New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients.
New models and online calculator for predicting non-sentinel lymph node status in sentinel lymph node positive breast cancer patients.
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DOI：
10.1186/1471-2407-8-66
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发表时间：
2008-03-04
期刊：
影响因子：
3.8
通讯作者：
Bay Area SLN Study
中科院分区：
文献类型：
--
作者：
Kohrt HE;Olshen RA;Bermas HR;Goodson WH;Wood DJ;Henry S;Rouse RV;Bailey L;Philben VJ;Dirbas FM;Dunn JJ;Johnson DL;Wapnir IL;Carlson RW;Stockdale FE;Hansen NM;Jeffrey SS;Bay Area SLN Study
目前的做法是对肿瘤累及前哨淋巴结（sln）的乳腺癌患者进行完全性腋窝淋巴结清扫（ALND），尽管只有不到一半的患者会发生非前哨淋巴结（NSLN）转移。我们的目标是开发新的模型来量化sln阳性患者的NSLN转移风险，并将其预测能力与另一种广泛使用的模型进行比较。我们构建了三个预测NSLN状态的模型：基于受试者工作特征曲线的递归划分（RP-ROC）、增强分类与回归树（CART）和基于CART的多元逻辑回归（MLR）。数据来自北加州和俄勒冈州的多中心数据库，其中包括784例预期接受SLN活检和完全性ALND的患者。我们比较了我们的最佳模型与我们的数据集和西北大学的独立数据集中的Memorial Sloan-Kettering Breast Cancer Nomogram （Nomogram）的预测能力。285例患者有sln阳性，其中213例已知血管淋巴浸润状态，171例有完整的病理数据，包括激素受体状态。264例（93%）患者患有有限的SLN疾病（微转移，70%，或分离的肿瘤细胞，23%）。所有sln阳性患者中101例（35%）有肿瘤累及的nsn。三个变量（肿瘤大小、血管淋巴浸润和SLN转移大小）预测了我们所有模型的风险。RP-ROC和提升CART将患者分为四个危险级别。CART告知的MLR最准确。使用由三个变量计算的两个复合预测因子，CART通知的MLR比使用八个预测因子计算的Nomogram更准确。在我们的数据集中，MLR （n = 213/n = 171）的ROC曲线下面积（AUC）为0.83/0.85,Nomogram （n = 171）的AUC为0.77。当应用于独立数据集（n = 77）时，我们的模型的AUC为0.74，Nomogram为0.62。我们模型中的复合预测因子是血管淋巴浸润与SLN转移大小的乘积，以及肿瘤大小与SLN转移大小的平方的乘积。我们提出了一个基于社区的SLN数据库开发的新模型，该模型仅使用3个变量而不是8个变量，在预测两个不同数据集的NSLN状态时达到了比Nomogram更高的准确性。
Current 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. We 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. 285 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. We 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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影响因子：
3.7
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通讯作者：
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