ArticleSpringerPlus2014
Tumor grade of clear cell renal cell carcinoma assessed by contrast-enhanced computed tomography.
Article in SpringerPlus, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 30 citations in OpenAlex.
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
- Development and validation of a CT algorithm based on intratumoral necrosis and tumor morphology to predict the nuclear grade of small (2-4 cm) solid clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- Multisequence combined magnetic resonance imaging radiomics model to noninvasively predict nuclear grade of clear cell renal cell carcinoma: interpretable model development.Revista da Associacao Medica Brasileira (1992) · 2025Article
- Multi-channel multiphase CT-based deep learning and radiomics fusion model for noninvasive pathological grading of clear cell renal cell carcinoma.Frontiers in oncology · 2025Article
- Centripetal filling and pathological insights: a rare case of sporadic renal hemangioblastoma with literature review.Frontiers in physiology · 2025Review
- Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.Scientific reports · 2024Article
- Dual-Energy Computed Tomography-Based Iodine Concentration Estimation for Evaluating Choroidal Malignant Melanoma Response to Treatment: Optimization and Primary Validation.Diagnostics (Basel, Switzerland) · 2022Article
- CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.European radiology · 2022Article
- CT-based transformer model for non-invasively predicting the Fuhrman nuclear grade of clear cell renal cell carcinoma.Frontiers in oncology · 2022Article
- Multiphase Contrast-Enhanced CT-Based Machine Learning Models to Predict the Fuhrman Nuclear Grade of Clear Cell Renal Cell Carcinoma.Cancer management and research · 2021Article
- Article
- Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.La Radiologia medica · 2020Article
- Texture analysis and machine learning algorithms accurately predict histologic grade in small (< 4 cm) clear cell renal cell carcinomas: a pilot study.Abdominal radiology (New York) · 2020Article
- Proteomic approaches for characterizing renal cell carcinoma.Clinical proteomics · 2020Review
- Article
- CT texture analysis: a potential tool for predicting the Fuhrman grade of clear-cell renal carcinoma.Cancer imaging : the official publication of the International Cancer Imaging Society · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to clarify the association between CT findings and Fuhrman grade of clear cell renal cell carcinoma (ccRCC). The study group consisted of 214 surgically proven ccRCC in 214 patients. Contrast-enhanced CT studies were retrospectively assessed for tumor size, cystic versus solid, calcification, heterogeneity of lesions, percentage of non-enhancing (necrotic) areas, and growth pattern. CT findings and Fuhrman grade were compared. Nineteen of 22 (86.4%) cystic ccRCC were low grade (Fuhrman grades 1-2). There was no significant correlation between tumor size and grade in cystic ccRCC (P = 0.43). In predominantly solid ccRCC, there was significant correlation between tumor size and grade (P < 0.0001). Thirty-eight of 43 (88.4%) infiltrative ccRCC were high grade (Fuhrman grades 3-4). Logistic regression showed tumor size and infiltrative growth were significantly associated with grades 3-4 (P = 0.00083 and P = 0.0059). Cystic ccRCC tends to be low grade. Infiltrative growth and larger tumor size may increase the likelihood of high grade ccRCC.
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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.