BMC pulmonary medicine · 2024
Preoperative prediction of occult lymph node metastasis in patients with non-small cell lung cancer: a simple and widely applicable model.
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Li Jing-Xiao, Feng Gui-Yu, He Kun-Lin, Li Guo-Sheng, Gao Xiang, Yan Guan-Qiang, Wei Long-Qian, He Xu, Li Yue, Fu Zong-Wang, Liu Jun, Zhou Hua-Fu
Аннотация
OBJECTIVE: Lymph node metastasis (LNM) is one of the most common pathways of metastasis in non-small cell lung cancer (NSCLC). Preoperative assessment of occult lymph node metastasis (OLNM) in NSCLC patients is beneficial for selecting appropriate treatment plans and improving patient prognosis.
METHOD: A total of 370 NSCLC patients were included in the study. Univariate and multivariate logistic regression analysis were used to screen potential risk factors for OLNM in preoperative NSCLC patients. And establish a nomogram for OLNM in NSCLC patients before surgery. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the established nomogram.
RESULT: Both univariate and multivariate logistic regression analyses suggested that multiple tumors, ERBB2 missense mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin are potential risk factors for OLNM in NSCLC patients. The constructed nomogram was evaluated, and the consistency index (C-index) and area under the ROC curve of the model were both 0.846. The calibration curve showed that the predicted values of the model had a high degree of fit with the actual observed values, and DCA suggested that the above indicators had good utility.
CONCLUSION: The personalized scoring prediction model constructed based on multiple tumors, ERBB2 miss mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin can screen NSCLC patients who may have OLNM.
METHOD: A total of 370 NSCLC patients were included in the study. Univariate and multivariate logistic regression analysis were used to screen potential risk factors for OLNM in preoperative NSCLC patients. And establish a nomogram for OLNM in NSCLC patients before surgery. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the established nomogram.
RESULT: Both univariate and multivariate logistic regression analyses suggested that multiple tumors, ERBB2 missense mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin are potential risk factors for OLNM in NSCLC patients. The constructed nomogram was evaluated, and the consistency index (C-index) and area under the ROC curve of the model were both 0.846. The calibration curve showed that the predicted values of the model had a high degree of fit with the actual observed values, and DCA suggested that the above indicators had good utility.
CONCLUSION: The personalized scoring prediction model constructed based on multiple tumors, ERBB2 miss mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin can screen NSCLC patients who may have OLNM.