ArticleCancer imaging : the official publication of the International Cancer Imaging Society2021
CT-based multi-phase Radiomic models for differentiating clear cell renal cell carcinoma.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Review
- Radiomics analysis of CT imaging for predicting MIT family translocation renal cell carcinoma: a multicenter retrospective clinical study.BMC cancer · 2026Article
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Tumor heterogeneity on contrast-enhanced computed tomography outperforms attenuation ratio in differentiating renal cell carcinoma subtypes: a pathologic correlation study.The Pan African medical journal · 2026Article
- Machine learning-based multiparametric CT radiomics for predicting microvascular invasion before nephrectomy in clear cell renal cell carcinoma.Abdominal radiology (New York) · 2025Article
- Intratumoral and peritumoral radiomics using multi-phase contrast-enhanced CT for diagnosis of renal oncocytoma and chromophobe renal cell carcinoma: a multicenter retrospective study.Frontiers in oncology · 2025Article
- CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.The British journal of radiology · 2024Article
- CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.Clinical radiology · 2024Article
- Introduction to Special Issue Imaging in Cancer Diagnosis.Tomography (Ann Arbor, Mich.) · 2024Article
- Classification of nasal polyps and inverted papillomas using CT-based radiomics.Insights into imaging · 2023Article
- Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study.Insights into imaging · 2023Article
- Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.Insights into imaging · 2023Article
- Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review.Cancers · 2023Review
- Computed Tomography Urography: State of the Art and Beyond.Tomography (Ann Arbor, Mich.) · 2023Review
- Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer.Journal of personalized medicine · 2023Article
- Multi-phase-combined CECT radiomics models for Fuhrman grade prediction of clear cell renal cell carcinoma.Frontiers in oncology · 2023Article
- Radiomics vs radiologist in bladder and renal cancer. Results from a systematic review.Central European journal of urology · 2023Review
- A review of radiomics and genomics applications in cancers: the way towards precision medicine.Radiation oncology (London, England) · 2022Review
- Solitary fibrous tumor of the renal pelvis: A case report.World journal of clinical cases · 2022Article
- Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.Frontiers in medicine · 2022Article
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5 authors.
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Abstract
backgroundThe aim of the study is to compare the diagnostic value of models that based on a set of CT texture and non-texture features for differentiating clear cell renal cell carcinomas(ccRCCs) from non-clear cell renal cell carcinomas(non-ccRCCs).
methodsA total of 197 pathologically proven renal tumors were divided into ccRCC(n = 143) and non-ccRCC (n = 54) groups. The 43 non-texture features and 296 texture features that extracted from the 3D volume tumor tissue were assessed for each tumor at both Non-contrast Phase, NCP; Corticomedullary Phase, CMP; Nephrographic Phase, NP and Excretory Phase, EP. Texture-score were calculated by the Least Absolute Shrinkage and Selection Operator (LASSO) to screen the most valuable texture features. Model 1 contains the three most distinctive non-texture features with p < 0.001, Model 2 contains texture scores, and Model 3 contains the above two types of features.
resultsThe three models shown good discrimination of the ccRCC from non-ccRCC in NCP, CMP, NP, and EP. The area under receiver operating characteristic curve (AUC)values of the Model 1, Model 2, and Model 3 in differentiating the two groups were 0.748-0.823, 0.776-0.887 and 0.864-0.900, respectively. The difference in AUC between every two of the three Models was statistically significant (p < 0.001).
conclusionsThe predictive efficacy of ccRCC was significantly improved by combining non-texture features and texture features to construct a combined diagnostic model, which could provide a reliable basis for clinical treatment options.
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