Machine learning-based prognostic and metastasis models of kidney cancer.

Background: Kidney cancer originates from the urinary tubule epithelial system of the renal parenchyma, accounting for 20% of all urinary system tumors. Approximately 70% of cases are localized at diagnosis, and 30% are metastatic. Most localized kidney cancers can be cured by surgery, but most metastatic patients relapse after surgery and eventually die of kidney cancer. Therefore, accurately predicting patient survival and identifying high-risk metastatic patients will effectively guide interventions and improve prognosis.

Methods: This study used the data of 12,394 kidney cancer patients from the surveillance, epidemiology, and end results database to construct a research cohort related to kidney cancer survival and metastasis. Eight machine learning models (including support vector machines, logistic regression, decision tree, random forest, XGBoost, AdaBoost, K-nearest neighbors, and multilayer perceptron) were developed to predict the survival and metastasis of kidney cancer and six evaluation indicators (accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic [AUROC]) were used to verify, evaluate, and optimize the models.

Results: Among the eight machine learning models, Logistic Regression has the highest AUROC in both prediction scenarios. For 3-year survival prediction, the Logistic Regression model had an accuracy of 0.684, a sensitivity of 0.702, a specificity of 0.670, a precision of 0.686, an F1 score of 0.683, and an AUROC of 0.741. For tumor metastasis prediction, the Logistic Regression model had an accuracy of 0.800, a sensitivity of 0.540, a specificity of 0.830, a precision of 0.769, an F1 score of 0.772, and an AUROC of 0.804.

Conclusion: In this study, we selected appropriate variables from both statistical and clinical significance and developed and compared eight machine learning models for predicting 3-year survival and metastasis of kidney cancer. The prediction results and evaluation results demonstrated that our model could provide decision support for early intervention for kidney cancer patients.