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These authors contributed equally to this work.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Nodal staging in breast cancer is a key predictor of prognosis. This paper presents the results of potential clinicopathological predictors of axillary lymph node involvement and develops an efficient prediction model to assist in predicting axillary lymph node metastases. Seventy patients with primary early breast cancer who underwent axillary dissection were evaluated. Univariate and multivariate logistic regression were performed to evaluate the association between clinicopathological factors and lymph node metastatic status. A logistic regression predictive model was built from 50 randomly selected patients; the model was also applied to the remaining 20 patients to assess its validity. Univariate analysis showed a significant relationship between lymph node involvement and absence of nm-23 (

Axillary lymph node metastasis is one of the most important prognostic determinants for patients with breast cancer [

Management strategies that avoid axillary invasive procedures are needed for lymph node negative patients. If we can predict the state of the axillary lymph nodes before SLNB, individuals who are axillary negative could avoid the unnecessary axillary operation. However, the preoperative clinical and imaging examinations of the axilla are rather poor for predicting axillary lymph node involvement. As we know, lymph node metastasis is a multifactorial event. Within the recent years, a number of studies have investigated the factors and their predictive value for predicting non-sentinel lymph node metastasis (NSLN). The Memorial Sloan Kettering Cancer Center (MSKCC) nomogram showed a receiver-operator characteristic curve (ROC) of 0.76 [

The goal of this study is to characterize the various clinicopathologic features in cases of early breast cancer by using a logistic regression model, in order to identify the factors that might help in predicting the status of the axillary lymph node. Immunohistochemistry technique was used to study hormone receptor (ER, PR), human epidermal growth factor receptor 2 (HER2), tumor metastasis /invasion related genes (Kiss-1, nm-23, Cath-D), oncogenesis related gene (p53), and proliferation related gene (Ki-67). Fluorescence in situ hybridization (FISH) was used for those where IHC staining for HER2 is equivocal or 2+. We also take some important clinical characteristics (e.g., tumor size, age, pathologic tumor grading, menopausal status) into account.

Detailed patient information is described in

Tumor tissues were obtained from paraffin embedded specimens. We took age at diagnosis, menopausal status, tumor size, histological grading, lymph node involvement and status of estrogen receptor (ER), progesterone receptor (PR), HER2/neu, Kiss-1, nm-23, p53, ki-67 and Cath-D into account. Among the 70 breast cancer patients, there were 62 infiltrating ductal carcinomas, six infiltrating lobular carcinomas, and two mucinous carcinomas, 44 had lymph-node-negative disease and 26 had lymph-node-positive disease (

Data was subjected to univariate and multivariate logistic regression using SPSS statistical software version 16.0 (SPSS Inc., Chicago, IL, USA). Prediction criterion was a dichotomous variable indicating the pathologic result of the axillary lymph node dissection revealing either no lymph node metastasis or at least one metastatic axillary lymph node. Factors included in the analysis were categorized as shown in

Goodness-of-fit of the model was evaluated by a Hosmer-Lemeshow (HL) test. Receiver operating characteristic (ROC) curves was used to assess the adequacy of the prediction model and determined an optimal cut-off value (A model with a ROC of 0.5 is equal to the toss of a coin. A model with a ROC of 0.7–0.8 is considered good, whereas an ROC of 0.81–0.9 has excellent discrimination). A P-value < 0.05 was regarded as statistically significant.

The standard logistic regression formula is:
_{0}” is a constant. X_{n} is a lymph node metastasis promoting factor when β_{n} > 0, conversely, X_{n} is a lymph node metastasis suppressing factor when β_{n} < 0.

The overall frequency of lymph node metastasis was 37.14%. In the univariate analysis, absence of nm-23 (

Fifty patients were randomly selected for the modeling group, the other 20 patients were placed in the validation group. There was no significant difference between the modeling group and the validation group (40%

To avoid omitting significant indicators, factors with a significance of

Goodness-of-fit of the model was assessed by Hosmer-Lemeshow (HL) test. Degree of freedom (df) is 8 (

The estimated probabilities of all the 50 patients used for building the model was calculated by the equation and shown in

The validity of the logistic regression model was assessed in the remaining 20 patients. Every patient's estimated probability was calculated by the formula and shown in

Nodal staging in breast cancer is a key predictor of prognosis [

In the past decade, some studies were conducted for the development of nomograms to identify patients with sufficiently low risk of NSLNM (non sentinel node metastasis) who can then avoid ALND. Van Zee

In our study, we found that postmenopausal women, large tumor (>2 cm) and PR positive women appear to have a trend of high risk of lymph node involvement. However, there was no statistical significance. On the contrary, there is a trend of negative correlation between women with positive expression of ER, HER2, nm-23, Kiss-1 and lymph node involvement. Furthermore, nm-23 and Kiss-1 have a significant negative correlation with ALN involvement in the univariate analysis, and absence of Kiss-1 remains significantly associated with positive axillary node status in the multivariate analysis.

