ArticleFrontiers in oncology2020
Computed Tomography Radiomics for Predicting Pathological Grade of Renal Cell Carcinoma.
Article in Frontiers in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05782400 (Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma), which is not on this map. Cited by 36 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma
Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 58 citations in OpenAlex.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Microvascular invasion is associated with poor prognosis in renal cell carcinoma: a retrospective cohort study and meta-analysis.Frontiers in oncology · 2024Pooled it
- A Systematic Evaluation of Cohort Selection Criteria and Their Impact on Machine Learning Model Performance and Demographic Disparities in COVID-19 Outcomes: Cohort Study.JMIR formative research · 2026Article
- Noninvasive renal mass diagnostic models based on radiomic features and clinical data: a multi-center study.Abdominal radiology (New York) · 2026Article
- The Central Role of Imaging in Renal Cell Carcinoma: A Comprehensive Review of Tumor Aggressiveness, Histology, and Radiomics.Cancers · 2026Review
- Radiomics in Medical Imaging: Methods, Applications, and Challenges.Journal of imaging · 2026Review
- Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO/ISUP grading of clear cell renal cell carcinoma: a two-center validation study.BMC medical imaging · 2026Article
- Radiomics-based interpretable machine learning model from multiphasic CT imaging for predicting pathological grade in upper tract urothelial carcinoma: a multicenter study.Frontiers in oncology · 2026Article
- Prediction of infected pancreatic necrosis in patients with acute necrotizing pancreatitis based on ensemble machine learning model.World journal of emergency surgery : WJES · 2025Article
- A predictive model for WHO/ISUP pathologic grading of renal clear cell carcinoma based on CT radiomics: a multicenter study.BMC nephrology · 2025Article
- Preoperative prediction of WHO/ISUP grade of ccRCC using intratumoral and peritumoral habitat imaging: multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial intelligence links CT images to pathologic features and survival outcomes of renal masses.Nature communications · 2025Article
- Prediction of Distant Metastasis of Renal Cell Carcinoma Based on Interpretable Machine Learning: A Multicenter Retrospective Study.Journal of multidisciplinary healthcare · 2025Article
- Subregion-based radiomics analysis for predicting the histological grade of clear cell renal cell carcinoma.Frontiers in oncology · 2025Article
- Automated classification of pathological differentiation in head and neck squamous cell carcinoma using combined radiomics models from CET1WI and T2WI.Clinical oral investigations · 2024Article
- Radiogenomics Pilot Study: Association Between Radiomics and Single Nucleotide Polymorphism-Based Microarray Copy Number Variation in Diagnosing Renal Oncocytoma and Chromophobe Renal Cell Carcinoma.International journal of molecular sciences · 2024Article
- Application of artificial intelligence model in pathological staging and prognosis of clear cell renal cell carcinoma.Discover oncology · 2024Article
- Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study.Quantitative imaging in medicine and surgery · 2024Article
- Deep learning-based pathological prediction of lymph node metastasis for patient with renal cell carcinoma from primary whole slide images.Journal of translational medicine · 2024Article
- CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.Clinical radiology · 2024Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundClear cell renal cell carcinoma (ccRCC) is the most common renal cancer and it has the worst prognosis among all renal cancers. However, traditional radiological characteristics on computed tomography (CT) scans of ccRCC have been insufficient to predict the pathological grade of ccRCC before surgery.
methodsPatients with ccRCC were retrospectively enrolled into this study and were separated into two groups according to the World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grading system, i.e., low-grade (Grade I and II) group and high-grade (Grade III and IV) group. Traditional CT radiological characteristics such as tumor size, pre- and post-enhancing CT densities were assessed. In addition, radiomic texture analysis based on the CT imaging of the ccRCC were also performed. A CT-based machine learning method combining the traditional radiological characteristics and radiomic features was used in the predictive modeling for differentiating the low-grade from the high-grade ccRCC. Model performance was evaluated with the receiver operating characteristic curve (ROC) analysis.
resultsA total of 264 patients with pathologically confirmed ccRCC were included in this study. In this cohort, 206 patients had the low-grade tumors and 58 had the high-grade tumors. The model built with traditional radiological characteristics achieved an area under the curve (AUC) of 0.9175 (95% CI: 0.8765-0.9585) and 0.8088 (95% CI: 0.7064-0.9113) in differentiating the low-grade from the high-grade ccRCC for the training cohort and the validation cohort respectively. The model built with the radiomic textural features yielded an AUC value of 0.8170 (95% CI: 0.7353-0.8987) and 0.8017 (95% CI: 0.6878-0.9157) for the training cohort and the validation cohort, respectively. The combined model integrating both the traditional radiological characteristics and the radiomic textural features achieved the highest efficacy, with an AUC of 0.9235 (95% CI: 0.8646-0.9824) and an AUC of 0.9099 (95% CI: 0.8324-0.9873) for the training cohort and validation cohort, respectively.
conclusionWe developed a machine learning radiomic model achieving a satisfying performance in differentiating the low-grade from the high-grade ccRCC. Our study presented a potentially useful non-invasive imaging-focused method to predict the pathological grade of renal cancers prior to surgery.
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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.