: Pathological complete response (pCR) of axillary lymph node (LN) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC). Treatment of the axilla after NAC is not well established and the value of sentinel LN biopsy following NAC remains unclear. This study investigated the predictive value of axillary response following NAC and evaluated the predictive value of a model based on axillary response. METHODS: Data prospectively collected on 201 patients with clinically node-positive breast cancer who were treated with NAC and underwent axillary LN dissection (ALND) were retrieved. A model predictive of axillary pCR was developed based on clinicopathologic variables. The overall predictive ability between models was compared by receiver operating characteristic (ROC) curve analysis. RESULTS: Of 201 patients who underwent ALND after NAC, 68 (33.8%) achieved axillary pCR. Multivariate analysis using axillary LN pCR after NAC as the dependent variable showed that higher histologic grade (p=0.031; odds ratio [OR], 2.537; 95% confidence interval [CI], 1.087–5.925) and tumor response rate ≥47.1% (p=0.001; OR, 3.212; 95% CI, 1.584–6.515) were significantly associated with an increased probability of achieving axillary pCR. The area under the ROC curve for estimating axillary pCR was significantly higher in the model that included tumor response rate than in the model that excluded this rate (0.732 vs. 0.649, p=0.022). CONCLUSION: Tumor response rate was the most significant independent predictor of axillary pCR in response to NAC. The model that included tumor response rate was a significantly better predictor of axillary pCR than the model that excluded tumor response rate.
