ArticlePloS one2019
Automated clear cell renal carcinoma grade classification with prognostic significance.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.iScience · 2026Article
- Artificial intelligence in genitourinary pathology.Histopathology · 2026Review
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Exploiting deep transfer learning based precise classification and grading of renal cell carcinoma using histopathological images.Scientific reports · 2025Article
- Predicting Grade and Patient Survival in Renal Cancer Using Machine Learning Analysis of Nucleolar Prominence.Cancer medicine · 2025Article
- Applicability of the regression approach for histological multi-class grading in clear cell renal cell carcinoma.Regenerative therapy · 2025Article
- A deep learning model to predict Ki-67 positivity in oral squamous cell carcinoma.Journal of pathology informatics · 2024Article
- Artificial Intelligence in Uropathology.Diagnostics (Basel, Switzerland) · 2024Review
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- Review
- A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma.World journal of urology · 2023Article
- Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images.Scientific reports · 2023Article
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
- Application of digital pathology and machine learning in the liver, kidney and lung diseases.Journal of pathology informatics · 2023Review
- RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features.Diagnostics (Basel, Switzerland) · 2022Article
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Artificial intelligence for renal cancer: From imaging to histology and beyond.Asian journal of urology · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed an automated 2-tiered Fuhrman's grading system for clear cell renal cell carcinoma (ccRCC). Whole slide images (WSI) and clinical data were retrieved for 395 The Cancer Genome Atlas (TCGA) ccRCC cases. Pathologist 1 reviewed and selected regions of interests (ROIs). Nuclear segmentation was performed. Quantitative morphological, intensity, and texture features (n = 72) were extracted. Features associated with grade were identified by constructing a Lasso model using data from cases with concordant 2-tiered Fuhrman's grades between TCGA and Pathologist 1 (training set n = 235; held-out test set n = 42). Discordant cases (n = 118) were additionally reviewed by Pathologist 2. Cox proportional hazard model evaluated the prognostic efficacy of the predicted grades in an extended test set which was created by combining the test set and discordant cases (n = 160). The Lasso model consisted of 26 features and predicted grade with 84.6% sensitivity and 81.3% specificity in the test set. In the extended test set, predicted grade was significantly associated with overall survival after adjusting for age and gender (Hazard Ratio 2.05; 95% CI 1.21-3.47); manual grades were not prognostic. Future work can adapt our computational system to predict WHO/ISUP grades, and validating this system on other ccRCC cohorts.
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Registered trials
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