ReviewDiagnostics (Basel, Switzerland)2021
The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis 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.
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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
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- The role of AI-powered molecular profiling in the diagnosis and management of cancers of unknown primary: a case report and literature review.Frontiers in oncology · 2026Article
- Trends in artificial intelligence and machine learning for renal cancer.Discover oncology · 2025Article
- RCC-Supporter: supporting renal cell carcinoma treatment decision-making using machine learning.BMC medical informatics and decision making · 2024Article
- Article
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Future of Artificial Intelligence Applications in Cancer Care: A Global Cross-Sectional Survey of Researchers.Current oncology (Toronto, Ont.) · 2023Article
- Deep learning techniques for imaging diagnosis of renal cell carcinoma: current and emerging trends.Frontiers in oncology · 2023Review
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.Frontiers in physiology · 2022Article
- Multimodal ultrasound fusion network for differentiating between benign and malignant solid renal tumors.Frontiers in molecular biosciences · 2022Article
- [Predicting postoperative recurrence of stage Ⅰ-Ⅲ renal clear cell carcinoma based on preoperative CT radiomics feature nomogram].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- Proteomics, Personalized Medicine and Cancer.Cancers · 2021Review
- Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review.Therapeutic advances in urologyReview
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.
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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.