Using machine learning to predict lymph node metastasis in patients with renal cell carcinoma: A population-based study - Zhang, Yuhan; Yi, Xinglin; Tang, Zhe; Xie, Pan; Yin, Na; Deng, Qiumiao; Zhu, Lin; Luo, Hu; Peng, Kanfu (Frontiers in Public Health, 2023)
Background: Lymph node (LN) metastasis is highly associated with distant metastasis of renal cell carcinoma (RCC) and portends adverse prognosis. Accurate LN-status prediction is extremely essential for individualized treatment of those with RCC and development of appropriate surgical options for physicians. Thus, a prediction model to assess the hazard index of LN metastasis in patients with RCC should be developed. Methods: Partial data were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Moreover, data of 492 individuals with RCC collected from the Southwest Hospital were used for external validation. Eight indicators of risk for LN metastasis were screened out. Six ML classifiers were established and tuned, focused on predicting LN metastasis in patients with RCC. The models were integrated with big data analytics and machine learning (ML) algorithms. Based on the optimal model, we developed an online risk calculator. Results: The extreme gradient boosting (XGB) model was superior to the others in both internal and external trials. The area under the curve, accuracy, sensitivity, and specificity were 0.930, 0.857, 0.856, and 0.873, respectively, in the internal test and 0.958, 0.935, 0.769, and 0.944, respectively, in the external test. These parameters show that XGB has an excellent ability for clinical application. Conclusion: Our study shows that integrating ML algorithms and clinical data can effectively predict LN metastasis in patients with confirmed RCC. Subsequently, a freely available online calculator (https://xinglinyi.shinyapps.io/20221004-app/) based on the XGB model was built.