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24.01.2018 | Breast Oncology | Ausgabe 5/2018

Annals of Surgical Oncology 5/2018

A Predictive Model for Axillary Node Pathologic Complete Response after Neoadjuvant Chemotherapy for Breast Cancer

Zeitschrift:
Annals of Surgical Oncology > Ausgabe 5/2018
Autoren:
MD Olga Kantor, MD Lynn McNulty Sipsy, MD Katharine Yao, MD, MS, FACS Ted A. James

Abstract

Background

Recent trials have suggested the feasibility of performing a sentinel lymph node biopsy (SNB) following neoadjuvant chemotherapy (NAC). The selection of suitable patients for this approach remains controversial. We developed a predictive model to identify patients most likely to benefit from SNB following NAC.

Methods

The National Cancer Data Base was used to identify patients with clinically node positive (cN+) breast cancer undergoing NAC followed by breast surgery and axillary lymph node dissection (ALND). Patients were randomly assigned to a 70% testing or 30% validation cohort for model development. A predictive model was built based on significant factors associated with pathologic nodal response (pN0) and breast response.

Results

Using the testing cohort (n = 13,396), multivariate regression was used to identify predictors of pN0 based on preoperative factors. Younger age, hormone receptor (HR)-negative/Her2-negative, HR-positive/Her2-positive, HR-negative/Her2-positive, high-grade, ductal histology, cN1 versus cN2, and extent of breast response were all significant independent predictors of pN0 on adjusted analysis. The odds ratios translated into a 10-point scale correlating to a stepwise increase in pN0 response. The area under the curve for the ROC curves for the testing and validation cohorts was 0.781 and 0.788, respectively (p < 0.01).

Conclusions

Our model incorporates known preoperative factors to predict the likelihood of pN0 response in patients with cN+ disease who undergo NAC. For patients with high scores, SNB should be considered over ALND, because these patients have a greater likelihood of having negative nodes at final pathology.

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