ArticleJournal of oncology2022
A Clinical Radiomics Nomogram Was Developed by Integrating Radiomics Signatures and Clinical Variables to Distinguish High-Grade ccRCC from Type 2 pRCC.
Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- CT-Based Radiomics in the Characterization of Solid Renal Tumors: A Systematic Review.Cancers · 2026Review
- Prognostic impact of nephrectomy and a prediction model for estimating the survival benefit of nephrectomy for patients with clear cell renal cell carcinoma (ccRCC).Translational andrology and urology · 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
- Multiparameter computed tomography (CT) radiomics signature fusion-based model for the preoperative prediction of clear cell renal cell carcinoma nuclear grade: a multicenter development and external validation study.Quantitative imaging in medicine and surgery · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Purpose: A nomogram was constructed by combining clinical factors and a CT-based radiomics signature to discriminate between high-grade clear cell renal cell carcinoma (ccRCC) and type 2 papillary renal cell carcinoma (pRCC). Methods: A total of 142 patients with 71 in high-grade ccRCC and seventy-one in type 2 pRCC were enrolled and split into a training cohort ( Results: The radiomics signature contained eight validated features from the CT images. The relative enhancement value of CMP (REV1) was an independent risk factor in the clinical factor model. The area under the curve (AUC) value of the clinical radiomics nomogram was 0.974 and 0.952 in the training and testing cohorts, respectively. In the training cohort, the decision curves of the nomogram demonstrated an added overall net advantage compared to the clinical factor model. Conclusion: A noninvasive prediction tool termed radiomics nomogram, combining clinical criteria and the radiomics signature, may accurately predict high-grade ccRCC and type 2 pRCC before surgery. It also has some importance in assisting clinicians in determining future treatment strategies.
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Registered trials
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