ArticleScientific reports2023
Machine learning based prediction for oncologic outcomes of renal cell carcinoma after surgery using Korean Renal Cell Carcinoma (KORCC) database.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 9 citations in OpenAlex.
- Propensity score-matched comparison of survival outcomes between clear cell and non-clear cell renal cell carcinoma in cT1N0M0 disease.International journal of clinical oncology · 2026Article
- Integrating multi-omics data and machine learning to identify endocrine disrupting chemicals targeting key ccRCC-related genes.Discover oncology · 2025Article
- Development and validation of nomograms and integrated software incorporating preoperative C-reactive protein level for prognostic prediction of nonmetastatic clear cell renal cell carcinoma: Results from the International Marker Consortium for Renal Cancer (INMARC) Registry.World journal of urology · 2025Article
- Radiomics-Based Machine Learning with Natural Gradient Boosting for Continuous Survival Prediction in Glioblastoma.Cancers · 2024Article
- Multimodal data integration using machine learning to predict the risk of clear cell renal cancer metastasis: a retrospective multicentre study.Abdominal radiology (New York) · 2024Article
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16 authors at 6 institutions in 1 country.
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Abstract
We developed a novel prediction model for recurrence and survival in patients with localized renal cell carcinoma (RCC) after surgery and a novel statistical method of machine learning (ML) to improve accuracy in predicting outcomes using a large Asian nationwide dataset, updated KOrean Renal Cell Carcinoma (KORCC) database that covered data for a total of 10,068 patients who had received surgery for RCC. After data pre-processing, feature selection was performed with an elastic net. Nine variables for recurrence and 13 variables for survival were extracted from 206 variables. Synthetic minority oversampling technique (SMOTE) was used for the training data set to solve the imbalance problem. We applied the most of existing ML algorithms introduced so far to evaluate the performance. We also performed subgroup analysis according to the histologic type. Diagnostic performances of all prediction models achieved high accuracy (range, 0.77-0.94) and F1-score (range, 0.77-0.97) in all tested metrics. In an external validation set, high accuracy and F1-score were well maintained in both recurrence and survival. In subgroup analysis of both clear and non-clear cell type RCC group, we also found a good prediction performance.
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