ArticleMedicine2019
Prediction of ISUP grading of clear cell renal cell carcinoma using support vector machine model based on CT images.
Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 3 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 3 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis.Clinical imaging · 2023Pooled it
- Accuracy of CT texture analysis for differentiating low-grade and high-grade renal cell carcinoma: systematic review and meta-analysis.BMJ open · 2021Pooled it
- CT-based radiomics for differentiating renal tumours: a systematic review.Abdominal radiology (New York) · 2021Pooled it
- Renal Cell Carcinoma Texture Analysis With the KiTS23 Dataset: A Retrospective Study.Advances in urology · 2026Article
- Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study.JMIR medical informatics · 2025Article
- Artificial intelligence-based multimodal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study.International journal of surgery (London, England) · 2025Article
- Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability.Diagnostics (Basel, Switzerland) · 2025Article
- Development and validation of a CT based radiomics nomogram for preoperative prediction of ISUP/WHO grading in renal clear cell carcinoma.Abdominal radiology (New York) · 2025Article
- CT-based deep learning for preoperative prediction of pathological grading of renal clear cell carcinoma.Frontiers in oncology · 2025Article
- Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.Medical physics · 2025Article
- Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.Insights into imaging · 2024Article
- Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.Scientific reports · 2024Article
- Article
- Machine learning algorithms being an auxiliary tool to predict the overall survival of patients with renal cell carcinoma using the SEER database.Translational andrology and urology · 2024Article
- Machine learning-based multiparametric MRI radiomics nomogram for predicting WHO/ISUP nuclear grading of clear cell renal cell carcinoma.Frontiers in oncology · 2024Article
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- Radiogenomics in Renal Cancer Management-Current Evidence and Future Prospects.International journal of molecular sciences · 2023Review
- Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.Central European journal of urology · 2023Review
- Usefulness of computed tomography textural analysis in renal cell carcinoma nuclear grading.Journal of medical imaging (Bellingham, Wash.) · 2022Article
- Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo explore whether radiomics combined with computed tomography (CT) images can be used to establish a model for differentiating high grade (International Society of Urological Pathology [ISUP] grade III-IV) from low-grade (ISUP I-II) clear cell renal cell carcinoma (ccRCC).
methodsFor this retrospective study, 3-phase contrast-enhanced CT images were collected from 227 patients with pathologically confirmed ISUP-grade ccRCC (155 cases in the low-grade group and 72 cases in the high-grade group). First, we delineated the largest dimension of the tumor in the corticomedullary and nephrographic CT images to obtain the region of interest. Second, variance selection, single variable selection, and the least absolute shrinkage and selection operator were used to select features in the corticomedullary phase, nephrographic phase, and 2-phase union samples, respectively. Finally, a model was constructed using the optimal features, and the receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the predictive performance of the features in the training and validation queues. A Z test was employed to compare the differences in AUC values.
resultsThe support vector machine (SVM) model constructed using the screening features for the 2-stage joint samples can effectively distinguish between high- and low-grade ccRCC, and obtained the highest prediction accuracy. Its AUC values in the training queue and the validation queue were 0.88 and 0.91, respectively. The results of the Z test showed that the differences between the 3 groups were not statistically significant.
conclusionThe SVM model constructed by CT-based radiomic features can effectively identify the ISUP grades of ccRCC.
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Registered trials
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.