ReviewAsian journal of urology2022
Artificial intelligence for renal cancer: From imaging to histology and beyond.
Review in Asian journal of urology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- Clinical Utility of Quantitative MRI Parameters for Differentiation of Renal Tumor Subtypes and Who Grades: A Multiparametric Approach with Internal Cortical Reference.Journal of clinical medicine · 2026Article
- Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction.Mayo Clinic proceedings. Digital health · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Construction and validation of deep learning-based pathomics signature model for predicting postoperative recurrence of patients with clear cell renal cell carcinoma.American journal of cancer research · 2026Article
- Real-world evidence in localized and locally advanced prostate cancer: applying artificial intelligence to electronic health records.BMC cancer · 2025Observational
- AI-driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models.BJUI compass · 2025Article
- Artificial Intelligence-Augmented Advancements in the Diagnostic Challenges Within Renal Cell Carcinoma.Journal of clinical medicine · 2025Review
- Synthetic data as an investigative tool in hypertension and renal diseases research.World journal of methodology · 2025Article
- Machine learning and deep learning models for the diagnosis of apical periodontitis: a scoping review.Clinical oral investigations · 2024Article
- Review
- Comparison of Perioperative, Functional, and Oncologic Outcomes of Open vs. Robot-Assisted Off-Clamp Partial Nephrectomy: A Propensity Scored Match Analysis.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence Reporting Guidelines' Adherence in Nephrology for Improved Research and Clinical Outcomes.Biomedicines · 2024Review
- Convolutional Neural Network Model for Segmentation and Classification of Clear Cell Renal Cell Carcinoma Based on Multiphase CT Images.Journal of imaging · 2023Article
- Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature.Diagnostics (Basel, Switzerland) · 2023Review
- Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- The Use of Radiomic Tools in Renal Mass Characterization.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence-Based Hyper Accuracy Three-Dimensional (HA3DDiagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions.Life (Basel, Switzerland) · 2022Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has made considerable progress within the last decade and is the subject of contemporary literature. This trend is driven by improved computational abilities and increasing amounts of complex data that allow for new approaches in analysis and interpretation. Renal cell carcinoma (RCC) has a rising incidence since most tumors are now detected at an earlier stage due to improved imaging. This creates considerable challenges as approximately 10%-17% of kidney tumors are designated as benign in histopathological evaluation; however, certain co-morbid populations (the obese and elderly) have an increased peri-interventional risk. AI offers an alternative solution by helping to optimize precision and guidance for diagnostic and therapeutic decisions. The narrative review introduced basic principles and provide a comprehensive overview of current AI techniques for RCC. Currently, AI applications can be found in any aspect of RCC management including diagnostics, perioperative care, pathology, and follow-up. Most commonly applied models include neural networks, random forest, support vector machines, and regression. However, for implementation in daily practice, health care providers need to develop a basic understanding and establish interdisciplinary collaborations in order to standardize datasets, define meaningful endpoints, and unify interpretation.
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What OpenQuestion holds
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.