A Prediction Model for Pathologic N2 Disease in Lung Cancer Patients with a Negative Mediastinum by Positron Emission Tomography
A Prediction Model for Pathologic N2 Disease in Lung Cancer Patients with a Negative Mediastinum by Positron Emission Tomography
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DOI：
10.1097/jto.0b013e3182992421
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发表时间：
2013-09-01
影响因子：
20.4
通讯作者：
Rizk, Nabil P.
中科院分区：
文献类型：
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作者：
Farjah, Farhood;Lou, Feiran;Rizk, Nabil P.
前言：正电子发射断层扫描(PET)对无纵隔疾病的肺癌患者的侵袭性分期指导有限。我们建立并验证了病理性N_2病(PN_2)的预测模型，使用了先前描述的6个危险因素：肿瘤的位置和大小(CT)、结节病变(CT)、原发肿瘤的最大标准摄取值、N_1(正电子发射计算机断层扫描)和组织学。方法：2004-2009年的队列研究在T_1/T_2(CT)和N_0/N_1(正电子发射计算机断层扫描)患者中进行。用Logistic回归分析建立随机发展集(n=625)中pn2的预测模型。该模型在包括三分之二患者的开发组和包括其余三分之一的验证组(n=313)中都得到了验证。结果：在938例患者中，9.9%的患者存在pN2%(9例通过侵入性分期发现，84例在术中发现)。在发展组中，单变量分析显示pn2与增大的肿瘤大小(p<；0.001)、CT的结节状态(p=0.007)、原发肿瘤的最大标准化摄取值(p=0.027)以及正电子发射计算机断层扫描的n1(p<；0.001)显著相关；然而，在多变量预测模型中，只有pn2与pn2(p<；0.001)相关。模型在开发集(c统计量，0.70；95%可信区间，0.63~0.77；拟合优度p=0.61)和验证集(c统计量，0.65；95%可信区间，0.56~0.74；拟合度p=0.19)中表现良好。结论：基于上述6个危险因素建立的pn2预测模型具有合理的性能特征。这项研究的观察结果可能会指导pn2预测模型的前瞻性、多中心开发和验证。
Introduction:Guidance is limited for invasive staging in patients with lung cancer without mediastinal disease by positron emission tomography (PET). We developed and validated a prediction model for pathologic N2 disease (pN2), using six previously described risk factors: tumor location and size by computed tomography (CT), nodal disease by CT, maximum standardized uptake value of the primary tumor, N1 by PET, and histology.Methods:A cohort study (2004-2009) was performed in patients with T1/T2 by CT and N0/N1 by PET. Logistic regression analysis was used to develop a prediction model for pN2 among a random development set (n = 625). The model was validated in both the development set, which comprised two thirds of the patients and the validation set (n = 313), which comprised the remaining one third. Model performance was assessed in terms of discrimination and calibration.Results:Among 938 patients, 9.9% had pN2 (9 detected by invasive staging and 84 intraoperatively). In the development set, univariate analyses demonstrated a significant association between pN2 and increasing tumor size (p < 0.001), nodal status by CT (p = 0.007), maximum standardized uptake value of the primary tumor (p = 0.027), and N1 by PET (p < 0.001); however, only N1 by PET was associated with pN2 (p < 0.001) in the multivariate prediction model. The model performed reasonably well in the development (c-statistic, 0.70; 95% confidence interval, 0.63-0.77; goodness of fit p = 0.61) and validation (c-statistic, 0.65; 95% confidence interval, 0.56-0.74; goodness-of-fit p = 0.19) sets.Conclusion:A prediction model for pN2 based on six previously described risk factors has reasonable performance characteristics. Observations from this study may guide prospective, multicenter development and validation of a prediction model for pN2.