ArticleInsights into imaging2024
Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma.
Article in Insights into imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Non-invasive prediction of nuclear grade in renal cell carcinoma using CT-Based radiomics: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Pooled it
- A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma.BMC medical imaging · 2025Trial
- Noninvasive Preoperative Stratification of Renal Tumors for Aggressiveness and Prognosis.Bioengineering (Basel, Switzerland) · 2026Article
- Staging and imaging-based prognostication in renal cell carcinoma: current guidelines and practical considerations.Abdominal radiology (New York) · 2026Review
- CT-Based Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
- Peritumoral Fat Radiomics for Dual Prediction of TNM Stage and Histological Grade in Clear Cell Renal Cell Carcinoma: Discovery of Target-Specific Optimal Imaging Distances.Diagnostics (Basel, Switzerland) · 2026Article
- Multicenter peritumoral radiomics in oncology: advances, challenges, and future directions.Radiation oncology (London, England) · 2026Review
- An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.BMC medical imaging · 2026Article
- A multicenter study on preoperative WHO/ISUP grading of clear cell renal cell carcinoma using triphasic contrast-enhanced CT-based habitat imaging.BMC medical imaging · 2026Article
- Article
- Multiphase CT-Based Tumor and Peritumoral Radiomics for Characterization of Clear Cell Renal Cell Carcinoma.Journal of imaging informatics in medicine · 2026Article
- Commentary: Grading of clear cell renal cell carcinoma using diffusion MRI with a multimodal apparent diffusion model.Frontiers in oncology · 2026Article
- Article
- MRI-based habitat radiomics for predicting WHO/ISUP nuclear grade in clear cell renal cell carcinoma.Frontiers in oncology · 2026Article
- Integration of multi-scale radiomics and deep learning for Ki-67 prediction in clear cell renal carcinoma.NPJ precision oncology · 2025Article
- Biological Interpretable Machine Learning Model for Predicting Pathological Grading in Clear Cell Renal Cell Carcinoma Based on CT Urography Peritumoral Radiomics Features.Bioengineering (Basel, Switzerland) · 2025Article
- Multicenter study of CT-based deep learning for predicting preoperative T staging and TNM staging in clear cell renal cell carcinoma.BMC cancer · 2025Article
- Robustness of radiomics within photon-counting detector CT: impact of acquisition and reconstruction factors.European radiology · 2025Article
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7 authors.
Funding
Abstract
objectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).
methodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan-Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.
resultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).
conclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC. CRITICAL RELEVANCE STATEMENT: The intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies. KEY POINTS: The multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis. The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading. The nomogram and radiomics models have a broad range of clinical applications.
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