- Poster presentation
- Open Access
Clinicopathologic and molecular predictors of axillary lymph node metastasis in early-stage breast cancer: a mathematical predictive model
© BioMed Central Ltd. 2010
- Published: 18 May 2010
- Breast Cancer
- Vascular Invasion
- Multivariate Logistic Regression Model
- Axillary Lymph Node Metastasis
- Stepwise Variable Selection
Axillary nodal (LN) stage is the most important prognostic factor in early-stage breast cancer (BC) which has risen as a result of widespread BC screening. However, surgical procedures for LN staging carry the risk of early and long-term postoperative morbidity. Therefore, reliable predictors of nodal status are needed to reduce the extent of axillary surgery and its consequences.
Predictive factors of axillary LN status at the time of primary diagnosis have been assessed using a broad panel of immunohistochemical tissue markers in a well-characterised series (n = 1,130) of primary operable (stage I & II) invasive BC cases, temporally divided into training (n = 730) and validation (n = 400) sets. Potential predictor factors were initially assessed using univariate analysis. A multivariate logistic regression model was fitted using backward stepwise variable selection in the training set. The resulting model was subsequently validated utilising the validation set.
Within the training set, the proportion of cancers with positive nodes was significantly higher with younger age, larger tumour size, higher grade, no special type tumours, definite vascular invasion (VI), ER-, HER2+, PIK3CA+, and high Ki-67 Labelling Index (Ki-67LI). A multivariate logistic regression model indicated that predictors of nodal positivity included definite VI, higher grade, histological type, tumour size ≥ 2 cm, HER2+, and Ki-67LI. This model resulted in 86.6% accuracy in predicting node positive cases, with area under the curve (AUC = 73.1%) and excellent goodness of fit (P = 0.981). Model cross-validation revealed an AUC of 72.3%.
In this study, VI and tumour grade were the strongest independent predictive factors of nodal status in BC patients at the time of primary diagnosis. Our predictive model, which jointly incorporates VI, tumour grade, histological type, tumour size, HER2 status and Ki-67LI, confers an objective predictive accuracy relative to single predictive factors.
Breast Cancer Campaign and Egyptian Government funded this project.