ReviewCancers2024
Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.
Review in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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
26 citing papers in PubMed, 38 citations in OpenAlex.
- [Use of artificial intelligence in clinical practice and hospitals].Urologie (Heidelberg, Germany) · 2026Review
- Deep learning-based automated identification of papillary bladder cancer from endoscopic still images using SE-ResNeXt-50.World journal of urology · 2026Article
- Navigating the Evolution of Urothelial Carcinoma Treatment: From Chemotherapy to Immunotherapy.Cancer medicine · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- A Novel Model for Muscle-Invasive Bladder Cancer Diagnosis Using Contrast-Enhanced Ultrasound.Annals of surgical oncology · 2026Article
- Review
- AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.Molecular genetics and genomics : MGG · 2026Review
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- Predicting recurrence of prostate cancer after radical treatment using AI models based on PET/CT radiomics: a dual-center study.Frontiers in oncology · 2026Article
- Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer.Journal of inflammation research · 2026Review
- Multimodal artificial intelligence in urologic precision oncology: from algorithm to translational medicine (a systemized narrative review).Frontiers in oncology · 2026Review
- A radiomics-driven machine learning model for predicting bladder cancer prognosis identifies genes associated with radiomic features.Clinical & experimental metastasis · 2025Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Optimizing bladder magnetic resonance imaging: accelerating scan time and improving image quality through deep learning.Abdominal radiology (New York) · 2025Article
- A multimodal automated deep learning-based model for predicting biochemical recurrence of prostate cancer following prostatectomy from baseline MRI, Presurgical clinical covariates.Clinical imaging · 2025Article
- Diagnostic performance of the clear cell likelihood score integrated with cystic degeneration or necrosis on MR imaging for identifying clear cell renal cell carcinoma in cT1 solid renal masses.Insights into imaging · 2025Article
- Inverted Urothelial Papilloma in a Young Woman: A Case Report and Review of Diagnostic Advances.Cureus · 2025Article
- Diagnostic accuracy and inter-reader agreement of the nacVI-RADS for bladder cancer treated with neoadjuvant chemotherapy: a prospective validation study.European radiology · 2025Article
- Bladder lesion detection using EfficientNet and hybrid attention transformer through attention transformation.Scientific reports · 2025Article
- Exploring the Genetic Orchestra of Cancer: The Interplay Between Oncogenes and Tumor-Suppressor Genes.Cancers · 2025Review
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 at 6 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.
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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.