Teramoto Atsuko, Shimazu Kenzo, Naoi Yasuto, Shimomura Atsushi, Shimoda Masafumi, Kagara Naofumi, Maruyama Naomi, Kim Seung Jin, Yoshidome Katsuhide, Tsujimoto Masahiko, Tamaki Yasuhiro, Noguchi Shinzaburo
Department of Breast and Endocrine Surgery, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-shi, Osaka 565-0871, Japan.
Department of Surgery, Osaka Police Hospital, 10-31 Kitayamacho, Tennouji-ku, Osaka-shi, Osaka 543-0035, Japan.
Breast. 2014 Oct;23(5):579-85. doi: 10.1016/j.breast.2014.05.026. Epub 2014 Jun 25.
The aim of the present study was to construct the intra-operative prediction model of non-sentinel lymph node (non-SLN) metastasis in breast cancer patients with SLN metastasis using one-step nucleic acid amplification (OSNA). Of 833 breast cancer patients (T1-T2, N0) who underwent SLN biopsy and had their SLNs examined intra-operatively with the OSNA assay, 161 with SLN metastasis and treated with completion axillary lymph node dissection (cALND) were randomly divided into a training (n = 81) and a validation (n = 80) cohort. Non-SLN metastasis of the training cohort was associated with the number of positive SLNs (P = 0.001), CK19 mRNA copy number (P = 0.001), and clinical tumor size (P = 0.055). These parameters were used to construct the intra-operative prediction model of non-SLN metastasis. Its diagnostic accuracy (AUC of ROC curve) was 0.809 and 0.704 for the training and validation cohorts, respectively. The intra-operative prediction model using OSNA may have a diagnostic accuracy of non-SLN metastasis comparable to that of the conventional, post-operative prediction model, indicating that it might help decide the indication for cALND.
本研究的目的是利用一步核酸扩增(OSNA)构建前哨淋巴结(SLN)转移的乳腺癌患者非前哨淋巴结(non-SLN)转移的术中预测模型。在833例行SLN活检并在术中用OSNA检测SLN的乳腺癌患者(T1-T2,N0)中,161例SLN转移并接受了腋窝淋巴结清扫术(cALND)的患者被随机分为训练组(n = 81)和验证组(n = 80)。训练组的non-SLN转移与阳性SLN数量(P = 0.001)、CK19 mRNA拷贝数(P = 0.001)和临床肿瘤大小(P = 0.055)相关。这些参数被用于构建non-SLN转移的术中预测模型。其诊断准确性(ROC曲线的AUC)在训练组和验证组中分别为0.809和0.704。使用OSNA的术中预测模型对non-SLN转移的诊断准确性可能与传统的术后预测模型相当,这表明它可能有助于决定cALND的指征。