Risk Of Axillary Lymph Node Metastasis Research Articles

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature. This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

Independent risk factors for axillary lymph node metastasis in breast cancer patients with one or two positive sentinel lymph nodes

BackgroundThe benefit of axillary lymph node dissection (ALND) in breast cancer patients with one or two positive sentinel lymph nodes (SLNs) remains inconclusive. The purpose of this study was to identify risk factors independently associated with axillary lymph node (ALN) metastasis.MethodsWe retrospectively analyzed data from 389 Chinese breast cancer patients with one or two positive SLNs who underwent ALND. Univariate and multivariate logistic regression analyses were performed to identify ALN metastasis-associated risk factors.ResultsAmong the 389 patients, 174 (44.7%) had ALN metastasis, while 215 (55.3%) showed no evidence of ALN metastasis. Univariate analysis revealed significant differences in age (< 60 or ≥ 60 years), human epidermal growth factor receptor-2 (Her-2) status, and the ratio of positive to total SLNs between the ALN metastasis and non-metastasis groups (P < 0.05). The multivariate analysis indicated that age, the ratio of positive to total SLNs, and occupations were significantly different between the two groups. Lastly, younger age (< 60 years), a higher ratio of positive to total SLNs, and manual labor jobs were independently associated with ALN metastasis (P < 0.05).ConclusionsThe risk of ALN metastasis in breast cancer patients with one or two positive SLNs can be further increased by younger age, manual labor jobs, and a high ratio of positive to total SLNs. Our findings may also aid in identifying which patients with one or two positive SLNs may not require ALND.

Association of axillary node status with clinicopathological characteristics and expression of EZH2 and CD44 in primary breast ductal carcinoma.

Objective:In order to enhance the prognostic benefit of new molecular markers, the aim of this study was to identify possible association of axillary lymph node (ALN) status and pN with clinicopathological characteristics and expression of EZH2 and CD44 in invasive ductal carcinoma (IDC) of the breast.Methods:The investigation included 106 patients with IDC who had undergone radical mastectomy at the Clinic of Endocrine Surgery in Nis. Clinicopathologic parameters and immunohistochemical expression of EZH2 and CD44 in primary IDC were investigated in relation to ALN status and pN.Results:Our univariate analysis established that T3-T4 stage, high EZH2, and high EZH2 with ER- were associated with ALN metastasis (p=0.014; 0.003; 0.013). Decreased probability for ALN involvement was found with T1 stage, and low EZH2 with ER+ (p=0.032; 0.022). Multivariant analysis established that high EZH2 in cancer cells was associated with high risk for ALN metastases (p=0.004); T1 tumors were associated with low risk (p=0.037). Higher pN was associated with high EZH2, high EZH2 with ER-, as well as an advanced clinical and disease stage (p=0.006; 0.001; p=0.002, 0.001). Lower pN was associated with ER+, and ER+ with low EZH2 (p= 0.004; 0.012). CD44 was not associated with ALN involvement, nor with pN.Conclusions:This study revealed association of EZH2 with ALN metastases, where disease stage and expression profiles of EZH2 and ER may have affected regional pN.

Reduced risk of axillary lymphatic spread in triple-negative breast cancer.

We examined the association between the hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status of women with primary breast cancer and the risk of axillary lymph node (ALN) involvement at the time of diagnosis. Information on 20,009 women diagnosed with primary breast cancer between 2008 and 2012 was retrieved from the Danish Breast Cancer Cooperative Group database. The associations between clinical and pathological variables and ALN involvement at the time of diagnosis were evaluated in univariate and multivariate regression analyses, as well as the significance of tumor subtypes in ALN involvement. The risk of ALN metastases at the time of diagnosis was significantly reduced in HR-negative patients compared to HR-positive patients [adjusted odds ratio (OR) 0.69; 95 % CI 0.63-0.76; P = 0.0009]. A HER2-positive status was associated with an increased risk of ALN involvement at diagnosis compared to a HER2-negative status (OR 1.37; 95 % CI 1.24-1.50; P < 0.0001). An interaction between HER2 and HR was observed, with a HER2-positive status significantly associated with ALN involvement at the time of diagnosis only in HR-negative patients (P < 0.0001). The triple-negative breast cancer (TNBC) patients showed a significantly reduced risk of ALN involvement at the time of diagnosis compared to patients with HR-positive/HER2-negative tumors (OR 0.55; 95 % CI 0.49-0.62; P < 0.0001). The HR and HER2 statuses are significantly associated with ALN involvement at the time of diagnosis. Despite the poor prognosis, TNBC patients have a reduced risk of ALN involvement at the time of diagnosis compared to patients with other subtypes, when adjusting for other risk factors. This may indicate that TNBC tends to spread hematogenously rather than lymphogenously.

Histopathological Predictors of Axillary Lymph Node Metastases in Patients with Breast Cancer.

BACKGROUND: A tumor 30 mm or less in diameter is a standard candidate for breast conserving surgery (BCS) in Japan. Axillary lymph node metastases (ALNM) is the most important prognostic factor for survival in patients with breast cancer, but the role of axillary node dissection has been controversial. Histopathological predictive factors of axillary lymph node involvement have not been established. The purpose of this study was to determine the association between the incidence of ALNM and histopathological factors by univariate and multivariate analysis METHODS: Sixty-five patients with noninvasive ductal carcinoma, and 993 patients with tumors 30 mm or less in diameter who underwent axillary dissection between 1988 and 1997 at our institute were reviewed. The association between ALNM and 13 histopathological factors (size, age, histological subtype, histological invasiveness, lymphatic invasion, vascular invasion, macroscopic classification, histological daughter mass, ductal spread, ER, PgR, p-53, and c-erbB-2) were analyzed by univariate and, when significant, by multivariate analysis. RESULTS: Only one patient with noninvasive ductal carcinoma had ALNM, and 33.1% of 993 patients with a tumor 30 mm or less in size had ALNM. Multivariate analysis identified six factors as independent predictors for ALNM: lymphatic invasion, size, histological invasiveness, macroscopic classification, age and histological daughter mass. CONCLUSION: Axillary lymph node dissection can be omitted in patients with noninvasive ductal carcinoma. Histopathological features of tumors 30 mm or less in diameter can be used to estimate the risk of ALNM, and routine axillary node dissection might be spared in selected patients at minimal risk of ALNM, if the treatment decision is not influenced by lymph node status, such as in elderly patients.