Prediction of Distant Metastasis in Renal Cell Carcinoma Using Machine Learning Algorithms: A Multicenter Cohort Study.

Abstract

IntroductionFew machine learning (ML) studies have investigated the prediction of distant metastasis in patients with renal cell carcinoma (RCC). This retrospective study aimed to develop and validate predictive models based on ML algorithms for RCC patients with distant metastasis.MethodsWe extracted RCC data from the SEER database between 2004 and 2015 (n=192,912) and from the Chinese National Cancer Center (CNCC) database between 2010 and 2020 (n=3,034). Seven different algorithms were applied to predict distant metastasis in RCC. Fivefold cross-validation was employed for model construction. The data were analyzed by using Python based on incomplete data, complete data, upsampling data and downsampling data.ResultsAfter data cleaning and screening, 121,741 cases from the SEER dataset and 2803 cases from the CNCC external test set were retained. For incomplete data, the neural network model [area under the curve (AUC) 95% confidence interval (CI) of the external data: 0.7467±0.0573] achieved the highest accuracy. For the complete data, the support vector machine (SVM) model achieved the highest accuracy, with an AUC 95% CI of 0.8221±0.0485. The disparity between positive and negative samples significantly varied across the different datasets. Upsampling and downsampling analyses were also conducted. For the upsampling data, the extreme gradient boosting (XGBoost) model demonstrated the highest accuracy, with an AUC 95% CI for the external data of 0.8162±0.0558. For the downsampling data, the SVM model achieved the highest accuracy, with an AUC 95% CI of 0.8274±0.0546 for the external data.ConclusionsOur study revealed that ML algorithms can effectively predict distant metastasis in patients with RCC. ML models exhibit favorable application prospects in clinical practice.

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