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Predictors of Sentinel Lymph Node Metastasis in Postoperatively Upgraded Invasive Breast Carcinoma Patients.

ABSTRACT: Sentinel lymph node (SLN) biopsy (SLNB) usually need not be simultaneously performed with breast-conserving surgery (BCS) for patients diagnosed with ductal carcinoma in situ (DCIS) by preoperative core needle biopsy (CNB), but must be performed once there is invasive carcinoma (IC) found postoperatively. This study aimed to investigate the factors contributing to SLN metastasis in underestimated IC patients with an initial diagnosis of DCIS by CNB. We retrospectively reviewed 1240 consecutive cases of DCIS by image-guided CNB from January 2010 to December 2017 and identified 316 underestimated IC cases with SLNB. Data on clinical characteristics, radiologic features, and final pathological findings were examined. Twenty-three patients (7.3%) had SLN metastasis. Multivariate analysis indicated that an IC tumor size > 0.5 cm (odds ratio: 3.11, p = 0.033) and the presence of lymphovascular invasion (odds ratio: 32.85, p < 0.0001) were independent risk predictors of SLN metastasis. In the absence of any predictors, the incidence of positive SLNs was very low (2.6%) in the total population and extremely low (1.3%) in the BCS subgroup. Therefore, omitting SLNB may be an acceptable option for patients who initially underwent BCS without risk predictors on final pathological assessment. Further prospective studies are necessary before clinical application.

SUBMITTER: Yu CC

PROVIDER: S-EPMC8392104 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background The rate of breast-conserving surgery is very low in China, compared with that in developed countries; most breast cancer patients receive mastectomy. It is great important to explore the possibility of omitting axillary lymph node dissection (ALND) in early-stage breast cancer patients with 1 or 2 positive sentinel lymph nodes (SLNs) in China. The aim of this study was to develop a nomogram based on elastography for the prediction of the risk of non-SLN (NSLN) metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. Methods A total of 601 breast cancer patients were initially recruited. According to the inclusion and exclusion criteria, 118 early-stage breast cancer patients with 1 or 2 positive SLNs were finally enrolled and were assigned to the training cohort (n=82) and the validation cohort (n=36), respectively. In the training cohort, the independent predictors were screened by logistic regression analysis and then were used to conducted the nomogram for the prediction of NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The calibration curves, concordance index (C-index), the area under the receiver operating characteristic (ROC) curve (AUC), and Decision curve analysis (DCA) were used to verified the performance of the nomogram. Results The multivariable analysis showed that the enrolled patients with positive HER2 expression (OR=6.179, P=0.013), Ki67≥14% (OR=8.976, P=0.015), larger lesion size (OR=1.038, P=0.045), and higher Emean (OR=2.237, P=0.006) were observed to be the independent factors of NSLN metastasis. Based on the above four independent predictors, a nomogram was conducted to predict the risk of the NSLN metastasis in early-stage breast cancer patients with 1 or 2 positive SLNs. The nomogram showed good discrimination in the prediction of NSLN metastasis, with bias-corrected C-index of 0.855 (95% CI, 0.754-0.956) and 0.853 (95% CI, 0.724-0.983) in the training and validation cohorts, respectively. Furthermore, the AUC was 0.877 (95%CI: 0.776- 0.978) and 0.861 (95%CI: 0.732-0.991), respectively, indicating a good performance of the nomogram. The calibration curve suggested a satisfactory agreement between the predictive and actual risk in both the training (χ2 = 11.484, P=0.176, HL test) and validation (χ2 = 6.247, p = 0.620, HL test) cohorts, and the obvious clinical nets were revealed by DCA. Conclusions We conducted a satisfactory nomogram model to evaluate the risk of NSLN metastasis in early-stage breast cancer patients with 1 or 2 SLN metastases. This model could be considered as an ancillary tool to help such patients to be selectively exempted from ALND.

