Trial reportScientific reports2021
Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma.
Trial report in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed.
- Machine learning studies for predicting 5-year renal cell cancer survival: a multicenter study.Translational andrology and urology · 2025Article
- Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.Scientific reports · 2025Article
- Pan-cancer predictive survival model development and evaluation using electronic health record and genetic data across 10 cancer types.Discover oncology · 2025Article
- The interpretable CT-based vision transformer model for preoperative prediction of clear cell renal cell carcinoma SSIGN score and outcome.Insights into imaging · 2025Article
- Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review.Cancer medicine · 2025Article
- Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure.Frontiers in medicine · 2025Article
- UroPredict: Machine learning model on real-world data for prediction of kidney cancer recurrence (UroCCR-120).NPJ precision oncology · 2024Article
- The predictive value of modified-DeepSurv in overall survivals of patients with lung cancer.iScience · 2023Article
- Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.Cancer medicine · 2023Article
- A machine learning model for colorectal liver metastasis post-hepatectomy prognostications.Hepatobiliary surgery and nutrition · 2023Article
- Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.BioData mining · 2023Article
- Prediction of clinicopathological features, multi-omics events and prognosis based on digital pathology and deep learning in HRJournal of thoracic disease · 2023Article
- Machine learning based prediction for oncologic outcomes of renal cell carcinoma after surgery using Korean Renal Cell Carcinoma (KORCC) database.Scientific reports · 2023Article
- The Efficacy of PretreatmentClinical Medicine Insights. Oncology · 2023Article
- Artificial intelligence-based prediction of overall survival in metastatic renal cell carcinoma.Frontiers in oncology · 2023Article
- Deep Learning-driven classification of external DICOM studies for PACS archiving.European radiology · 2022Article
- Machine learning-based prognostic and metastasis models of kidney cancer.Cancer innovation · 2022Article
- Predicting completion of clinical trials in pregnant women: Cox proportional hazard and neural network models.Clinical and translational science · 2022Article
- An energy-efficient in-memory computing architecture for survival data analysis based on resistive switching memories.Frontiers in neuroscience · 2022Article
- Prediction and Risk Stratification of Cardiovascular Disease in Diabetic Kidney Disease Patients.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Survival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals' patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel's C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel's C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel's C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.
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