肾癌骨转移风险与预后评估模型数据集（基于机器学习）

数据集获取（Figshare）

• 源站标题：Data_Sheet_1_Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.DOCX
• DOI：10.3389/fpubh.2022.1015952.s001
• 发布日期：2022-11-17
• 文件数：1 个
• 文件总体积：1.06 MB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Bone_metastasis_risk_and_prognosis_assessment_models_for_kidney_cancer_based_on_machine_learning_DOCX/21570729

命令行下载

curl -L -o "Data_Sheet_1_Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.DOCX" "https://ndownloader.figshare.com/files/38233560"

文件清单

• Data_Sheet_1_Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.DOCX（1.06 MB）

文件总体积 1.06 MB（源站 API 实测）。

数据说明

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.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fpubh.2022.1015952.s001（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Bone_metastasis_risk_and_prognosis_assessment_models_for_kidney_cancer_based_on_machine_learning_DOCX/21570729）。