ArticleJournal of magnetic resonance imaging : JMRI2024
Non-Invasive Tumor Grade Evaluation in Von Hippel-Lindau-Associated Clear Cell Renal Cell Carcinoma: A Magnetic Resonance Imaging-Based Study.
Article in Journal of magnetic resonance imaging : JMRI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 3 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 3 syntheses or guidelines pooled it, 3 citations in OpenAlex.
- Performance of Machine Learning Models Based on Medical Imaging in Predicting Pathological Grade of Clear Cell Renal Cell Carcinoma.Cancer medicine · 2026Pooled it
- Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.World journal of urology · 2026Pooled it
- MRI-based radiomics for differentiating high-grade from low-grade clear cell renal cell carcinoma: a systematic review and meta-analysis.Abdominal radiology (New York) · 2025Pooled it
- CT-based radiogenomic prediction of ICAM1 and RAET1E as biomarkers of NK cytotoxicity in clear cell renal cell carcinoma.Frontiers in immunology · 2026Article
- Intra- and inter-tumoural heterogeneity in von Hippel-Lindau disease-related renal cancer: a multimodal data study protocol.European radiology experimental · 2025Article
- State of the art review of AI in renal imaging.Abdominal radiology (New York) · 2025Review
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Authors and funding
18 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPathology grading is an essential step for the treatment and evaluation of the prognosis in patients with clear cell renal cell carcinoma (ccRCC). PURPOSE: To investigate the utility of texture analysis in evaluating Fuhrman grades of renal tumors in patients with Von Hippel-Lindau (VHL)-associated ccRCC, aiming to improve non-invasive diagnosis and personalized treatment. STUDY TYPE: Retrospective analysis of a prospectively maintained cohort. POPULATION: One hundred and thirty-six patients, 84 (61%) males and 52 (39%) females with pathology-proven ccRCC with a mean age of 52.8 ± 12.7 from 2010 to 2023. FIELD STRENGTH AND SEQUENCES: 1.5 and 3 T MRIs. Segmentations were performed on the T1-weighted 3-minute delayed sequence and then registered on pre-contrast, T1-weighted arterial and venous sequences. ASSESSMENT: A total of 404 lesions, 345 low-grade tumors, and 59 high-grade tumors were segmented using ITK-SNAP on a T1-weighted 3-minute delayed sequence of MRI. Radiomics features were extracted from pre-contrast, T1-weighted arterial, venous, and delayed post-contrast sequences. Preprocessing techniques were employed to address class imbalances. Features were then rescaled to normalize the numeric values. We developed a stacked model combining random forest and XGBoost to assess tumor grades using radiomics signatures. STATISTICAL TESTS: The model's performance was evaluated using positive predictive value (PPV), sensitivity, F1 score, area under the curve of receiver operating characteristic curve, and Matthews correlation coefficient. Using Monte Carlo technique, the average performance of 100 benchmarks of 85% train and 15% test was reported.
resultsThe best model displayed an accuracy of 0.79. For low-grade tumor detection, a sensitivity of 0.79, a PPV of 0.95, and an F1 score of 0.86 were obtained. For high-grade tumor detection, a sensitivity of 0.78, PPV of 0.39, and F1 score of 0.52 were reported. DATA
conclusionRadiomics analysis shows promise in classifying pathology grades non-invasively for patients with VHL-associated ccRCC, potentially leading to better diagnosis and personalized treatment. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.
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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.