Axillary Lymph Node Metastasis Status Research Articles

Role of Ultrasonographic and Clinicopathological Characteristics in Assessing Stromal Tumor-Infiltrating Lymphocyte Density in Triple-Negative Breast Cancer

A high density of stromal tumor-infiltrating lymphocytes (sTILs) is positively correlated with the pathological complete response rate and favorable survival in patients with triple-negative breast cancer (TNBC). Heterogeneity in stromal lymphocyte distribution and limited tumor sampling may affect the accuracy of sTIL quantification in biopsy samples from patients receiving neoadjuvant therapy. Thus, identifying additional biomarkers to complement sTIL evaluation is essential. To identify biomarkers that could be used to complement sTILs evaluation. Retrospective cohort study. A total of 162 patients with invasive TNBC were enrolled in the study. The following data were gathered: sTIL density, Ki67, nuclear grades of cancer cells, lymphovascular invasion status, the American Joint Committee on Cancer stage, axillary lymph node metastasis status, and ultrasonographic parameters of the tumor (size, shape, orientation, margin, internal echo pattern, posterior feature, and vascularity). The relationship between sTIL density and ultrasonographic or clinicopathological characteristics was investigated using both continuous and categorical analyses. Posterior features of the primary tumors was associated with sTIL density (p = 0.038). Additionally, the Ki67 levels and nuclear grades were also associated with sTIL density (p < 0.001 and p = 0.024, respectively). When stratified according to a 20% cut-off of sTIL density, the posterior features of primary tumors, Ki67 levels, and nuclear grades significantly differed between the high and low sTIL density tumors. Tumors with high Ki67 levels were more likely to exhibit high sTIL density than low sTIL density [odds ratio (OR): 2.75, p = 0.021]. Furthermore, nuclear grade III tumors demonstrated significantly higher sTIL density than nuclear grade I-II tumors (OR: 2.49, p = 0.014). Additionally, tumors with posterior enhancement or no posterior features were more likely to exhibit high sTIL density than tumors with acoustic shadows (OR: 2.91, p = 0.028; OR: 2.74, p = 0.022, respectively). Low sTIL density is frequently observed in tumors exhibiting acoustic shadows on ultrasound. However, high sTIL density is more common in tumors with posterior enhancement or no posterior features. Furthermore, high Ki67 levels (> 40%) and high nuclear grades are positively correlated with high sTIL density. This study findings highlight the need for closer surveillance of these biomarkers to complement sTIL evaluation in TNBC.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients.

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes. We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis. The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model. The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer

ObjectivesTo develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. Materials and methodsA retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. ResultsThe constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. ConclusionA combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

Prediction of Axillary Lymph Node Metastasis in Early-stage Triple-Negative Breast Cancer Using Multiparametric and Radiomic Features of Breast MRI

To investigate whether machine learning (ML) approaches using breast magnetic resonance imaging (MRI)-derived multiparametric and radiomic features could predict axillary lymph node metastasis (ALNM) in stage I-II triple-negative breast cancer (TNBC). Between 2013 and 2019, 86 consecutive patients with TNBC who underwent preoperative MRI and surgery were enrolled and divided into ALNM (N=27) and non-ALNM (n=59) groups according to histopathologic results. For multiparametric features, kinetic features using computer-aided diagnosis (CAD), morphologic features, and apparent diffusion coefficient (ADC) values at diffusion-weighted images were evaluated. For extracting radiomic features, three-dimensional segmentation of tumors using T2-weighted images (T2WI) and T1-weighted subtraction images were respectively performed by two radiologists. Each predictive model using three ML algorithms was built using multiparametric features or radiomic features, or both. The diagnostic performances of models were compared using the DeLong method. Among multiparametric features, non-circumscribed margin, peritumoral edema, larger tumor size, and larger angio-volume at CAD were associated with ALNM in univariate analysis. In multivariate analysis, larger angio-volume was the sole statistically significant predictor for ALNM (odds ratio=1.33, P=0.008). Regarding ADC values, there were no significant differences according to ALNM status. The area under the receiver operating characteristic curve for predicting ALNM was 0.74 using multiparametric features, 0.77 using radiomic features from T1-weighted subtraction images, 0.80 using radiomic features from T2WI, and 0.82 using all features. A predictive model incorporating breast MRI-derived multiparametric and radiomic features may be valuable in predicting ALNM preoperatively in patients with TNBC.

