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首页> 外文期刊>Breast care >A new and simple predictive formula for non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes, and validation of 3 different nomograms in Turkish breast cancer patients
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A new and simple predictive formula for non-sentinel lymph node metastasis in breast cancer patients with positive sentinel lymph nodes, and validation of 3 different nomograms in Turkish breast cancer patients

机译：前哨淋巴结阳性的乳腺癌患者非前哨淋巴结转移的新的简单预测公式，以及土耳其乳腺癌患者3种不同列线图的验证

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Background: Nomogram accuracies for predicting non-sentinel lymph node (SLN) involvement vary between different patient populations. Our aim is to put these nomograms to test on our patient population and determine our individual predictive parameters affecting SLN and non-SLN involvement. Patients and Methods: Data from 932 patients was analyzed. Nomogram values were calculated for each patient utilizing MSKCC, Tenon, and MHDF models. Moreover, using our own patient- and tumor-depended parameters, we established a unique predictivity formula for SLN and non-SLN involvement. Results: The calculated area under the curve (AUC) values for MSKCC, Tenon, and MHDF models were 0.727 (95% confidence interval (CI) 0.64-0.8), 0.665 (95% CI 0.59-0.73), and 0.696 (95% CI 0.59-0.79), respectively. Cerb-2 positivity (p = 0.004) and size of the metastasis in the lymph node (p = 0.006) were found to correlate with non-SLN involvement in our study group. The AUC value of the predictivity formula established using these parameters was 0.722 (95% CI 0.63-0.81). Conclusion: The most accurate nomogram for our patient group was the MSKCC nomogram. Our unique predictivity formula proved to be as equally effective and competent as the MSKCC nomogram. However, similar to other nomograms, our predictivity formula requires future validation studies.
机译：背景：用于预测非前哨淋巴结（SLN）受累的诺法图准确性在不同患者人群之间有所不同。我们的目标是将这些列线图放在我们的患者人群上进行测试，并确定影响SLN和非SLN参与的个人预测参数。患者和方法：分析了932位患者的数据。使用MSKCC，Tenon和MHDF模型为每位患者计算Nomogram值。此外，使用我们自己的患者和肿瘤相关参数，我们为SLN和非SLN参与建立了独特的预测公式。结果：对于MSKCC，Tenon和MHDF模型，曲线下的计算面积（AUC）值为0.727（95％置信区间（CI）0.64-0.8），0.665（95％CI 0.59-0.73）和0.696（95％） CI 0.59-0.79）。在我们的研究组中，Cerb-2阳性（p = 0.004）和淋巴结转移的大小（p = 0.006）与非SLN受累相关。使用这些参数建立的可预测性公式的AUC值为0.722（95％CI 0.63-0.81）。结论：对于我们的患者组，最准确的列线图是MSKCC列线图。我们独特的预测公式被证明与MSKCC列线图一样有效和胜任。但是，类似于其他列线图，我们的预测公式需要将来的验证研究。

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