ArticleComputational and mathematical methods in medicine2022
Establishment and Validation of a Machine Learning Prediction Model Based on Big Data for Predicting the Risk of Bone Metastasis in Renal Cell Carcinoma Patients.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Development and validation of a diagnostic nomogram integrating anatomical scores and systemic immune-inflammatory biomarkers forFrontiers in immunology · 2026Article
- Navigating advanced renal cell carcinoma in the era of artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Artificial Intelligence in Detection, Management, and Prognosis of Bone Metastasis: A Systematic Review.Cancers · 2024Review
- Prediction model of ocular metastasis from primary liver cancer: Machine learning-based development and interpretation study.Cancer medicine · 2023Article
- Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.Frontiers in public health · 2022Article
- Prediction of Distant Metastasis in Renal Cell Carcinoma Using Machine Learning Algorithms: A Multicenter Cohort Study.Cancer control : journal of the Moffitt Cancer CenterArticle
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Since the prognosis of renal cell carcinoma (RCC) patients with bone metastasis (BM) is poor, this study is aimed at using big data to build a machine learning (ML) model to predict the risk of BM in RCC patients. Methods: A retrospective study was conducted on 40,355 RCC patients in the SEER database from 2010 to 2017. LASSO regression and multivariate logistic regression analysis was performed to determine independent risk factors of RCC-BM. Six ML algorithm models, including LR, GBM, XGB, RF, DT, and NBC, were used to establish risk models for predicting RCC-BM. The prediction performance of ML models was weighed by 10-fold cross-validation. Results: The study investigated 40,355 patients diagnosed with RCC in the SEER database, where 1,811 (4.5%) were BM patients. Independent risk factors for BM were tumor grade, T stage, N stage, liver metastasis, lung metastasis, and brain metastasis. Among the RCC-BM risk prediction models established by six ML algorithms, the XGB model showed the best prediction performance (AUC = 0.891). Therefore, a network calculator based on the XGB model was established to individually assess the risk of BM in patients with RCC. Conclusion: The XGB risk prediction model based on the ML algorithm performed a good prediction effect on BM in RCC patients.
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Registered trials
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