Construction of a nomogram prediction model for lung metastasis in elderly patients with clear cell renal cell carcinoma
10.3760/cma.j.cn371439-20240910-00128
- VernacularTitle:老年肾透明细胞癌患者发生肺转移的列线图预测模型构建
- Author:
Tian LI
1
;
Yang WU
;
Jiangming ZHANG
;
Chunsheng XI
Author Information
1. 中国人民解放军联勤保障部队第九四〇医院肾脏病科，兰州 730050
- Keywords:
Aged;
Carcinoma, renal cell;
Root cause analysis;
Nomograms;
Lung metastasis
- From:
Journal of International Oncology
2024;51(12):755-762
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To discusse the influencing factors of lung metastasis in elderly patients (≥60 years old) with clear cell renal cell carcinoma (ccRCC) based on Surveillance, Epidemiology, and End Results (SEER) database, and to construct and evaluate the nomogram prediction model.Methods:The SEER database was used to retrieve the data of elderly ccRCC patients from 2017 to 2021. The screened 8 183 ccRCC patients were randomly assigned to the training set ( n=5 728) and the validation set ( n=2 455) at a ratio of 7∶3 by using the software R4.4.1. The incidence of lung metastasis in elderly patients with ccRCC was calculated, and the influencing factors of lung metastasis in elderly patients with ccRCC were analyzed by univariate and multivariate logistic regression. According to the results of multivariate analysis, the nomogram prediction model was constructed, and the prediction efficiency of the model was evaluated by using the receiver operator characteristic (ROC) curve, the clinical application value of the prediction model was evaluated by calibration curve and decision curve analysis (DCA) . Results:A total of 8 183 elderly ccRCC patients were retrieved, including 620 patients with lung metastasis, and the incidence of lung metastasis was 7.58%. Univariate analysis showed that, race (white race: OR=1.58, 95% CI: 1.01-2.49, P=0.046; others: OR=1.85, 95% CI: 1.10-3.10, P=0.020), sex ( OR=1.32, 95% CI: 1.07-1.64, P=0.009), maximum tumor diameter (55-95 mm: OR=8.22, 95% CI: 6.11-11.07, P<0.001; >95 mm: OR=28.12, 95% CI: 20.81-37.99, P<0.001), T stage (T 2 stage: OR=15.62, 95% CI: 11.51-21.19, P<0.001; T 3 stage: OR=7.93, 95% CI: 6.06-10.36, P<0.001; T 4 stage: OR=28.65, 95% CI: 18.71-43.86, P<0.001), N stage ( OR=17.18, 95% CI: 13.36-22.10, P<0.001) and surgery situation ( OR=0.12, 95% CI: 0.09-0.14, P<0.001) were all influencing factors for lung metastasis in elderly patients with ccRCC. Multivariate analysis showed that, race (white race: OR=1.82, 95% CI: 1.07-3.09, P=0.027; others: OR=2.18, 95% CI: 1.17-4.05, P=0.014), maximum tumor diameter (55-95 mm, OR=4.63, 95% CI: 3.13-6.86, P<0.001; >95 mm, OR=8.29, 95% CI: 5.28-13.02, P<0.001), T stage (T 2 stage: OR=2.26, 95% CI: 1.45-3.51, P<0.001; T 3 stage: OR=3.38, 95% CI: 2.28-5.01, P<0.001; T 4 stage: OR=2.45, 95% CI: 1.39-4.31, P=0.002), N stage ( OR=3.81, 95% CI: 2.81-5.17, P<0.001) and surgery situation ( OR=0.10, 95% CI: 0.08-0.14, P<0.001) were independent influencing factors of lung metastasis in elderly patients with ccRCC. According to the results of multivariate analysis, a nomogram prediction model was constructed based on race, maximum tumor diameter, T stage, N stage and surgery situation. ROC curve analysis showed that the area under the curve (AUC) of the prediction model in the training set and the validation set for predicting lung metastasis in ccRCC patients was 0.91 (95% CI: 0.90-0.92) and 0.91 (95% CI: 0.89-0.93), respectively, which indicated that the prediction model had excellent distinguishing ability. Calibration curve showed that the actual occurrence probability of the training set and the validation set was consistent with the predicted probability, which showed that the calibration degree of the prediction model was good. DCA curve showed that the predictive model had good discrimination ability in both training set and validation set, which indicated that the predictive model had potential clinical application value. Conclusion:The incidence of lung metastasis in elderly patients with ccRCC is high. Race, maximum tumor diameter, T stage, N stage and surgery situation are all independent influencing factors of lung metastasis in elderly patients with ccRCC. The prediction model based on the above indexes has excellent prediction efficiency and clinical application value, and can be used to predict the risk of lung metastasis in elderly patients with ccRCC.
