ReviewCancers2020
Radiomics Applications in Renal Tumor Assessment: A Comprehensive Review of the Literature.
Review in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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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.
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Who cites it
33 citing papers in PubMed.
- Bosniak IIF renal cysts: natural history and the impact of lesion size on progression.International urology and nephrology · 2026Observational
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Noninvasive CT radiomics-clinical model accurately classifies anhydrous uric acid stones: a multicenter study.World journal of urology · 2025Article
- Multimodal imaging-based interpretable radiomics for differentiating brucella and tuberculosis spondylitis: a two-center study.BMC infectious diseases · 2025Article
- Radiomics-Based Classification of Clear Cell Renal Cell Carcinoma ISUP Grade: A Machine Learning Approach with SHAP-Enhanced Explainability.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Article
- CT imaging analysis differentiating papillary renal neoplasm with reverse polarity from papillary renal cell carcinoma: combined with a radiomics model.Japanese journal of radiology · 2024Article
- Predicting EGFR Status After Radical Nephrectomy or Partial Nephrectomy for Renal Cell Carcinoma on CT Using a Self-attention-based Model: Variable Vision Transformer (vViT).Journal of imaging informatics in medicine · 2024Article
- CT-based conventional radiomics and quantification of intratumoral heterogeneity for predicting benign and malignant renal lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment.World journal of gastrointestinal surgery · 2024Article
- Automatic grading of knee osteoarthritis with a plain radiograph radiomics model: combining anteroposterior and lateral images.Insights into imaging · 2024Article
- Imaging-based deep learning in kidney diseases: recent progress and future prospects.Insights into imaging · 2024Review
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- The Application of CT Radiomics in the Diagnosis of Vein Wall Invasion in Patients With Renal Cell Carcinoma Combined With Tumor Thrombus.The oncologist · 2024Article
- Automated Prediction of Neoadjuvant Chemoradiotherapy Response in Locally Advanced Cervical Cancer Using Hybrid Model-Based MRI Radiomics.Diagnostics (Basel, Switzerland) · 2023Article
- A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.European radiology · 2023Article
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- Exploratory Analysis of the Role of Radiomic Features in the Differentiation of Oncocytoma and Chromophobe RCC in the Nephrographic CT Phase.Life (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics texture analysis offers objective image information that could otherwise not be obtained by radiologists' subjective radiological interpretation. We investigated radiomics applications in renal tumor assessment and provide a comprehensive review. A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 March 2020 to identify English literature relevant to radiomics applications in renal tumor assessment. In total, 42 articles were included in the analysis and divided into four main categories: renal mass differentiation, nuclear grade prediction, gene expression-based molecular signatures, and patient outcome prediction. The main area of research involves accurately differentiating benign and malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat and oncocytoma. Nuclear grade prediction may enhance proper patient selection for risk-stratified treatment. Radiomics-predicted gene mutations may serve as surrogate biomarkers for high-risk disease, while predicting patients' responses to targeted therapies and their outcomes will help develop personalized treatment algorithms. Studies generally reported the superiority of radiomics over expert radiological interpretation. Radiomics provides an alternative to subjective image interpretation for improving renal tumor diagnostic accuracy. Further incorporation of clinical and imaging data into radiomics algorithms will augment tumor prediction accuracy and enhance individualized medicine.
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