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How Many Sentinel Lymph Nodes Are Enough for Accurate Axillary Staging in T1-2 Breast Cancer?

机译:T1-2乳腺癌中多少个前哨淋巴结足以进行准确的腋窝分期?

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Purpose During a sentinel lymph node biopsy (SLNB) for breast cancer, the appropriate number of sentinel lymph nodes (SLNs) to be removed for accurate axillary staging is still controversial. We hypothesized that there might be an optimal threshold number of SLNs. We investigated how many SLNs should be removed to achieve an acceptable accuracy and ensure minimal morbidity. Methods We reviewed data of 328 patients with invasive breast cancer who underwent SLNB followed by complete level I and II axillary dissection between January 2004 and December 2005. The false negative rate (FNR) and accuracy of SLNB according to the number of removed SLNs were evaluated. Results The mean number of SLNs removed was 3.0 (range, 1-14), and that of total retrieved axillary lymph nodes was 17.5 (range, 10-40). In total, 111 (33.8%) patients had positive nodes on the permanent pathological report. Among them, 12 patients had negative SLNs; thus, the overall FNR of SLNB was 10.8% (12/111) and the accuracy was 96.3% (316/328). The FNR was 26.6% for a single SLN, 8.0% for two, and 11.1% for three. In cases where four or more SLNs were removed, the FNR decreased to 0% and accuracy reached 100%. Conclusion Our data suggest that a SLNB should not only remove one or two of the hottest node(s) when other hot nodes exist. We also suggest that four might be an optimal threshold number of SLNs to be removed and that removal of more than four SLNs does not improve axillary staging accuracy.
机译:目的在乳腺癌前哨淋巴结活检(SLNB)期间,为准确的腋窝分期而取下适当数量的前哨淋巴结(SLN)仍存在争议。我们假设可能存在最佳阈值数量的SLN。我们调查了应去除多少SLN,以达到可接受的准确性并确保最低的发病率。方法我们回顾性分析了2004年1月至2005年12月间328例行SLNB并完成I级和II级腋窝淋巴结清扫术的浸润性乳腺癌患者的数据。根据去除的SLN数量评估SLNB的假阴性率(FNR)和准确性。结果去除的SLN平均数为3.0(范围为1-14),而总腋窝淋巴结清扫的平均数目为17.5(范围为10-40)。永久性病理报告中共有111例(33.8%)患者具有阳性淋巴结。其中12例SLN阴性。因此,SLNB的整体FNR为10.8%(12/111),准确度为96.3%(316/328)。单个SLN的FNR为26.6%,两个为8.0%,三个为11.1%。如果去除了四个或更多SLN,则FNR降至0%,准确度达到100%。结论我们的数据表明,当存在其他热节点时,SLNB不仅应删除一个或两个最热的节点。我们还建议,四个可能是要去除的SLN的最佳阈值数量,并且去除四个以上的SLN不会提高腋窝分期的准确性。

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