Project description:Purpose: Sentinel lymph node biopsy (SLNB) has emerged as the preferred standard procedure in patients with breast cancer, melanoma and other types of cancer. Herein, we developed a method to intra-operatively map SLNs and differentiate tumor metastases within SLNs at the same time, with the aim to provide more accurate and real-time intraoperative guidance. Experimental Design: Hyaluronic acid (HA), a ligand of lymphatic vessel endothelial hyaluronan receptor (LYVE)-1, is employed as a SLN mapping agent after being conjugated with a near-infrared fluorophore (Cy5.5). Different sized HAs (5, 10 and 20K) were tested in normal mice and mice with localized inflammation to optimize LN retention time and signal to background ratio. Cetuximab, an antibody against epidermal growth factor receptor (EGFR), and trastuzumab, an antibody against human epidermal growth factor receptor 2 (HER2), were labeled with near-infrared fluorophore (IRDye800) for detecting metastatic tumors. LN metastasis model was developed by hock injection of firefly luciferase engineered human head neck squamous carcinoma cancer UM-SCC-22B cells or human ovarian cancer SKOV-3 cells. The metastases within LNs were confirmed by bioluminescence imaging (BLI). IRDye800-Antibodies were intravenously administered 24 h before local administration of Cy5.5-HA. Optical imaging was then performed to identify nodal metastases. Results: Binding of HA with LYVE-1 was confirmed by ELISA and fluorescence staining. HA with a size of 10K was chosen based on the favorable migration and retention profile. After sequential administration of IRDye800-antibodies intravenously and Cy5.5-HA locally to a mouse model with LN metastases and fluorescence optical imaging, partially metastasized LNs were successfully distinguished from un-metastasized LNs and fully tumor occupied LNs, based on the different signal patterns. Conclusions: Fluorophore conjugated HA is a potential lymphatic mapping agent for SLNB. Dual-tracer imaging with the combination of lymphatic mapping agents and tumor targeting agents can identify tumor metastases within SLNs, thus may provide accurate and real-time intra-operative guidance to spare the time spent waiting for a biopsy result.

Project description:BackgroundCurrent practice is to perform a completion axillary lymph node dissection (ALND) for breast cancer patients with tumor-involved sentinel lymph nodes (SLNs), although fewer than half will have non-sentinel node (NSLN) metastasis. Our goal was to develop new models to quantify the risk of NSLN metastasis in SLN-positive patients and to compare predictive capabilities to another widely used model.MethodsWe constructed three models to predict NSLN status: recursive partitioning with receiver operating characteristic curves (RP-ROC), boosted Classification and Regression Trees (CART), and multivariate logistic regression (MLR) informed by CART. Data were compiled from a multicenter Northern California and Oregon database of 784 patients who prospectively underwent SLN biopsy and completion ALND. We compared the predictive abilities of our best model and the Memorial Sloan-Kettering Breast Cancer Nomogram (Nomogram) in our dataset and an independent dataset from Northwestern University.Results285 patients had positive SLNs, of which 213 had known angiolymphatic invasion status and 171 had complete pathologic data including hormone receptor status. 264 (93%) patients had limited SLN disease (micrometastasis, 70%, or isolated tumor cells, 23%). 101 (35%) of all SLN-positive patients had tumor-involved NSLNs. Three variables (tumor size, angiolymphatic invasion, and SLN metastasis size) predicted risk in all our models. RP-ROC and boosted CART stratified patients into four risk levels. MLR informed by CART was most accurate. Using two composite predictors calculated from three variables, MLR informed by CART was more accurate than the Nomogram computed using eight predictors. In our dataset, area under ROC curve (AUC) was 0.83/0.85 for MLR (n = 213/n = 171) and 0.77 for Nomogram (n = 171). When applied to an independent dataset (n = 77), AUC was 0.74 for our model and 0.62 for Nomogram. The composite predictors in our model were the product of angiolymphatic invasion and size of SLN metastasis, and the product of tumor size and square of SLN metastasis size.ConclusionWe present a new model developed from a community-based SLN database that uses only three rather than eight variables to achieve higher accuracy than the Nomogram for predicting NSLN status in two different datasets.

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Predictors of Sentinel Lymph Node Metastasis in Postoperatively Upgraded Invasive Breast Carcinoma Patients.

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