ArticleTranslational andrology and urology2020
Establishment of predictive model for patients with kidney cancer bone metastasis: a study based on SEER database.
Article in Translational andrology and urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- A nomogram predicting the OS and CSS of ≥ T1b renal cell carcinoma patients after surgery.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Clinical decision system for renal cell carcinoma integrating interpretable machine learning algorithms.Frontiers in surgery · 2025Article
- A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer.Scientific reports · 2024Article
- Web-Based Nomograms for Overall Survival and Cancer-Specific Survival of Bladder Cancer Patients with Bone Metastasis: A Retrospective Cohort Study from SEER Database.Journal of clinical medicine · 2023Article
- Construction and validation of a novel prognostic nomogram for patients with metastatic renal cell carcinoma: a SEER-based study.The Journal of international medical research · 2022Article
- Dynamic Predictive Models with Visualized Machine Learning for Assessing the Risk of Lung Metastasis in Kidney Cancer Patients.Journal of oncology · 2022Article
- A novel long non-coding RNA-based prognostic signature for renal cell carcinoma patients with stage IV and histological grade G4.Bioengineered · 2021Article
- Clinical features and prognostic factors of patients with metastatic renal cell carcinoma stratified by age.Aging · 2021Article
- Competing risk analysis of cardiovascular/cerebrovascular death in T1/2 kidney cancer: a SEER database analysis.BMC cancer · 2021Article
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Abstract
backgroundBone is a common metastatic tissue of kidney cancer. Accurate prediction of the prognosis of patients with kidney cancer bone metastasis (KCBM) can help doctors and patients choose a further appropriate treatment.
methodsDuring the period from January 1, 2010 to December 31, 2015, screening patients with kidney cancer diagnosed with bone metastases from the SEER database. Summary of demographic, pathology, number of other metastatic organs, and treatment for KCBM patients. All prognostic factors were plotted for Kaplan-Meier survival curves and log-rank test. Prognostic factors of P<0.001 in the log-rank test were chosen and used to establish nomograms of OS and KCSS. We used C-index, ROC curve, and calibration plot to test the prediction accuracy of two nomograms.
resultsA total of 4,234 KCBM patients were included in the study, and patients were diagnosed between January 1, 2010 and December 31, 2015. The model establishment group included 2,966 KCBM patients and the validation group included 1,268 KCBM patients. We have established nomograms for OS and KCSS respectively. These two nomograms included factors such as age, marital status, insurance status, histological type, grade, T stage, N stage, number of extra-bone metastatic organs, surgery, RT, and CT. The C-index of nomograms of OS and KCSS was 0.733 and 0.752, respectively. In all ROC curves, all AUC values were greater than 0.7, proving that the nomograms of both OS and KCSS have achieved medium prediction accuracy. The calibration plots of the model establishment group and the validation group showed good consistency between the predicted nomograms of OS and KCSS.
conclusionsIn this study, nomograms of OS and KCSS were established based on the published data of KCBM patients in the SEER database, and the model was validated internally and externally. The prediction accuracy of nomograms of OS and KCSS achieved satisfactory results. At present, this model has the ability to predict the prognosis of KCBM patients and can be used in clinical work.
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