A predictive tool to estimate the risk of axillary metastases in breast cancer patients with negative axillary ultrasound.

BACKGROUND: Sentinel node biopsy (SNB) is the "gold standard" in axillary staging in clinically node-negative breast cancer patients. However, axillary treatment is undergoing a paradigm shift and studies are being conducted on whether SNB may be omitted in low-risk patients. The purpose of this study was to evaluate the risk factors for axillary metastases in breast cancer patients with negative preoperative axillary ultrasound. METHODS: A total of 1,395 consecutive patients with invasive breast cancer and SNB formed the original patient series. A univariate analysis was conducted to assess risk factors for axillary metastases. Binary logistic regression analysis was conducted to form a predictive model based on the risk factors. The predictive model was first validated internally in a patient series of 566 further patients and then externally in a patient series of 2,463 patients from four other centers. All statistical tests were two-sided. RESULTS: A total of 426 of the 1,395 (30.5 %) patients in the original patient series had axillary lymph node metastases. Histological size (P < 0.001), multifocality (P < 0.001), lymphovascular invasion (P < 0.001), and palpability of the primary tumor (P < 0.001) were included in the predictive model. Internal validation of the model produced an area under the receiver operating characteristics curve (AUC) of 0.731 and external validation an AUC of 0.79. CONCLUSIONS: We present a predictive model to assess the patient-specific probability of axillary lymph node metastases in patients with clinically node-negative breast cancer. The model performs well in internal and external validation. The model needs to be validated in each center before application to clinical use.

International multicenter tool to predict the risk of four or more tumor-positive axillary lymph nodes in breast cancer patients with sentinel node macrometastases.

Recently, many centers have omitted routine axillary lymph node dissection (ALND) after metastatic sentinel node biopsy in breast cancer due to a growing body of literature. However, existing guidelines of adjuvant treatment planning are strongly based on axillary nodal stage. In this study, we aim to develop a novel international multicenter predictive tool to estimate a patient-specific risk of having four or more tumor-positive axillary lymph nodes (ALN) in patients with macrometastatic sentinel node(s) (SN). A series of 675 patients with macrometastatic SN and completion ALND from five European centers were analyzed by logistic regression analysis. A multivariate predictive model was created and validated internally by 367 additional patients and then externally by 760 additional patients from eight different centers. All statistical tests were two-sided. Prevalence of four or more tumor-positive ALN in each center's series (P = 0.010), number of metastatic SNs (P < 0.0001), number of negative SNs (P = 0.003), histological size of the primary tumor (P = 0.020), and extra-capsular extension of SN metastasis (P < 0.0001) were included in the predictive model. The model's area under the receiver operating characteristics curve was 0.766 in the internal validation and 0.774 in external validation. Our novel international multicenter-based predictive tool reliably estimates the risk of four or more axillary metastases after identifying macrometastatic SN(s) in breast cancer. Our tool performs well in internal and external validation, but needs to be further validated in each center before application to clinical use.

Multicentre validation of different predictive tools of non-sentinel lymph node involvement in breast cancer.

Sentinel lymph node (SN) biopsy offers the possibility of selective axillary treatment for breast cancer patients, but there are only limited means for the selective treatment of SN-positive patients. Eight predictive models assessing the risk of non-SN involvement in patients with SN metastasis were tested in a multi-institutional setting. Data of 200 consecutive patients with metastatic SNs and axillary lymph node dissection from each of the 5 participating centres were entered into the selected non-SN metastasis predictive tools. There were significant differences between centres in the distribution of most parameters used in the predictive models, including tumour size, type, grade, oestrogen receptor positivity, rate of lymphovascular invasion, proportion of micrometastatic cases and the presence of extracapsular extension of SN metastasis. There were also significant differences in the proportion of cases classified as having low risk of non-SN metastasis. Despite these differences, there were practically no such differences in the sensitivities, specificities and false reassurance rates of the predictive tools. Each predictive tool used in clinical practice for patient and physician decision on further axillary treatment of SN-positive patients may require individual institutional validation; such validation may reveal different predictive tools to be the best in different institutions.

The hTERT mRNA in plasma samples of early breast cancer patients, non-cancer patients and healthy individuals.

One of the most important changes, which make cancer cells immortal, is reactivation of the telomerase enzyme. Human telomerase is composed of at least two subunits, hTERT and hTR. Many investigators have already detected telomerase mRNA in bodily fluids. The first aim of our study was to find out if there is a difference in the appearance frequency of detectable hTERT mRNA in plasma of early breast cancer patients, non-cancer patients and healthy individuals. The second aim was to determine whether surgical removal of the tumor affects the presence of hTERT mRNA in plasma of early breast cancer patients. 87 patients with early breast cancer, 22 non-cancer patients and 21 healthy individuals were included in the study. From early breast cancer patients, two blood samples were collected, the first prior and the second 24 hours after the surgical removal of the tumor. From other individuals one blood sample was collected. The presence or absence of hTERT mRNA was determined from all blood samples. 47% of early breast cancer patients, 32% of non-cancer patients and 5% of healthy individuals tested positive for the presence of hTERT mRNA in plasma. The difference between early breast cancer patients and healthy individuals was statistically significant (p<0,001). Among early breast cancer patients, 26% were positive for the presence of plasma hTERT mRNA before and after the surgical removal of the tumor, 21% were positive before and negative after, 36% were negative before and after and 17% were negative before and positive after the surgical removal of the tumor. In conclusion, we found statistically significant difference of hTERT mRNA presence in plasma of early breast cancer patients when compared to healthy individuals. Second, we found that hTERT mRNA in plasma of early breast cancer patients is affected by the surgical removal of the tumor.