术中快速预测乳腺癌非前哨淋巴结转移模型的建立与验证研究

背景与目的:大部分前哨淋巴结(sentinel lymph node,SLN)阳性而接受腋窝淋巴结清扫术(axillary lymph node dissection,ALND)的患者,腋窝非前哨淋巴结(non-sentinel lymph node,nSLN)并没有发生转移,因此准确预测nSLN转移至关重要.该研究将建立基于分子诊断一步核酸扩增法(one-step nucleic acid amplification,OSNA)的术中快速预测乳腺癌nSLN转移的模型,以期有效指导乳腺癌后续治疗.方法:利用2010年OSNA临床试验入组的552例患者中SLN阳性、并接受ALND的103例患者数据,建立基于分子诊断的乳腺癌NSLN转移的预测模型,并利用2015年OSNA临床试验入组的327例患者中61例符合相同条件的患者数据进行验证.结果:原发肿瘤大小、SLN总肿瘤负荷、SLN阳性数及阴性数是NSLN转移的四个独立相关因素,利用这四个因素建立预测列线图,得出建模组患者的受试者工作特征(receiver operating characteristic curve,ROC)曲线的曲线下面积(area under the ROC curve,AUC)为0.814,验证组患者的AUC为0.842.利用验证组61例患者影像学评估的肿瘤大小替代病理大小对本模型进行了验证,得出AUC为0.838,与模型验证性AUC相比差异无统计学意义(P=0.7406).结论:基于分子诊断的乳腺癌预测nSLN转移的模型既可以术中快速预测腋窝淋巴结转移风险,也可以术后常规预测,明显优于其他预测模型,对后续腋窝的处理及放疗靶区勾画具有更好的指导价值.

A nomogram for the intraoperative prediction of non-sentinel lymph node metastasis in breast cancer patients

Background and purpose: When patients have positive sentinel lymph node (SLN), axillary lymph node dissection (ALND) is usually performed, but most of them have no metastasis in the non-sentinel lymph node (nSLN). It is of great significance to predict metastasis of nSLN precisely. The aim of the study was to establish a nomogram for the intraoperative prediction of nSLN metastasis in breast cancer patients using one-step nucleic acid amplification (OSNA) techniques and to direct the subsequent therapy for breast cancer effectively. Methods: Of 552 breast cancer patients who underwent SLN biopsy in the 2010 OSNA clinical trial, 103 with SLN metastasis treated with ALND were assessed to establish a nomogram for intraoperative prediction of nSLN based on the molecular diagnosis. A validation cohort of 61 patients who met the similar criteria in the 2015 OSNA clinical trial subsequently validated it. Results: Primary tumor size, total tumor load, the number of positive SLNs and negative SLNs were associated with the presence of nSLN metastasis based on the multivariable logistic regression results, and a nomogram was established with these variables. Its area under the ROC curve was 0.814 for the predictive model and it was 0.842 in the re-validation cohort. The tumor size assessed by the postoperative histological examination was replaced by the size evaluated by the imaging examination, and the area under the ROC curve was 0.838. There was no statistically significant difference in the accuracy compared with the former validation data (P=0.7406). Conclusion: The predictive nomogram based on the molecular diagnosis can predict the nSLN metastases intra/post-operatively. It appears to be obviously superior to other predictive models and may help to guide the axillary management and to make decisions about radiation target region.