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Published on: April 29, 2014
Machine learning-based prognostic and metastasis models of kidney cancer
Yuxiang Zhang1, Na Hong2, Sida Huang3
1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.
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.
Insights
Logistic Regression models accurately predict kidney cancer survival and metastasis, aiding early intervention for high-risk patients. This machine learning approach improves prognostic accuracy for kidney cancer patients.
Area of Science:
- Oncology
- Medical Informatics
- Machine Learning
Background:
- Kidney cancer accounts for 20% of urinary system tumors, with 30% of cases presenting as metastatic at diagnosis.
- While localized kidney cancer is often curable by surgery, metastatic disease frequently leads to relapse and mortality.
- Accurate survival prediction and identification of high-risk metastatic patients are crucial for effective intervention and improved outcomes.
Purpose of the Study:
- To develop and compare machine learning models for predicting 3-year survival and metastasis in kidney cancer patients.
- To identify the most effective model for prognostication and risk stratification in kidney cancer.
Main Methods:
- Utilized data from 12,394 kidney cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database.
- Developed and evaluated eight machine learning models: Support Vector Machines, Logistic Regression, Decision Tree, Random Forest, XGBoost, AdaBoost, K-Nearest Neighbors, and Multilayer Perceptron.
- Assessed model performance using accuracy, precision, sensitivity, specificity, F1 score, and Area Under the Receiver Operating Characteristic (AUROC).
Main Results:
- Logistic Regression demonstrated the highest AUROC for both survival (0.741) and metastasis (0.804) prediction.
- The Logistic Regression model achieved an accuracy of 0.684 for 3-year survival prediction and 0.800 for metastasis prediction.
- Specific performance metrics for Logistic Regression included sensitivity, specificity, and F1 scores for both prediction tasks.
Conclusions:
- Machine learning models, particularly Logistic Regression, can effectively predict kidney cancer survival and metastasis.
- The developed models provide valuable decision support for early intervention strategies in kidney cancer management.
- Optimized models can aid clinicians in identifying high-risk patients, thereby improving overall prognosis.
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