A Clinical-Radiomics Model of Preoperative Prediction IMN Status of Breast Cancer Patients

<h3>Purpose/Objective(s)</h3> To establish a prediction model based on clinicopathological factors and dynamic contrast enhanced (DCE)-MRI radiomics features, and to make a non-invasive prediction of IMN metastasis risk before operation. <h3>Materials/Methods</h3> The DCE-MR images and clinicopathological data of 124 breast cancer patients who had undergone internal mammary sentinel lymph node biopsy and / or internal mammary lymph node (IMN)dissection with IMN negative on preoperative images from January 2013 to December 2019 were obtained. All patients received plain breast scan and dynamic contrast-enhanced MR in Shandong Cancer Hospital before operation. In this study, three types of predictive models were established, which are based on clinicopathological risk factors, DCE-MRI imaging features and the combination of the two: 1) Clinical model: a logistic regression model based on clinicopathological factors was developed to predict ALN status. 2) Radiomics model: radiomics model based on radiomics score (RS) score is developed. 3) Clinical-radiomics model: combining the imaging features with the independent risk factors for independent prediction of IMN status, the multiple logistic regression method was used to establish a nomogram for predicting the risk of IMN metastasis. All models were evaluated by ROC and AUC, and clinical decision analysis curves was used to evaluate the clinical application value of the model. <h3>Results</h3> Three variables including primary tumor location, PR status and axillary lymph node metastasis status were included in the clinical model, and the AUC was 0.913. 150 imaging features were extracted from DCE-MRI images of primary tumors. Finally, 4 imaging features were selected to participate in the development of RS. Finally, the AUC of the imaging model combined with DCE-MRI imaging features of IMN was 0.831. The clinical-radiomics model with an AUC of 0.964. Delong test showed that, there was no significant difference in ROC curve between clinical model and radiomics model (p=0.1337) , but there were significant differences in ROC curve between clinical model and clinical-radiomics model, and between clinical model and clinical-radiomics model (p<0.05). DCA curve analysis shows that the application of clinical-radiomics model has the greatest net benefit, optimal predictive efficiency and better distinguishing ability, which is better than clinical model and radiomics model. <h3>Conclusion</h3> Clinical-radiomics model is helpful to evaluate the IMN metastasis status of breast cancer patients before operation.

A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients

In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients.

Estrogen, Progestrone Receptor and Her2/Neu Expressions incases of Breast Cancer

To correlate ER, PR and HER2/neu expressions with established prognostic factors viz. type of tumour, tumour size, tumour grade, tumour necrosis, lymphovascular invasion (tumor emboli), and axillary lymph node metastasis status. To correlate immunehistochemical marker status ( ER, PR and HER2/neu) with clinical details (age and sex) of patients. The investigative research of Progesterone receptor (PR), Estrogen receptor (ER), as well as HER-2/neu expressions in cases of developing breast carcinoma was carried out over a period of 2 years from June 2015 to May 2017 which included 101 breast cancer cases. Palpable breast lump with or without pain were most frequent complaints. Maximum number of breast cancer cases revealed involvement of left breast. It was found that ER and PR expression was strongly correlated. No significant association was found between the presence of ER, PR expression as well as the size of the tumor. ER and PR negativity was associated with breast carcinoma cases having axillary lymph node metastasis. Out of 101 breast cancer cases tumor necrosis was present in 38.61%. However, the occurrence or absence of tumour necrosis in ER, PR expression was not strongly linked. Tumor emboli were noted in 31.68% of cases. In case of breast cancer with tumor emboli decreased ER and PR expression was observed.

