Primary Tumor Characteristics Predict Sentinel Lymph Node Macrometastasis in Breast Cancer

Department of Surgery, University of California, San Francisco, San Francisco, California, United States
The Breast Journal (Impact Factor: 1.43). 01/2006; 11(5):338-43. DOI: 10.1111/j.1075-122X.2005.00043.x
Source: PubMed

ABSTRACT Selective sentinel lymphadenectomy (SSL) is rapidly becoming the standard of care in the surgical management of patients with early breast cancer. Sentinel lymph node macrometastasis has been well documented in the literature to have a higher risk of nonsentinel node tumor involvement when compared to micrometastasis. The aim of our study was to determine the primary tumor characteristics associated with sentinel node macrometastasis that will allow us to preoperatively determine this subgroup of patients at risk. This study was a retrospective review of 644 patients who underwent successful SSL as part of their surgical treatment of breast cancer at the University of California San Francisco Carol Franc Buck Breast Care Center from November 1997 to August 2003. All patients underwent preoperative lymphoscintigraphy followed by wide excision or mastectomy and sentinel lymphadenectomy with or without axillary lymph node dissection. One hundred twenty-two patients had positive sentinel nodes on histology. Micrometastasis was present in 43 of these patients and macrometastasis in the remaining 79. Statistical analysis showed that a tumor size greater than 15 mm, poor tubule formation by the tumor cells, and lymphovascular invasion were significantly associated with sentinel node macrometastasis. A high mitotic count showed a trend but was not significant in our study. Patients with a tumor size greater than 15 mm, poor tubule formation, and lymphovascular invasion are at risk of having sentinel node macrometastasis. These patients can be identified preoperatively based on imaging and biopsy criteria, allowing the option of selective intraoperative pathologic evaluation of the sentinel node and immediate completion axillary dissection as necessary.

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    ABSTRACT: Background Tumor characteristics was sought to be related to axillary lymph node metastasis (ALNM), the paramount prognostic factor in patients with invasive breast cancer. This study was aimed to identify the ALNM-associated tumor characteristics and to determine the predictive clinical pathway. Material/Methods Data from 1325 patients diagnosed with invasive breast cancer between January 2004 and January 2010 were retrospectively reviewed. The structure equation model (SEM) was used to build the predictive clinical pathway. Results Among the factors found in the final model, the status of human epidermal growth factor receptor 2 is the primary influence on ALNM through histology grade (β=0.18), followed by tumor size (β=0.16). Tumor size was highly relevant to lymphovascular invasion (LVI) and influenced ALNM through LVI (β=0.26), the strongest predictor of ALNM in the final model (β=0.46) and the highest risk of ALNM (odds ratio=9.282; 95% confidence interval: 7.218–11.936). Conclusions The structure equation model presented the relation of these important predictors, and might help physicians to assess axillary nodal condition and appropriate surgical procedures.
    Medical science monitor: international medical journal of experimental and clinical research 07/2014; 20:1155-61. DOI:10.12659/MSM.890491 · 1.22 Impact Factor
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    ABSTRACT: The negative sentinel lymph node (SLN) biopsy avoids conventional axillary dissection in patients with breast cancer with clinically negative axilla. Despite negative SLN, there is a risk of leaving involved non-SLN behind in the axilla. We investigated the predictive power of tumor characteristics for non-SLN metastasis. Lymphatic mapping with blue dye method for SLN biopsy and level 1-2 axillary dissections were performed to establish axillary status in 59 patients with T1 and T2 breast cancer and clinically negative axilla. Tumor's characteristics were histopathologically established to assess their association with non-SLN metastasis. The axilla was malignant in 23 (39%) patients. The SLN alone was metastatic in 10, both SLN and non-SLN in 9, and non-SLN alone in 4 (7%) patients. The false negative rate for SLN biopsy was 10% in our series. The rate of positive non-SLN was found as 0% in T1a-b, 19% in T1c, and 40% in T2 tumors (p=0.035). Lymphovascular invasion was positive in 14 (61%) patients with axillary metastasis (p<0.001), and in 10 (77%) patients with non-SLN involvement (p<0.001). We concluded that there was a small risk of involved non-SLN despite negative SLN. Tumor size (near or greater than 2 cm) was significantly associated with non-SLN metastasis. Peritumoral lymphovascular invasion was a positive predictor of the metastatic involvement in non-SLNs.
    06/2011; 14(2):124-8. DOI:10.4048/jbc.2011.14.2.124
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    ABSTRACT: Background Axillary lymph nodes (ALN) are the most commonly involved site of disease in breast cancer that has spread outside the primary lesion. Although sentinel node biopsy is a reliable way to manage ALN, there are still no good methods of predicting ALN status before surgery. Since morbidity in breast cancer surgery is predominantly related to ALN dissection, predictive models for lymph node involvement may provide a way to alert the surgeon in subgroups of patients. Material and Methods A total of 1325 invasive breast cancer patients were analyzed using tumor biological parameters that included age, tumor size, grade, estrogen receptor, progesterone receptor, lymphovascular invasion, and HER2, to test their ability to predict ALN involvement. A support vector machine (SVM) was used as a classification model. The SVM is a machine-learning system developed using statistical learning theories to classify data points into 2 classes. Notably, SVM models have been applied in bioinformatics. Results The SVM model correctly predicted ALN metastases in 74.7% of patients using tumor biological parameters. The predictive ability of luminal A, luminal B, triple negative, and HER2 subtypes using subgroup analysis showed no difference, and this predictive performance was inferior, with only 60% accuracy. Conclusions With an SVM model based on clinical pathologic parameters obtained in the primary tumor, it is possible to predict ALN status in order to alert the surgeon about breast cancer counseling and in decision-making for ALN management.
    Medical science monitor: international medical journal of experimental and clinical research 04/2014; 20:577-81. DOI:10.12659/MSM.890345 · 1.22 Impact Factor