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Management of axilla in breast cancer: The saga continues
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Cancer Science & Therapy

ISSN: 1948-5956

Open Access

Management of axilla in breast cancer: The saga continues


World Congress on Breast Cancer

August 03-05, 2015 Birmingham, UK

Rakhshanda Layeequr Rahman and Portia Siwawa

Texas Tech University Health Sciences Center, USA

Posters-Accepted Abstracts: J Cancer Sci Ther

Abstract :

Sentinel-lymph-node biopsy(SLNB) has not been subjected to the STARD(Standards for Reporting of Diagnostic Accuracy) for defining its role in management of axilla in breast cancer patients. Prospective trials investigating the accuracy of SLNB for cN0 (primary surgical therapy) and cN1 patients (neoadjuvant chemotherapy) have not utilized likelihood ratios(LR) to assess the impact of false negative SLNB. This review utilizes the: (i) Reported rates for pre-test probabilities of node positive disease from Surveillance, Epidemiology, and End Results(SEER) database for the cN0 patients (primary surgical therapy) for each T stage; calculatesthe negative LR from NSABP B-32 trial data; and uses the Bayesian nomogram to compute the post-test probability of missing the metastatic axillary node based on negative SLNB. (ii) Reported rates of complete axillary response in ACOSOG-Z1071 trial for cN1 patients to calculate the pre-test probabilities of residual nodal disease for each biological tumor sub-type; calculates the negative LR from ACOSOG-Z1071, and SENTINA trial data; and uses the Bayesian nomogram to compute the post-test probability of missing the residual metastatic axillary node based on negative SLNB. For cN0 disease, the odds of missing axillary disease based on negative SLNB for each T stage are: T1a=0.7%; T1b=1.1%; T1c=4%; T2=6%; T3=17%. For cN1 disease, the odds of missing residual axillary disease based on negative SLNB for each biological subtype are: HER2neu+=7%; Triple negative=13%; ER+/PR+/HER2neu-=40%. Negative LR is more accurate and superior to false negative rate for determining the clinical utility of SLNB by taking into account the changing pre-test probability of disease.

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