ReviewWorld journal of urology2007
Reproducibility and reliability of tumor grading in urological neoplasms.
Review in World journal of urology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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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
32 citing papers in PubMed.
- Updates and controversies in contemporary grading of clear cell renal cell carcinoma and papillary renal cell carcinoma.Histopathology · 2026Review
- Receptor-Mediated SPION Labeling of CD4Nanomaterials (Basel, Switzerland) · 2025Article
- New histological risk grading system for prediction of lymph node metastasis in patients with penile cancer.Virchows Archiv : an international journal of pathology · 2025Article
- Fractal dimensions for tumour-related cell types of prostate cancer on histopathology images using multiple-threshold box counting algorithm.Biophysics and physicobiology · 2025Article
- Expected impact of MRI-targeted biopsy interreader variability among uropathologists on ProScreen prostate cancer screening trial: a pre-trial validation study.World journal of urology · 2024Article
- A narrative review of advances in the management of urothelial cancer: Diagnostics and treatments.Bladder (San Francisco, Calif.) · 2024Review
- Article
- Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review.Cancers · 2023Review
- An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.Journal of translational medicine · 2023Article
- Bladder cancer prognosis using deep neural networks and histopathology images.Journal of pathology informatics · 2022Article
- WWOX Loses the Ability to Regulate Oncogenic AP-2γ and Synergizes with Tumor Suppressor AP-2α in High-Grade Bladder Cancer.Cancers · 2021Article
- AI reality check when evaluating difficult to grade prostate cancers.Virchows Archiv : an international journal of pathology · 2021Article
- Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning.PloS one · 2021Article
- Applications of Topological Data Analysis in Oncology.Frontiers in artificial intelligence · 2021Review
- Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.Virchows Archiv : an international journal of pathology · 2020Article
- All change in the prostate cancer diagnostic pathway.Nature reviews. Clinical oncology · 2020Review
- Impact of a deep learning assistant on the histopathologic classification of liver cancer.NPJ digital medicine · 2020Article
- Computerized Image Analysis of Tumor Cell Nuclear Morphology Can Improve Patient Selection for Clinical Trials in Localized Clear Cell Renal Cell Carcinoma.Journal of pathology informatics · 2020Article
- A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Review of methods and applications of attenuation coefficient measurements with optical coherence tomography.Journal of biomedical optics · 2019Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathologic tumor grading reflects the degree of differentiation of a given tumor and for most urological tumors grading is an important factor in predicting their biological aggressiveness. Consequently, the clinical management of tumor patients is often strongly influenced by the tumor grade, provided by pathologists. This implicates that an ideal grading system should not only be of high prognostic relevance, but also of high reproducibility among different pathologists. To this end individual histological grading systems have been developed for different tumor entities and even for a given tumor type several grading systems have been proposed. All of these grading systems possess an inherent degree of subjectivity and consequently, both intra- and interobserver variability exist. In this review, grading systems for the most frequent urological tumors (i.e. prostate cancer, renal cell carcinoma, and urothelial tumors) are mentioned and data on the reproducibility and reliability of the most commonly used grading systems are summarized.
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Registered trials
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