ArticleBMC medical imaging2022
A CT-based radiomics model for predicting renal capsule invasion in renal cell carcinoma.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis.Clinical imaging · 2023Pooled it
- 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
- Review
- A contrast-enhanced ultrasound-based nomogram for preoperative prediction of pseudocapsule status in localized clear cell renal cell carcinoma: a retrospective study.Translational andrology and urology · 2026Article
- Development and validation of a deep learning radiomics model for predicting capsular invasion in small renal masses: a multicenter retrospective study.Quantitative imaging in medicine and surgery · 2026Article
- A predictive model for WHO/ISUP pathologic grading of renal clear cell carcinoma based on CT radiomics: a multicenter study.BMC nephrology · 2025Article
- Prediction study of surrounding tissue invasion in clear cell renal cell carcinoma based on multi-phase enhanced CT radiomics.Abdominal radiology (New York) · 2025Article
- Navigating advanced renal cell carcinoma in the era of artificial intelligence.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Review
- Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors.Frontiers in oncology · 2025Article
- Preoperative prediction of renal fibrous capsule invasion in clear cell renal cell carcinoma using CT-based radiomics model.The British journal of radiology · 2024Article
- Review
- Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review.Cancers · 2024Review
- Prediction of clear cell renal cell carcinoma ≤ 4cm: visual assessment of ultrasound characteristics versus ultrasonographic radiomics analysis.Frontiers in oncology · 2024Article
- 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
- Investigating the Effects of Reconstruction Conditions on Image Quality and Radiomic Analysis in Photon-counting Computed Tomography.Journal of medical physicsArticle
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRenal cell carcinoma (RCC) is a heterogeneous group of kidney cancers. Renal capsule invasion is an essential factor for RCC staging. To develop radiomics models from CT images for the preoperative prediction of capsule invasion in RCC patients.
methodsThis retrospective study included patients with RCC admitted to the Chongqing University Cancer Hospital (01/2011-05/2019). We built a radiomics model to distinguish patients grouped as capsule invasion versus non-capsule invasion, using preoperative CT scans. We evaluated effects of three imaging phases, i.e., unenhanced phases (UP), corticomedullary phases (CMP), and nephrographic phases (NP). Five different machine learning classifiers were compared. The effects of tumor and tumor margins are also compared. Five-fold cross-validation and the area under the receiver operating characteristic curve (AUC) are used to evaluate model performance.
resultsThis study included 126 RCC patients, including 46 (36.5%) with capsule invasion. CMP exhibited the highest AUC (AUC = 0.81) compared to UP and NP, when using the forward neural network (FNN) classifier. The AUCs using features extracted from the tumor region were generally higher than those of the marginal regions in the CMP (0.81 vs. 0.73) and NP phase (AUC = 0.77 vs. 0.76). For UP, the best result was obtained from the marginal region (AUC = 0.80). The robustness analysis on the UP, CMP, and NP achieved the AUC of 0.76, 0.79, and 0.77, respectively.
conclusionsRadiomics features in renal CT imaging are associated with the renal capsule invasion in RCC patients. Further evaluation of the models is warranted.
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