Nm-23 protein was originally identified as a metastasis suppressor protein [

Kiss-1 has been identified as a putative human metastasis suppressor gene in melanomas [

Tumor size is an important factor influencing the lymph node involvement in breast cancer. This fact has been confirmed in some studies. Wada

ER status is both prognostic and predictive factor for breast cancer [

PR may play a contrasting role from the regression model (OR = 3.975). This observation seems counterintuitive, but it actually is in agreement with the findings of other large studies. Ravdin

One could argue that HER2 seems as a metastases inhibitor in our study. The frequencies of lymph node metastases in HER2-positive tumors were lower than those for HER2-negative tumors (31.2%

Some studies have shown that elder or postmenopausal women have a lower risk of ALN involvement [

The predictive model presented here relies on readily available clinicopathological factors, indicating that some clinicopathologic factors might be used to select patients who were more likely to have positive axillary lymph nodes. However, this model should be applied prospectively to a large number of patients and including additional parameters into the prediction strategy to verify its validity. Maybe in the future, a substantial proportion of women with invasive breast cancer could avoid the morbidity of axillary dissection.

ROC curve calculation for the logistic regression model applied to the modeling group (n = 50).

Logistic regression model Scatter diagram (n = 50).

Logistic regression model Scatter diagram (n = 20).

ROC curve calculation for Logistic regression model applied to the validation group (n = 20).

Patients and tumor characteristics (n = 70).

X_{1} |
Age(year) | ≤50 | 0 | 28 (40.0%) |

>50 | 1 | 42 (60.0%) | ||

| ||||

X_{2} |
menopausal status | Premenopausal | 0 | 25 (35.7%) |

postmenopausal | 1 | 45 (64.3%) | ||

| ||||

X_{3} |
tumor size(cm) | ≤2 | 0 | 28 (40.0%) |

>2 | 1 | 42 (60.0%) | ||

| ||||

X_{4} |
histological grading | I | 0 | 16 (22.9%) |

II-III | 1 | 54 (77.1%) | ||

| ||||

X_{5} |
ER | (−) | 0 | 38 (54.3%) |

(+) | 1 | 32 (45.7%) | ||

| ||||

X_{6} |
PR | (−) | 0 | 20 (28.6%) |

(+) | 1 | 50 (71.4%) | ||

| ||||

X_{7} |
HER2 | (−) | 0 | 50 (71.4%) |

(+) | 1 | 20 (28.6%) | ||

| ||||

X_{8} |
nm-23 | (−) | 0 | 22 (31.4%) |

(+) | 1 | 48 (68.6%) | ||

| ||||

X_{9} |
Kiss-1 | (−) | 0 | 26 (37.1%) |

(+) | 1 | 44 (62.9%) | ||

| ||||

X_{10} |
P53 | (−) | 0 | 34 (48.6%) |

(+) | 1 | 36 (51.4%) | ||

| ||||

X_{11} |
Ki-67 | (−) | 0 | 25 (35.7%) |

(+) | 1 | 45 (64.3%) | ||

| ||||

X_{12} |
Cath-D | (−) | 0 | 41 (58.6%) |

(+) | 1 | 29 (41.4%) |

Modeling group patients and tumor characteristics (n = 50).

