Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients

Abstract

Objective. To establish and verify the clinical prediction model of lung metastasis in renal cancer patients. Method. Kidney cancer patients from January 1, 2010, to December 31, 2017, in the SEER database were enrolled in this study. In the first section, LASSO method was adopted to select variables. Independent influencing factors were identified after multivariate logistic regression analysis. In the second section, machine learning (ML) algorithms were implemented to establish models and 10-foldcross-validation was used to train the models. Finally, receiver operating characteristic curves, probability density functions, and clinical utility curve were applied to estimate model’s performance. The final model was shown by a website calculator. Result. Lung metastasis was confirmed in 7.43% (3171 out of 42650) of study population. In multivariate logistic regression, bone metastasis, brain metastasis, grade, liver metastasis, N stage, T stage, and tumor size were independent risk factors of lung metastasis in renal cancer patients. Primary site and sequence number were independent protection factors of LM in renal cancer patients. The above 9 impact factors were used to develop the prediction models, which included random forest (RF), naive Bayes classifier (NBC), decision tree (DT), xgboost (XGB), gradient boosting machine (GBM), and logistic regression (LR). In 10-foldcross-validation, the average area under curve (AUC) ranked from 0.907 to 0.934. In ROC curve analysis, AUC ranged from 0.879–0.922. We found that the XGB model performed best, and a Web-based calculator was done according to XGB model. Conclusion. This study provided preliminary evidence that the ML algorithm can be used to predict lung metastases in patients with kidney cancer. This low cost, noninvasive and easy to implement diagnostic method is useful for clinical work. Of course this model still needs to undergo more real-world validation.

Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors

“…The disease’s early stages do not have any visible symptoms. As a result, people who want to receive medical treatment may already have kidney cancer that has spread to other parts of their bodies and may be experiencing related complications [ 1 ]. Malignant kidney tumors make up 2% of all cancer cases worldwide and are becoming more common.…”

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study

“…They also suggested that parameters such as race, grade, T stage, N stage, surgery, tumor size, and distant metastasis in other sites were independent variables for LM. Similarly, Xu et al [ 29 ] developed machine learning-based models to evaluate the risk of developing lung metastasis in kidney cancer patients using the SEER database. They performed multivariate logistic regression and found that grade, T and N stage, tumor size, and metastasis to other sites, including the bone, brain, and liver, were all risk factors.…”

Analysis of survival rates and evaluation of prognostic factors in patients with lung metastases from renal cell carcinoma

“…Цель исследования -анализ влияния прогностических факторов на показатели общей выживаемости у больных с метастазами рака почки в легкие. [12] костные метастазы, метастазы в печень и головной мозг были важными прогностическими предикторами метастазов в легких при раке почки, что подтверждается данными нашего исследования. В нашей работе только при однофакторном анализе выявлено влияние метастазов в кости, печень и лимфатические узлы на показатели ОВ у больных с ПКР (p<0,001).…”