Dataset Information

Precise intraoperative sentinel lymph node biopsies guided by lymphatic drainage in breast cancer.

ABSTRACT: The purpose of this study was to present a novel surgical method for intraoperative precise sentinel lymph node biopsy (SLNB) and to determine its clinical efficacy and sensitivity in breast cancer patients. The sentinel lymph nodes (SLNs) were preoperatively evaluated by axillary ultrasound. The intraoperative detection of SLNs was guided by lymphatic drainage pathway. The lymphatic vessels and SLNs were visualized. During operation, we searched for all the true SLNs (trSLNs), para-SLNs (paSLNs) and post-SLNs (poSLNs) followed lymphatic drainage ducts. After precisely locating the lymphatic channels and lymph node, all the lymph nodes that firstly receive lymphatic drainage are designated as trSLNs. We precisely distinguished the trSLNs, paSLNs and poSLNs. We found the average number of trSLNs ranged from1 to 6. In addition, we assessed the novel technique in a total of 125 breast cancer patients. trSLNs were successfully identified in all patients (detection rate: 100 %). The accuracy of trSLNs is 99.2%. Data from our study strongly suggest that our method is a feasible and effective for the detection of precise trSLNs in breast cancer with real-time observations. (ClinicalTrials.gov number, NCT02651142).

SUBMITTER: Li X

PROVIDER: S-EPMC5609903 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Precise intraoperative sentinel lymph node biopsies guided by lymphatic drainage in breast cancer.

Li Xiaoyan X Chen Sisi S Jiang Liyu L Kong Xiaoli X Ma Tingting T Xu Hong H Yang Qifeng Q

Oncotarget 20170627 38

The purpose of this study was to present a novel surgical method for intraoperative precise sentinel lymph node biopsy (SLNB) and to determine its clinical efficacy and sensitivity in breast cancer patients. The sentinel lymph nodes (SLNs) were preoperatively evaluated by axillary ultrasound. The intraoperative detection of SLNs was guided by lymphatic drainage pathway. The lymphatic vessels and SLNs were visualized. During operation, we searched for all the true SLNs (trSLNs), para-SLNs (paSLNs ...[more]

PMID: 28968971

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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.

Dataset Information

Precise intraoperative sentinel lymph node biopsies guided by lymphatic drainage in breast cancer.

Publications

Precise intraoperative sentinel lymph node biopsies guided by lymphatic drainage in breast cancer.

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