ArticleNPJ digital medicine2025
Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation.
Article in NPJ digital medicine, 2025. 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 Peritumoral and Perirenal Fat Radiomics in Renal Cell Carcinoma: A Systematic Review and Meta-Analysis of Grade, Stage, and Adherent Perinephric Fat Prediction.Journal of clinical medicine · 2026Review
- Multi-center and multi-reader comparison of abdominopelvic non-contrast and contrast-enhanced CT in the postoperative surveillance of gastrointestinal stromal tumors.Quantitative imaging in medicine and surgery · 2026Article
- Deciphering the mechanistic landscape of immune checkpoint blockade in ccRCC: from molecular drivers to therapeutic responses.Frontiers in immunology · 2026Review
- Machine learning-based radiomics from multiparametric MRI for predicting aggressive pathology in clear cell renal cell carcinoma.BMC medical imaging · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Preoperative detection of pT3a invasion in non-metastatic renal cell carcinoma (RCC) remains challenging with CT. This study developed and validated radiomic models using preoperative CT to identify pT3a invasions. Six models were trained and internally validated via nested cross-validation on 999 patients from one hospital. External validation included 313 patients from two hospitals and 204 patients from four TCIA datasets. A multi-reader multi-case study with seven radiologists evaluated the model's incremental value. The morphology model achieved the highest internal AUC (0.867, 95% CI: 0.866-0.869) and maintained performance in external validations (AUC = 0.895 and 0.842). When used as a second reader, it significantly improved junior radiologists' sensitivity and discrimination (AUC: 0.790 vs. 0.831, p < 0.001) without compromising specificity. This study demonstrates that CT-based radiomic models, particularly the morphology model, can reliably detect pT3a invasion and enhance diagnostic accuracy for junior radiologists, offering potential clinical utility in preoperative staging.
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Registered trials
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