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

Chan Xu; Qian Zhou; Wencai Liu; Wenle Li; Shengtao Dong; Wanying Li; Xiaofeng Xu; Ximin Qiao; Youli Jiang; Jingfang Chen; Chengliang Yin

doi:10.1155/2022/5798602

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

J Oncol. 2022 Oct 14:2022:5798602. doi: 10.1155/2022/5798602. eCollection 2022.

Authors

Chan Xu¹, Qian Zhou², Wencai Liu³, Wenle Li⁴, Shengtao Dong⁵, Wanying Li¹, Xiaofeng Xu^{1

6}, Ximin Qiao^{1

6}, Youli Jiang⁷, Jingfang Chen^{7

8}, Chengliang Yin⁹

Affiliations

¹ Clinical Medical Research Center, Xianyang Central Hospital, Xianyang, China.
² Department of Respiratory and Critical Care Medicine, The First People's Hospital of Chong Qing Liang Jiang New Area, Chongqing, China.
³ Department of Orthopaedic Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, China.
⁴ Xiamen University, Molecular Imaging and Translational Medicine Research Center, State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, Xiamen, China.
⁵ Department of Spine Surgery, Second Affiliated Hospital of Dalian Medical University, Dalian 116000, China.
⁶ Department of Urology, Xianyang Central Hospital, Xianyang, China.
⁷ Hengyang Medical School, School of Nursing, University of South China, Hengyang, Hunan, China.
⁸ National Clinical Research Center for Infectious Diseases, The Third People's Hospital of Shenzhen, Shenzhen, Guangdong, China.
⁹ Faculty of Medicine, Macau University of Science and Technology, Macau, China.

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.