Frontiers in Public Health (Nov 2022)
Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning
- Lichen Ji,
- Lichen Ji,
- Lichen Ji,
- Lichen Ji,
- Wei Zhang,
- Wei Zhang,
- Wei Zhang,
- Jiaqing Huang,
- Jiaqing Huang,
- Jinlong Tian,
- Jinlong Tian,
- Jinlong Tian,
- Jinlong Tian,
- Xugang Zhong,
- Xugang Zhong,
- Xugang Zhong,
- Junchao Luo,
- Junchao Luo,
- Junchao Luo,
- Junchao Luo,
- Senbo Zhu,
- Senbo Zhu,
- Senbo Zhu,
- Senbo Zhu,
- Zeju He,
- Zeju He,
- Zeju He,
- Zeju He,
- Yu Tong,
- Yu Tong,
- Yu Tong,
- Xiang Meng,
- Xiang Meng,
- Xiang Meng,
- Xiang Meng,
- Yao Kang,
- Yao Kang,
- Yao Kang,
- Qing Bi,
- Qing Bi,
- Qing Bi
Affiliations
- Lichen Ji
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Lichen Ji
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Lichen Ji
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Lichen Ji
- Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Wei Zhang
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Wei Zhang
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Wei Zhang
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Jiaqing Huang
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Jiaqing Huang
- The Second Clinic Medical College, Zhejiang Chinese Medicine University, Hangzhou, China
- Jinlong Tian
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Jinlong Tian
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Jinlong Tian
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Jinlong Tian
- The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
- Xugang Zhong
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Xugang Zhong
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Xugang Zhong
- Department of Orthopedics, Zhejiang Provincial People's Hospital, Qingdao University, Qingdao, China
- Junchao Luo
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Junchao Luo
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Junchao Luo
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Junchao Luo
- Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Senbo Zhu
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Senbo Zhu
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Senbo Zhu
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Senbo Zhu
- Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Zeju He
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Zeju He
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Zeju He
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Zeju He
- Department of Orthopedics, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
- Yu Tong
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Yu Tong
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Yu Tong
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Xiang Meng
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Xiang Meng
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Xiang Meng
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Xiang Meng
- The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China
- Yao Kang
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Yao Kang
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Yao Kang
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- Qing Bi
- Cancer Center, Department of Orthopedics, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Qing Bi
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
- Qing Bi
- Center for Rehabilitation Medicine, Osteoporosis Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China
- DOI
- https://doi.org/10.3389/fpubh.2022.1015952
- Journal volume & issue
-
Vol. 10
Abstract
BackgroundBone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well.MethodsThe study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts.ResultsOur prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083–0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979–0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance.ConclusionOur developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.
Keywords
