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验证列线图预测北非突尼斯乳腺癌前哨淋巴结受累患者非前哨淋巴结转移风险的能力。

Validation of nomograms to predict the risk of non-sentinels lymph node metastases in North African Tunisian breast cancer patients with sentinel node involvement.

机构信息

O&G Department, Farhat Hached University Teaching Hospital, Boulevard M Karoui, Sousse, Tunisia.

出版信息

Breast. 2011 Feb;20(1):26-30. doi: 10.1016/j.breast.2010.07.006. Epub 2010 Aug 21.

Abstract

INTRODUCTION

In approximately half of patients with breast cancer and lymph node metastases, the sentinel node (SN) is the only involved axillary node. Scoring systems have been developed to predict probability of non-SN metastases among those with a positive SN. The goal of the present study was to determine whether the five models (Memorial Sloan-Kettering Cancer Center (MSKCC), Stanford, Tenon, Cambridge and the Turkish model) accurately predicted non-SN involvement in a North African Tunisian population.

METHODS

During a five years period, we identified 87 cases of invasive breast cancer which had a positive SN biopsy and complete axillary lymph node dissection (CALND). The MSKCC, Stanford, Tenon, Cambridge and Turkish models were tested. Results were compared using the area under the curve (AUC) of the receiver operating characteristics for each model. False negative and false positive rates were also calculated.

RESULTS

The AUC of the MSKCC, Stanford, Tenon, Cambridge and Turkish models was respectively 0.73 (95% CI 0.6-0.86), 0.76 (95% CI 0.65-0.87), 0.75 (95% CI 0.63-0.87), 0.67 (95% CI 0.53-0.82) and 0.75 (95% CI 0.63-0.88). The threshold for a 10% false negative of non-SN involvement was obtained with a cut off value of 10% for MSKCC, 25% for Stanford, a score of 3 for Tenon, 6% for Cambridge and 15% for the Turkish nomogram.

CONCLUSIONS

Meaningfully applied to our population, although AUC values had overlapping of 95% confidence intervals but combined our data suggest that the Stanford nomogram may be the most accurate. Before prospective trials validate these nomograms, CALND remains the standard for patients who have SN metastases.

摘要

介绍

在大约一半的乳腺癌和淋巴结转移患者中,前哨淋巴结(SN)是唯一受影响的腋窝淋巴结。已经开发了评分系统来预测 SN 阳性患者中 SN 以外淋巴结转移的概率。本研究的目的是确定在北非突尼斯人群中,五个模型(纪念斯隆凯特琳癌症中心(MSKCC)、斯坦福、Tenon、剑桥和土耳其模型)是否能准确预测非 SN 受累。

方法

在五年期间,我们确定了 87 例 SN 活检阳性且腋窝淋巴结清扫(CALND)完整的浸润性乳腺癌病例。测试了 MSKCC、斯坦福、Tenon、剑桥和土耳其模型。使用每个模型的接收者操作特性曲线(ROC)的曲线下面积(AUC)比较结果。还计算了假阴性和假阳性率。

结果

MSKCC、斯坦福、Tenon、剑桥和土耳其模型的 AUC 分别为 0.73(95%CI 0.6-0.86)、0.76(95%CI 0.65-0.87)、0.75(95%CI 0.63-0.87)、0.67(95%CI 0.53-0.82)和 0.75(95%CI 0.63-0.88)。非 SN 受累 10%假阴性的阈值为 MSKCC 的截断值为 10%,斯坦福的截断值为 25%,Tenon 的截断值为 3 分,剑桥的截断值为 6%,土耳其的截断值为 15%。

结论

尽管 AUC 值的 95%置信区间有重叠,但综合我们的数据表明,斯坦福模型可能是最准确的。在前瞻性试验验证这些预测模型之前,CALND 仍然是 SN 转移患者的标准治疗方法。

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