Predicting nodal positivity in women with hormone receptor-positive (HR+), early stage breast cancer (ESBC).

Authors

Emily Ray

Emily Miller Ray

UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC

Emily Miller Ray , Paula D. Strassle , Charles E. Gaber , Hyman B. Muss , Stephanie M. Downs-Canner

Organizations

UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, University of North Carolina at Chapel Hill, Chapel Hill, NC

Research Funding

No funding received
None

Background: Omission of axillary surgery is appropriate in some patients with clinically node-negative (cN0), HR+ ESBC; however, there are no pre-operative tools to predict pathologic node positivity (pN+) in these women. We propose a clinically validated predictive model to inform treatment decisions regarding axillary evaluation. Methods: We constructed a cohort of adult women with ESBC (clinical T1/T2, N0, M0) diagnosed 2012-2016, who underwent lumpectomy or mastectomy and lymph node surgery without neoadjuvant therapy using the National Cancer Database breast cancer dataset. The dataset was non-randomly split into training (2012-2015) and testing (2016) for development and validation. Stepwise logistic regression was used to identify predictors of pathologic node positivity (pN0 vs pN+) in the training dataset. Potential predictors included: age, race, ethnicity, comorbidity score, histologic type, clinical T, ER positivity, PR positivity, HER2 positivity, and grade. Predictor variables required a bivariate p-value <0.30 to be entered into model, and an adjusted p-value <0.35 to stay in model. A partial score method was used to develop a lymph node prediction score (LNPS) by assigning a weighted value to each strong predictor variable (OR >1.5) and adding together the values for each included variable. LNPS was treated as a linear variable for prediction in validation dataset. Results: 423,068 women were included (2012-2015: 334,778; 2016: 88,290). Pathologic node positivity was 17% in 2012-2015 and 2016. All variables were included in the final stepwise model. Strong predictors were age, histologic type, clinical T, and grade. Scores ranged from 0-11. In the validation dataset, predicted pN+ by LNPS was very similar to actual pN+ (Table). A 1-point increase in LNPS was associated with a 3.3% increase in absolute risk of pN+. Conclusions: A novel lymph node prediction score can be used in HR+ cT1-T2 cN0 breast cancers to estimate the probability of pN+ and guide decisions regarding axillary surgical evaluation.

Predicted and actual probability of pN+ in the testing dataset.

LNPSaNPredicted pN+ (%)Actual pN+ (%)Difference
(%)
01,5076.53.23.3
18717.95.02.9
21289.67.81.8
430,21714.114.9-0.8
61,38120.218.22.0
84,19428.031.2-3.2
103,72237.432.94.5
117842.646.2-3.6

aLNPS included: age (18-59 years: 1 point; ≥60 years: 0), histologic type (lobular: 4; ductal: 3, other subtypes: 0), clinical T (T1mi/T1a: 0; T1b: 1; T1c: 3; T2: 7; T1 unspecified: 2), and grade (grade I/II: 0; grade III/IV: 1)

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Abstract Details

Meeting

2020 ASCO Virtual Scientific Program

Session Type

Poster Session

Session Title

Breast Cancer—Local/Regional/Adjuvant

Track

Breast Cancer

Sub Track

Local-Regional Therapy

Citation

J Clin Oncol 38: 2020 (suppl; abstr 574)

DOI

10.1200/JCO.2020.38.15_suppl.574

Abstract #

574

Poster Bd #

66

Abstract Disclosures

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