Trial reportThe Journal of international medical research2022
Prediction of pathological staging and grading of renal clear cell carcinoma based on deep learning algorithms.
Trial report in The Journal of international medical research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Integrated metabolomics and transcriptomics analysis unveils key metabolic pathways and potential biomarkers in clear cell renal cell carcinoma.Discover oncology · 2026Article
- Noninvasive prediction of occult pT3a upstaging in localized ccRCC with radiogenomic insights and prognostic relevance.NPJ precision oncology · 2026Article
- Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning.Journal of cancer research and clinical oncology · 2025Article
- A predictive model for WHO/ISUP pathologic grading of renal clear cell carcinoma based on CT radiomics: a multicenter study.BMC nephrology · 2025Article
- Evaluation of deep learning tools in medical diagnosis and treatment of cancer: research analysis of clinical and randomized clinical trials.Frontiers in network physiology · 2025Review
- Applications of artificial intelligence in urologic oncology.Investigative and clinical urology · 2024Review
- Article
- Application of artificial intelligence in the diagnosis and treatment of urinary tumors.Frontiers in oncology · 2024Review
- A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.Translational cancer research · 2023Article
- Review
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Authors and funding
7 authors.
Funding
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Abstract
objectiveDeep learning algorithms were used to develop a model for predicting the staging and grading of renal clear cell carcinoma to inform clinicians' treatment plans.
methodsClinical and pathological information was collected from 878 patients diagnosed with renal clear cell carcinoma in the Department of Urology, Peking University First Hospital. The patients were randomly assigned to the test set (n = 702) or the verification set (n = 176). Pathological staging and grading of renal clear cell carcinoma were predicted by preoperative clinical variables using deep learning algorithms. Receiver operating characteristic curves were used to evaluate the predictive accuracy as measured by the area under the receiver operating characteristic curve (AUC).
resultsFor tumor pathological staging, AUC values of 0.933, 0.947, and 0.948 were obtained using the BiLSTM, CNN-BiLSTM, and CNN-BiGRU models, respectively. For tumor pathological grading, the AUC values were 0.754, 0.720, and 0.770, respectively.
conclusionsThe proposed model for predicting renal clear cell carcinoma allows for accurate projection of the staging and grading of renal clear cell carcinoma and helps clinicians optimize individual treatment plans.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.