ArticleJapanese journal of radiology2024
CT imaging analysis differentiating papillary renal neoplasm with reverse polarity from papillary renal cell carcinoma: combined with a radiomics model.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Percutaneous thermal ablation for renal cell carcinoma: a comprehensive review of current evidence and future directions.La Radiologia medica · 2026Review
- Papillary renal neoplasm with reverse polarity: A case report.The Journal of international medical research · 2026Article
- 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
- Illuminating radiogenomic signatures in pediatric-type diffuse gliomas: insights into molecular, clinical, and imaging correlations. Part I: high-grade group.La Radiologia medica · 2025Review
- Two Cases of Papillary Renal Neoplasm with Reverse Polarity: Radiologic-Pathologic Correlation.Yonago acta medica · 2025Article
- Papillary renal neoplasm with reverse polarity: a case report and literature review.Frontiers in oncology · 2025Article
- Bilateral synchronous papillary renal neoplasm with reverse polarity and renal cell carcinoma with fibromyomatous stroma: a case report and review of the literature.Frontiers in oncology · 2025Article
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6 authors.
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Abstract
purposeTo assess the computed tomography (CT) findings of papillary renal neoplasm with reverse polarity (PRNRP) and develop a radiomics-based model to distinguish PRNRPs from papillary renal cell carcinomas (PRCCs). MATERIALS AND
methodsWe analyzed 31 PRNRPs and 68 PRCCs using preoperative kidney CT. We evaluated CT features that could discriminate PRNRPs from PRCCs. A radiomics signature was constructed using features selected through a least absolute shrinkage and selection operator algorithm. A radiomics-based model incorporating a radiomics signature and subjective CT parameters using multivariate logistic regression was developed. The diagnostic performance of the CT parameters, radiomics model, and their combination was evaluated using the area under the curve (AUC).
resultsMost of PRNRPs had a round shape (93.5%), well-defined margin (100%), and persistent enhancement (77.4%). Compared with PRCC, PRNRPs exhibited distinct CT features including small size (16.7 vs. 37.7 mm, P < 0.001), heterogeneity (64.5 vs. 32.4%, P = 0.004), enhancing dot sign (16.1 vs. 1.5%, P = 0.001), and high attenuation in pre-contrast CT (44.2 vs. 35.5 HU, P = 0.003). Multivariate analysis revealed smaller mass size (odds ratio [OR]: 0.9; 95% confidence interval [CI] 0.9-1.0, P = 0.013), heterogeneity (OR: 8.8; 95% CI 1.9-41.4, P = 0.006), and higher attenuation in pre-contrast CT (OR: 1.1; 95% CI 1.0-1.2, P = 0.011) as significant independent factors for identifying PRNRPs. The diagnostic performance of the combination model was excellent (AUC: 0.923).
conclusionSmaller tumor size, heterogeneity, and higher attenuation in pre-contrast CT were more closely associated with PRNRPs than with PRCCs. Though the retrospective design, small sample size, and single-center data of this study may affect the generalizability of the findings, combining subjective CT features with a radiomics model is beneficial for distinguishing PRNRPs from PRCCs.
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