Department of Breast Surgery, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
World J Surg Oncol. 2022 Sep 28;20(1):314. doi: 10.1186/s12957-022-02779-9.
There are currently no scoring-type predictive models using only easily available pre- and intraoperative data developed for assessment of the risk of advanced axillary lymph node metastasis (ALNM) in patients with breast cancer with metastatic sentinel lymph nodes (SLNs). We aimed to develop and validate a scoring system using only pre- and intraoperative data to distinguish between non-advanced (≤ 3 lymph nodes) and advanced (> 3 lymph nodes) ALNM in patients with breast cancer with metastatic SLNs.
We retrospectively identified 804 patients with breast cancer (cT1-3cN0) who had metastatic SLNs and had undergone axillary lymph node dissection (ALND). We evaluated the risk factors for advanced ALNM using logistic regression analysis and developed and validated a scoring system for the prediction of ALNM using training (n = 501) and validation (n = 303) cohorts, respectively. The predictive performance was assessed using the receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration plots.
Ultrasound findings of multiple suspicious lymph nodes, SLN macrometastasis, the ratio of metastatic SLNs to the total number of SLNs removed, and the number of metastatic SLNs were significant risk factors for advanced ALNM. Clinical tumor size and invasive lobular carcinoma were of borderline significance. The scoring system based on these six variables yielded high AUCs (0.90 [training] and 0.89 [validation]). The calibration plots of frequency compared to the predicted probability showed slopes of 1.00 (training) and 0.85 (validation), with goodness-of-fit for the model. When the cutoff score was set at 4, the negative predictive values (NPVs) of excluding patients with advanced ALNM were 96.8% (training) and 96.9% (validation). The AUC for predicting advanced ALNM using our scoring system was significantly higher than that predicted by a single independent predictor, such as the number of positive SLNs or the proportion of positive SLNs. Similarly, our scoring system also showed good discrimination and calibration ability when the analysis was restricted to patients with one or two SLN metastases.
Our easy-to-use scoring system can exclude advanced ALNM with high NPVs. It may contribute to reducing the risk of undertreatment with adjuvant therapies in patients with metastatic SLNs, even if ALND is omitted.
目前尚无仅使用术前和术中易于获得的数据开发的评分型预测模型,用于评估转移性前哨淋巴结 (SLN) 乳腺癌患者腋窝高级淋巴结转移 (ALNM) 的风险。我们旨在开发和验证一种仅使用术前和术中数据的评分系统,以区分乳腺癌伴转移性 SLN 患者中无高级(≤3 个淋巴结)和高级(>3 个淋巴结)ALNM。
我们回顾性地确定了 804 名患有乳腺癌(cT1-3cN0)、转移性 SLN 且已行腋窝淋巴结清扫术(ALND)的患者。我们使用逻辑回归分析评估高级 ALNM 的危险因素,并分别使用训练队列(n=501)和验证队列(n=303)开发和验证用于预测 ALNM 的评分系统。使用接受者操作特征(ROC)曲线、曲线下面积(AUC)和校准图评估预测性能。
超声显示多个可疑淋巴结、SLN 大转移、转移的 SLN 与切除的 SLN 总数之比以及转移的 SLN 数量是高级 ALNM 的显著危险因素。临床肿瘤大小和浸润性小叶癌具有边缘显著性。基于这六个变量的评分系统产生了高 AUC(0.90[训练]和 0.89[验证])。频率与预测概率的校准图显示斜率为 1.00(训练)和 0.85(验证),模型拟合良好。当截距分数设定为 4 时,排除高级 ALNM 患者的阴性预测值(NPV)为 96.8%(训练)和 96.9%(验证)。使用我们的评分系统预测高级 ALNM 的 AUC 明显高于使用单个独立预测因子(如阳性 SLN 数量或阳性 SLN 比例)的预测。同样,当分析仅限于具有一个或两个 SLN 转移的患者时,我们的评分系统也表现出良好的区分和校准能力。
我们易于使用的评分系统可以排除具有高 NPV 的高级 ALNM。即使省略 ALND,它也可能有助于降低转移性 SLN 患者辅助治疗不足的风险。