Construction and validation of a risk prediction model for clinical axillary lymph node metastasis in T1\u20132 breast cancer

The current diagnostic technologies for assessing the axillary lymph node metastasis (ALNM) status accurately in breast cancer (BC) remain unsatisfactory. Here, we developed a diagnostic model for evaluating the ALNM status using a combination of mRNAs and the T stage of the primary tumor as a novel biomarker. We collected relevant information on T1–2 BC from public databases. An ALNM prediction model was developed by logistic regression based on the screened signatures and then internally and externally validated. Calibration curves and the area under the curve (AUC) were employed as performance metrics. The prognostic value and tumor immune infiltration of the model were also determined. An optimal diagnostic model was created using a combination of 11 mRNAs and T stage of the primary tumor and showed high discrimination, with AUCs of 0.828 and 0.746 in the training sets. AUCs of 0.671 and 0.783 were achieved in the internal validation cohorts. The mean external AUC value was 0.686 and ranged between 0.644 and 0.742. Moreover, the new model has good specificity in T1 and hormone receptor-negative/human epidermal growth factor receptor 2- negative (HR−/HER2−) BC and good sensitivity in T2 BC. In addition, the risk of ALNM and 11 mRNAs were correlated with the infiltration of M2 macrophages, as well as the prognosis of BC. This novel prediction model is a useful tool to identify the risk of ALNM in T1–2 BC patients, particularly given that it can be used to adjust surgical options in the future.

Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks

Deep learning (DL) algorithms have been proven to be very effective in a wide range of computer vision applications, such as segmentation, classification, and detection. DL models can automatically assess complex medical image scenes without human intervention and can be applied as a second reader to provide an additional opinion for the physician. To predict the axillary lymph node (ALN) metastatic status in patients with early-stage breast cancer, a deep learning-based computer-aided prediction system for ultrasound (US) images was proposed. A total of 153 women with breast tumor US images were involved in this study; there were 59 patients with metastasis and 94 patients without ALN metastasis. A deep learning-based computer-aided prediction (CAP) system using the tumor region and peritumoral tissue in ultrasound (US) images were employed to determine the ALN status in breast cancer. First, we adopted Mask R–CNN as our tumor detection and segmentation model to obtain the tumor localization and region. Second, the peritumoral tissue was extracted from the US image, which reflects metastatic progression. Third, we used the DL model to predict ALN metastasis. Finally, the simple linear iterative clustering (SLIC) superpixel segmentation method and the LIME explanation algorithm were employed to explain how the model makes decisions. The experimental results indicated that the DL model had the best prediction performance on tumor regions with 3 mm thick peritumoral tissue, and the accuracy, sensitivity, specificity, and AUC were 81.05% (124/153), 81.36% (48/59), 80.85% (76/94), and 0.8054, respectively. The results indicated that the proposed CAP system could help determine the ALN status in patients with early-stage breast cancer. The results reveal that the proposed CAP model, which combines primary tumor and peritumoral tissue, is an effective method to predict the ALN status in patients with early-stage breast cancer.

Visceral fat metabolic activity evaluated by preoperative 18F-FDG PET/CT significantly affects axillary lymph node metastasis in postmenopausal luminal breast cancer

Obesity is known to increase breast cancer risk and aggressiveness in postmenopausal luminal breast cancer and obesity-driven dysfunctional metabolic activity in visceral adipose tissue (VAT) is considered as one of the principal underlying mechanism. We aimed to investigate the relationship between VAT metabolic activity evaluated by preoperative 18F-FDG PET/CT and axillary lymph node (ALN) metastasis in postmenopausal luminal breast cancer patients. In total, 173 patients were enrolled in study. They all underwent preoperative 18F-FDG PET/CT and surgery. VAT metabolic activity was defined as the maximum standardized uptake value (SUVmax) of VAT divided by the SUVmax of subcutaneous adipose tissue (V/S ratio). In luminal breast cancer, the patients with ALN metastasis showed significantly higher V/S ratio than the patients without ALN metastasis. Furthermore, V/S ratio was significantly associated with ALN metastasis in luminal breast cancer patients. Erythrocyte sedimentation rate, which reflect the systemic inflammation, was significantly higher in ALN metastasis group than the negative ALN metastasis group in luminal breast cancer patients and showed significant positive correlation with V/S ratio. V/S ratio significantly affects the ALN metastasis status in postmenopausal luminal breast cancer patients and it may be useful as a potential biomarker of obesity-driven systemic inflammation associated with tumor aggressiveness.

