Department of Breast Surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, Henan Province, China.
Department of Thyroid and Breast Surgery, Ruzhou First People's Hospital, Ruzhou, Henan Province, China.
BMC Cancer. 2021 Apr 26;21(1):466. doi: 10.1186/s12885-021-08178-9.
It is reported that appropriately 50% of early breast cancer patients with 1-2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1-2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1-2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs.
We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients.
The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682-0.771) in the primary cohort and 0.722 (95% CI: 0.653-0.792) in the validation cohort. The addition of CA 15-3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732-0.815) vs. 0.727 (0.682-0.771), P < 0.001) and validation cohort (AUC: (0.777 (0.713-0.840) vs. 0.722 (0.653-0.792), P < 0.001). Decision curve analysis demonstrated that the nomograms were clinically useful.
The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1-2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.
据报道，约有 50%的 1-2 个前哨淋巴结（SLN）微转移的早期乳腺癌患者不能从腋窝淋巴结清扫（ALND）或保乳手术联合全乳放疗中获益。然而，1-2 个 SLN 宏转移的患者是否能从 ALND 中获益尚不清楚。我们的研究旨在开发和验证仅基于其病理特征或联合 STMs 评估 1-2 个 SLN 宏转移的患者腋窝非 SLN 转移的列线图。
我们回顾性分析了来自两个独立队列的 1150 例早期乳腺癌患者的病理特征和 STMs。采用最佳子集回归进行特征选择和特征构建。为每个患者计算腋窝非 SLN 转移的风险评分，作为所选预测因子的线性组合，这些预测因子由各自的系数加权。
基于病理学的列线图具有很强的腋窝非 SLN 转移区分能力，在主要队列中的接受者操作特征（ROC）曲线下面积（AUC）为 0.727（95%CI：0.682-0.771），在验证队列中的 AUC 为 0.722（95%CI：0.653-0.792）。在主要队列中，加入 CA 15-3 和 CEA 可显著提高基于病理学的列线图的性能（AUC：0.773（0.732-0.815）与 0.727（0.682-0.771），P<0.001）和验证队列（AUC：0.777（0.713-0.840）与 0.722（0.653-0.792），P<0.001）。决策曲线分析表明，该列线图具有临床实用性。
基于病理特征的列线图可用于识别 1-2 个 SLN 阳性的乳腺癌患者的腋窝非 SLN 转移。此外，与仅基于病理特征相比，联合 STMs 和病理特征可更准确地识别有腋窝非 SLN 转移的患者。
