Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram
Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram
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DOI：
10.1007/s10549-012-2219-x
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发表时间：
2012-10-01
影响因子：
3.8
通讯作者：
Wu, Jiong
中科院分区：
文献类型：
--
作者：
Chen, Jia-ying;Chen, Jia-jian;Wu, Jiong
我们评估了MSKCC诺模图在预测中国乳腺癌SLN转移中的表现。一个新的模型（SCH诺模图）与临床相关的变量和可能的优势。数据收集自2005年3月至2011年11月期间成功进行SLN活检的1，545例患者。我们在建模和验证组中验证了MSKCC诺模图。对模型组1,000例患者的临床和SLN活检病理特征进行多因素Logistic回归分析，以预测乳腺癌SLN转移的存在。SCH诺模图由逻辑回归模型创建，随后应用于545例连续SLN活检。通过多因素分析，年龄、肿瘤大小、肿瘤部位、肿瘤类型和淋巴管浸润是SLN转移的独立预测因素。然后使用这五个变量开发SCH列线图。与MSKCC列线图（建模组中的AUC为0.7105）相比，新模型是准确和有区别的（建模组中的AUC为0.7649）。验证人群中SCH列线图的ROC曲线下面积为0.7587。各种十分位数的实际概率趋势与预测概率相当。SCH列线图的假阴性率分别为1.67%、3.54%和8.20%，预测概率临界点为5%、10%和15%。与MSKCC诺模图相比，SCH诺模图具有更好的AUC和更少的变量，并且对于低概率亚组具有更低的假阴性率。SCH列线图可以作为一个更可接受的临床工具，在术前讨论与患者，特别是极低风险的患者。当应用于这些患者时，SCH列线图可用于安全地避免SLN手术。应在不同患者人群中验证列线图，以证明其可重复性。
We assessed the MSKCC nomogram performance in predicting SLN metastases in a Chinese breast cancer population. A new model (the SCH nomogram) was developed with clinically relevant variables and possible advantages. Data were collected from 1,545 patients who had a successful SLN biopsy between March 2005 and November 2011. We validated the MSKCC nomogram in the modeling and validation group. Clinical and pathologic features of SLN biopsy in modeling group of 1,000 patients were assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The SCH nomogram was created from the logistic regression model and subsequently applied to 545 consecutive SLN biopsies. By multivariate analysis, age, tumor size, tumor location, tumor type, and lymphovascular invasion were identified as independent predictors of SLN metastasis. The SCH nomogram was then developed using the five variables. The new model was accurate and discriminating (with an AUC of 0.7649 in the modeling group) compared to the MSKCC nomogram (with an AUC of 0.7105 in the modeling group). The area under the ROC curve for the SCH nomogram in the validation population is 0.7587. The actual probability trends for the various deciles were comparable to the predicted probabilities. The false-negative rates of the SCH nomogram were 1.67, 3.54, and 8.20 % for the predicted probability cut-off points of 5, 10, and 15 %, respectively. Compared with the MSKCC nomogram, the SCH nomogram has a better AUC with fewer variables and has lower false-negative rates for the low-probability subgroups. The SCH nomogram could serve as a more acceptable clinical tool in preoperative discussions with patients, especially very-low-risk patients. When applied to these patients, the SCH nomogram could be used to safely avoid a SLN procedure. The nomogram should be validated in various patient populations to demonstrate its reproducibility.