Metabolic and volume-based parameters of (18F)FDG PET/CT for primary mass and axillary lymph node metastasis in patients with invasive ductal carcinoma: a retrospective analysis in relation to molecular subtype, axillary lymph node metastasis and immunohistochemistry and inflammatory markers.

To evaluate metabolic and volume-based parameters of 18-Fluorodeoxyglucose positron emission tomography/computed tomography ((F)FDG PET/CT) for primary mass and axillary lymph node (ALN) metastasis in relation to molecular subtype, and immunohistochemistry and inflammatory markers in patients with invasive ductal carcinoma. A total of 129 patients (mean±SD age: 49.2±13.0 years) with invasive ductal breast cancer who had (F)FDG PET/CT imaging prior to chemotherapy or surgery were included in this single-center retrospective study. Data on patient age, molecular subtype, ALN metastasis status, inflammatory markers, immunohistochemistry markers and (F)FDG PET/CT imaging parameters for primary mass and ALN were recorded. ALN metastasis was evident in 52.7% of patients and associated with significantly higher median diameter (P=0.027), MTV (P<0.001) and TLG (P<0.001). NLR was positively correlated with all primary mass (p ranged from 0.041 to 0.001) and ALN (P ranged from 0.026 to <0.001) PET parameters. PET parameters did not change with respect to molecular subtype or immunohistochemistry markers. Primary mass and ALN metastasis PET parameters showed significant positive correlations for TLG (r=0.274, P=0.001) and SUVmax (r=0.358, P<0.001). In conclusion, our findings in a retrospective cohort of invasive ductal breast cancer patients revealed primary mass PET parameters to significantly differ with respect to ALN metastasis status and NLR levels, but not according to molecular subtype or immunohistochemistry markers. Accordingly, our findings highlight the value of acquisition of preoperative PET/CT imaging and role of both metabolic and volume-based parameters in predicting aggressiveness of the tumor as correlated with presence of ALN metastasis and high NLR.

Can Clinically Node-Negative Breast Cancer Patients with Suspicious Axillary Lymph Nodes at Ultrasound But Negative Fine-Needle Aspiration Be Approached as Having Node-Negative Disease?

Few data exist to elucidate whether patients with a suspicious axillary lymph node (ALN) at ultrasound but a negative fine-needle aspiration result (FNA group) can be managed as having ultrasound node-negative disease (AUN group). This study compared various ALN statuses between the AUN and FNA groups to guide further ALN management. Patients who had clinical T1-2N0 breast cancer treated with sentinel lymph node (SLN) biopsy were retrospectively analyzed. The ALN metastasis status, SLN status, and non-SLN metastasis rates in the entire population and the patients with one or two positive SLNs were compared between the AUN and FNA groups. A total of 1007 patients (886 AUN and 121 FNA patients) were eligible for the final analysis: The incidence of ALN metastasis did not differ between the AUN group (16.5%) and the FNA group (21.5%) (P=0.170). In addition, three or more metastases were found in only 2.4% of the AUN patients and 3.3% of the FNA patients (P=0.405). The non-SLN metastasis rate was 22.6% (33/146) in the AUN group and 19.2% (5/26) in the FNA group (P=0.699). For the patients with one or two positive SLNs, the rate of non-SLN metastasis was similar between the AUN group (19.6%, 27/138) and the FNA group (12.5%, 3/24) (P=0.591). Patients with a suspicious ALN at ultrasound but a negative FNA result had ALN statuses similar to those of the ultrasound node-negative patients, indicating that these patients can be treated as having ultrasound node-negative disease.

Clinicopathological analysis of CD44 and CD24 expression in invasive breast cancer.

A subpopulation of breast cancer cells with cluster of differentiation (CD)44-positive and CD24-negative expression has been reported to have stem cell properties and to have a higher tumorigenic capacity than other cells. However, the clinicopathological characteristics of this subpopulation are not fully understood. In this study, we aimed to identify the correlations between the expression of CD44 and CD24 and clinicopathological parameters and overall survival. We studied specimens from 262 patients with invasive breast cancer. Immunohistochemical staining for CD44 and CD24 was performed using tissue microarrays. The clinicopathological factors were evaluated from the patients' medical records. In correlation analysis, CD44 expression was significantly associated with human epidermal growth factor receptor 2 (HER2)-negative status (P<0.001). Conversely, CD24 expression was significantly associated with HER2-positive status (P<0.001). CD44 and CD24 expression did not demonstrate any correlation with the age, tumor size, axillary lymph node metastasis status, tumor stage, histological grade, estrogen receptor status and progesterone receptor status of patients. Upon survival analysis, there was no statistical difference in overall survival according to the expression of CD44 and CD24. The results from this study suggest that CD44 and CD24 are clinically significant markers associated with breast tumorigenesis, but not sufficient factors in determining the prognosis of invasive breast cancer.

