ReviewTherapeutic advances in urology
Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.
Review in Therapeutic advances in urology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.
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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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.
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Who cites it
47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 99 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
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Ultrasound Radiomics for Differential Diagnosis of Renal Masses: A Systematic Review and Meta-Analysis.Technology in cancer research & treatmentPooled it
- Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.World journal of urology · 2026Article
- A comprehensive multi-task deep learning model for kidney cancer: histological subtyping, clinical staging, and anatomical complexity grading.European radiology · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Artificial Intelligence in Renal Imaging: A Multi-Dataset Study for Kidney Disease Classification.Biomedicines · 2026Article
- Preoperative predictors of adverse pathology and recurrence-free survival for patients with renal masses.BJUI compass · 2026Article
- Machine learning based classification of aggressive and malignant renal tumors from multimodal data.PLOS digital health · 2026Article
- Publication Trends of Research on Immune Tolerance After Kidney Transplantation: A Bibliometric Analysis from 1976 to 2024.Journal of multidisciplinary healthcare · 2026Article
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- Scaling of Haralick features with image bit depth and gray level co-occurrence matrix displacement vector for linear gradients.Computers in biology and medicine · 2025Article
- Malignant Transformation of Renal Cyst with Bosniak I Features.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- In Pursuit of KI-RADS: Toward a Single, Evidence-based Imaging Classification of Renal Masses.Radiology · 2025Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.European radiology · 2024Article
- Construction and verification of an innovative immune-related and hallmark gene sets prognostic model for bladder cancer.Translational cancer research · 2024Article
- Exosome-related lncRNA score: A value-based individual treatment strategy for predicting the response to immunotherapy in clear cell renal cell carcinoma.Cancer medicine · 2024Article
- The efficacy of subsequent therapy after failure of anti-PD-1 antibody in metastatic renal cell carcinoma.Translational cancer research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 11 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.
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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.