ArticleCancers2020
Radiomic Features and Machine Learning for the Discrimination of Renal Tumor Histological Subtypes: A Pragmatic Study Using Clinical-Routine Computed Tomography.
Article in Cancers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detection of renal cell carcinoma: a systematic review and meta-analysis.BMC cancer · 2025Pooled it
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Multimodal deep learning for prediction of postoperative recurrence in clear cell renal cell carcinoma: a clinical-radiologic-pathologic approach.Frontiers in oncology · 2026Article
- A Robust Deep Learning Method with Uncertainty Estimation for the Pathological Classification of Renal Cell Carcinoma Based on CT Images.Journal of imaging informatics in medicine · 2025Article
- Using deep learning to differentiate among histology renal tumor types in computed tomography scans.BMC medical imaging · 2025Article
- Noninvasive prediction of T-score in IgA nephropathy using machine learning within the Oxford classification system.Frontiers in endocrinology · 2025Article
- Radiomics-based machine learning role in differential diagnosis between small renal oncocytoma and clear cells carcinoma on contrast-enhanced CT: A pilot study.European journal of radiology open · 2024Article
- Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.European radiology · 2024Article
- Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study.Abdominal radiology (New York) · 2024Article
- Stage IA papillary and chromophobe renal cell carcinoma: effectiveness of cryoablation and partial nephrectomy.Insights into imaging · 2024Article
- Review
- A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.European radiology · 2023Article
- Empowering Renal Cancer Management with AI and Digital Pathology: Pathology, Diagnostics and Prognosis.Biomedicines · 2023Review
- A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia.Scientific reports · 2023Article
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- Diagnostic accuracy of ultrasound-based multimodal radiomics modeling for fibrosis detection in chronic kidney disease.European radiology · 2023Article
- Research on automatic classification technology of kidney tumor and normal kidney tissue based on computed tomography radiomics.Frontiers in oncology · 2023Article
- A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC.Journal of oncology · 2022Article
- MRI-Based Radiomics and Urine Creatinine for the Differentiation of Renal Angiomyolipoma With Minimal Fat From Renal Cell Carcinoma: A Preliminary Study.Frontiers in oncology · 2022Article
- A CT-based radiomics nomogram for differentiation of renal oncocytoma and chromophobe renal cell carcinoma with a central scar-matched study.The British journal of radiology · 2022Article
Corrections and comments
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10 authors.
Funding
Abstract
This study evaluates the diagnostic performance of radiomic features and machine learning algorithms for renal tumor subtype assessment in venous computed tomography (CT) studies from clinical routine. Patients undergoing surgical resection and histopathological assessment of renal tumors at a tertiary referral center between 2012 and 2019 were included. Preoperative venous-phase CTs from multiple referring imaging centers were segmented, and standardized radiomic features extracted. After preprocessing, class imbalance handling, and feature selection, machine learning algorithms were used to predict renal tumor subtypes using 10-fold cross validation, assessed as multiclass area under the curve (AUC). In total,
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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.