A retrospective study of metachronous and synchronous ipsilateral supraclavicular lymph node metastases in breast cancer patients

Clinically, ipsilateral supraclavicular lymph node metastasis (ISLM) in breast cancer can be classified into 2 manifestations: metachronous and synchronous. Synchronous ISLM (T1-4, N3, M0) is stage IIIc. Metachronous ISLM is isolated supraclavicular lymph node relapse after curative treatment. Although both are featured with ipsilateral supraclavicular lymph node metastases they are two clinical entities needing to be addressed differently. It is of particular interest to know other potential discrepancies between them except for the timing of ISLM occurrence. We retrospectively reviewed 2486 breast cancer patients. Among them, 48 women were identified with metachronous ISLM and 33 with synchronous ISLM. No significant difference was found between them in terms of clinicopathological characteristics as well as survival. However, the multivariate analysis showed they had different independent prognostic factors. Axillary lymph node metastasis status ( P = 0.009) and chemotherapy after occurrence of ISLM ( P = 0.016) were independent prognostic predictors for metachronous ISLM whilst primary tumor size ( P = 0.016) and radiotherapy after diagnosis of ISLM ( P = 0.022) were independent prognostic factors for synchronous ISLM. The multidisciplinary management of ISLM patients is strongly recommended.

Correlation of Her-2 overexpression with endocrine status and response to tamoxifen treatment in patients with breast cancer

To investigate the correlation of Her-2 overexpression with endocrine status and response to tamoxifen treatment in patients with breast cancer. Immunohistochemistry was used to measure the expression of estrogen receptor (ER), progesterone receptor (PR), Her-2, p53, proliferating cell nuclear antigen (PCNA) in 4773 consecutive in-hospitalized patients with primary breast cancer confirmed by pathological examination of the resected specimens, all females, aged 50 (15 - 92), and were followed up for 42 months on average. 1090 if then received tamoxifen (TAM), an anti-estrogen agent, post-operationally. The correlation of Her-2 overexpression with other factors was analyzed. (1) The Her-2 overexpression rate was 26.6%. Her-2 overexpression was significantly negatively correlated with expressions of ER and PR (both P < 0.01). (2) The general 3-year disease-free survival (DFS) rate of those treated with TAM was 92.7%; and the 3-year GFS rate of the subgroup of the TAM-treated patients with Her-1 overexpression was 91.2%, significantly lower than that of those without Her-2 overexpression (93.4%, P = 0.004). (3) The 3-year DFS rate premenopausal patients with Her-2 overexpression was 91.8%, not significantly different from that of those without Her-2 overexpression (92.6%, P > 0.05), however, the 3-year DFS rate of the postmenopausal patients with Her-2 overexpression was 90.4%, significantly lower than that of those without Her-2 overexpression (92.6%, P = 0.010). (4) The 3-year DFS rate of the axillary lymph node-positive patients with Her-2 overexpression was 89.1%, significantly lower than that without Her-2 overexpression (92.3%, P = 0.037). (5) In the premenopausal patients there was no significant difference in the 3-year DFS rate between the lymph node-negative patients with and without Her-2 overexpression (P > 0.05). However, in the postmenopausal lymph node positive patients the 3-year DFS rate of those with Her-2 overexpression was 88.7%, significantly lower than that of those without Her-2 overexpression (92.2%, P = 0.0069). ER and PR are independent factors of Her-2 expression. Her-2 overexpression signifies resistance to TAM treatment. The response to TAM is not influenced by the Her-2 expression and axillary lymph node metastasis status in the premenopausal patients, and is not influenced by the Her-2 expression level in the postmenopausal patients.