曹迎明, 刘淼, 周波, 潘璐, 王殊, 杨德起. MSKCC和SOC模型预测中国乳腺癌患者非前哨淋巴结转移的验证比较研究[J]. 中国肿瘤临床, 2014, 41(8): 508-512. DOI: 10.3969/j.issn.1000-8179.21031009
引用本文: 曹迎明, 刘淼, 周波, 潘璐, 王殊, 杨德起. MSKCC和SOC模型预测中国乳腺癌患者非前哨淋巴结转移的验证比较研究[J]. 中国肿瘤临床, 2014, 41(8): 508-512. DOI: 10.3969/j.issn.1000-8179.21031009
CAO Yingming, LIU Miao, ZHOU Bo, PAN Lu, WANG Shu, YANG Deqi. Comparative validation of MSKCC and SOC models for predicting non-sentinel lymph node metastasis in Chinese breast cancer patients[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2014, 41(8): 508-512. DOI: 10.3969/j.issn.1000-8179.21031009
Citation: CAO Yingming, LIU Miao, ZHOU Bo, PAN Lu, WANG Shu, YANG Deqi. Comparative validation of MSKCC and SOC models for predicting non-sentinel lymph node metastasis in Chinese breast cancer patients[J]. CHINESE JOURNAL OF CLINICAL ONCOLOGY, 2014, 41(8): 508-512. DOI: 10.3969/j.issn.1000-8179.21031009

MSKCC和SOC模型预测中国乳腺癌患者非前哨淋巴结转移的验证比较研究

Comparative validation of MSKCC and SOC models for predicting non-sentinel lymph node metastasis in Chinese breast cancer patients

  • 摘要:
      目的   验证纪念斯隆-凯特琳癌症中心(Memorial Sloan-Kettering Cancer Center,MSKCC)模型和斯坦福大学模型(Stanford Online Calculator,SOC)预测中国前哨淋巴结(sentinel lymph node,SLN)阳性乳腺癌患者非前哨淋巴结(non-sentinel lymph node,NSLN)转移的能力并进行比较。
      方法   收集120例SLN阳性的乳腺癌病例验证MSKCC和SOC模型,通过受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)下面积(Area Under the Curve,AUC)、不同截断值的预测能力来比较两个模型在中国乳腺癌患者中的应用价值。
      结果   用MSKCC和SOC模型验证120例中国乳腺癌患者,AUC分别为0.688和0.734。取10%为截断值,MSKCC和SOC模型的假阴性率均为4.4%,阴性预测值分别为75.0%和90.0%。取90.0%为截断值,MSKCC和SOC模型的假阳性率分别为0.0%和6.7%,阳性预测值分别为100.0%和68.8%。
      结论   用MSKCC和SOC模型预测中国乳腺癌NSLN转移,结果皆劣于原始研究,SOC模型的预测能力略优于MSKCC模型。

     

    Abstract:
      Objective  The study aimed to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) prediction of non-sentinel lymph node (NSLN) metastasis in Chinese patients with sentinel lymph node (SLN)-positive breast cancers.
      Methods  The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients who were positive for SLNs. The area under the receiver operating characteristic curves (AUC) for each model was evaluated. Patients with 10% and 90% probabilities of NSLN metastasis were separately examined.
      Results  The MSKCC and SOC predicted the likelihood of NSLN metastasis in a consecutive group of 120 patients with AUCs of 0.688 and 0.734, respectively. At the lowest probability cutoff value of 10%, the false-negative rates of MSKCC and SOC were both 4.4%, and the negative predictive values were 75.0% and 90.0%, respectively. When the highest probability cutoff value of 90% was used, the false-positive rates were 0.0% and 6.7%, and the positive predictive values were 100.0% and 68.8%, respectively.
      Conclusion  Results of the MSKCC nomogram and SOC were inferior to those of previous studies on predicting NSLN metastasis in Chinese patients with breast cancers. The prediction ability of SOC was slightly superior to that of the MSKCC nomogram.

     

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