X_{1} |
Age (years) | ≤50 | 0 | 18 (36.0%) | |

>50 | 1 | 32 (64.0%) | |||

| |||||

X_{2} |
Menopausal status | Premenopausal | 0 | 16 (32.0%) | |

postmenopausal | 1 | 34 (68.0%) | |||

| |||||

X_{3} |
Tumor size (cm) | ≤2 | 0 | 21 (42.0%) | |

>2 | 1 | 29 (58.0%) | |||

| |||||

X_{4} |
Histological grading | I | 0 | 12 (24.0%) | |

II-III | 1 | 38 (76.0%) | |||

| |||||

X_{5} |
ER | (−) | 0 | 27 (54.0%) | |

(+) | 1 | 23 (46.0%) | |||

| |||||

X_{6} |
PR | (−) | 0 | 14 (28.0%) | |

(+) | 1 | 36 (72.0%) | |||

| |||||

X_{7} |
HER2 | (−) | 0 | 34 (68.0%) | |

(+) | 1 | 16 (32.0%) | |||

| |||||

X_{8} |
nm-23 | (−) | 0 | 18 (36.0%) | |

(+) | 1 | 32 (64.0%) | |||

| |||||

X_{9} |
Kiss-1 | (−) | 0 | 15 (30.0%) | |

(+) | 1 | 35 (70.0%) | |||

| |||||

X_{10} |
P53 | (−) | 0 | 25 (50.0%) | |

(+) | 1 | 25 (50.0%) | |||

| |||||

X_{11} |
Ki-67 | (−) | 0 | 17 (34.0%) | |

(+) | 1 | 33 (66.0%) | |||

| |||||

X_{12} |
Cath-D | (−) | 0 | 30 (60.0%) | |

(+) | 1 | 20 (40.0%) |

Univariate analysis of tumor characteristics and lymph nodes involvement (n = 50).

^{2} |
||||||
---|---|---|---|---|---|---|

| ||||||

X_{1} |
Age (years) | |||||

≤50 | 10 | 8 | 0.231 | 0.630 | ||

>50 | 20 | 12 | ||||

X_{2} |
Menopausal status | |||||

Premenopausal | 11 | 5 | 0.751 | 0.386 | ||

Postmenopausal | 19 | 15 | ||||

X_{3} |
Tumor size (cm) | |||||

≤2 | 15 | 6 | 1.970 | 0.160 | ||

>2 | 15 | 14 | ||||

X_{4} |
Histological grading | |||||

I | 7 | 5 | 0.018 | 0.892 | ||

II-III | 23 | 15 | ||||

X_{5} |
ER | |||||

(−) | 14 | 13 | 1.624 | 0.203 | ||

(+) | 16 | 7 | ||||

X_{6} |
PR | |||||

(−) | 10 | 4 | 1.058 | 0.304 | ||

(+) | 20 | 16 | ||||

X_{7} |
HER2 | |||||

(−) | 19 | 15 | 0.751 | 0.386 | ||

(+) | 11 | 5 | ||||

X_{8} |
nm-23 | |||||

(−) | 7 | 11 | 5.223 | 0.022 | ||

(+) | 23 | 9 | ||||

X_{9} |
Kiss-1 | |||||

(−) | 4 | 11 | 9.921 | 0.002 | ||

(+) | 26 | 9 | ||||

X_{10} |
P53 | |||||

(−) | 14 | 11 | 0.333 | 0.564 | ||

(+) | 16 | 9 | ||||

X_{11} |
Ki-67 | |||||

(−) | 10 | 7 | 0.015 | 0.903 | ||

(+) | 20 | 13 | ||||

X_{12} |
Cath-D | |||||

(−) | 18 | 12 | 0.000 | 1.000 | ||

(+) | 12 | 8 |

Multivariate analysis of clinicopathological data and nodes involvement (n = 50).

| ||||||||
---|---|---|---|---|---|---|---|---|

X_{2} |
menopausal status | 1.182 | 0.879 | 1.808 | 0.179 | 3.262 | 0.582 | 18.282 |

X_{3} |
Tumor size | 1.297 | 0.816 | 2.526 | 0.112 | 3.658 | 0.739 | 18.108 |

X_{5} |
ER | −0.906 | 0.783 | 1.340 | 0.247 | 0.404 | 0.087 | 1.874 |

X_{6} |
PR | 1.380 | 0.963 | 2.052 | 0.152 | 3.975 | 0.601 | 26.269 |

X_{7} |
HER2 | −0.124 | 0.829 | 0.022 | 0.881 | 0.883 | 0.174 | 4.488 |

X_{8} |
nm-23 | −1.166 | 0.836 | 1.948 | 0.163 | 0.312 | 0.061 | 1.602 |

X_{9} |
Kiss-1 | −2.171 | 0.921 | 5.559 | 0.018 | 0.114 | 0.019 | 0.693 |

Constant | −0.474 |