ArticleJMIR medical informatics2021
Machine Learning Approach to Predict the Probability of Recurrence of Renal Cell Carcinoma After Surgery: Prediction Model Development Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 19 citations in OpenAlex.
- Development and validation of a machine learning model for predicting distant metastasis in patients with renal cell carcinoma: a population-based study.World journal of surgical oncology · 2026Article
- Predicting distant metastasis in early-onset kidney cancer using machine learning: a SEER database study with external validation.Clinical and experimental medicine · 2025Article
- Prognostic impact of non-clear cell histology on recurrence and cancer-free survival in renal cell carcinoma: a time-to-event analysis.BMC urology · 2025Article
- Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- The Use of Artificial Intelligence in Urologic Oncology: Current Insights and Challenges.Research and reports in urology · 2025Review
- UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120).NPJ precision oncology · 2024Article
- Machine learning based prediction for oncologic outcomes of renal cell carcinoma after surgery using Korean Renal Cell Carcinoma (KORCC) database.Scientific reports · 2023Article
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
- Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.Frontiers in oncology · 2023Article
- Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma.BMC medical informatics and decision making · 2022Article
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence for renal cancer: From imaging to histology and beyond.Asian journal of urology · 2022Review
- Precision Medicine: An Optimal Approach to Patient Care in Renal Cell Carcinoma.Frontiers in medicine · 2022Review
- Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.Sensors (Basel, Switzerland) · 2021Article
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5 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRenal cell carcinoma (RCC) has a high recurrence rate of 20% to 30% after nephrectomy for clinically localized disease, and more than 40% of patients eventually die of the disease, making regular monitoring and constant management of utmost importance.
objectiveThe objective of this study was to develop an algorithm that predicts the probability of recurrence of RCC within 5 and 10 years of surgery.
methodsData from 6849 Korean patients with RCC were collected from eight tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database. To predict RCC recurrence, analytical data from 2814 patients were extracted from the database. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared.
resultsWithin 5 years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naïve Bayes (NB) model, with a value of 0.836. Within 10 years of surgery, the highest AUROC was obtained from the NB model, with a value of 0.784.
conclusionsAn algorithm was developed that predicts the probability of RCC recurrence within 5 and 10 years using the KORCC database, a large-scale RCC cohort in Korea. It is expected that the developed algorithm will help clinicians manage prognosis and establish customized treatment strategies for patients with RCC after surgery.
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