ArticleBMC cancer2024
CT features based preoperative predictors of aggressive pathology for clinical T1 solid renal cell carcinoma and the development of nomogram model.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Improving Clear Cell Renal Cell Carcinoma Diagnosis: Modified CT Algorithms with Simplified Heterogeneity Scoring.Annals of surgical oncology · 2026Article
- Using flow ratio of peritumoral artery/renal aorta from DSA imaging to predict high-risk clear cell renal cell carcinoma.World journal of urology · 2026Article
- Preoperative predictors of adverse pathology and recurrence-free survival for patients with renal masses.BJUI compass · 2026Article
- A preoperative CT-based radiological score for predicting recurrence in papillary renal cell carcinoma: a multicenter validation study.Insights into imaging · 2025Article
- Development and validation of a CT-based nomogram to preoperative prediction of pancreatic neuroendocrine tumors (pNETs) grade.Abdominal radiology (New York) · 2025Article
- Clinical, Pathologic, and Genetic Correlates of Nuclear Grade in von Hippel-Lindau-associated Renal Cell Carcinoma.Urology · 2025Article
- Development and validation of a CT algorithm based on intratumoral necrosis and tumor morphology to predict the nuclear grade of small (2-4 cm) solid clear cell renal cell carcinoma.BMC medical imaging · 2025Article
- Glomerular filtration rate in patients with renal space-occupying masses using combination of SPECTFrontiers in oncology · 2025Article
- Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology.World journal of surgical oncology · 2024Article
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6 authors at 3 institutions in 1 country.
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Abstract
backgroundWe aimed to identify preoperative predictors of aggressive pathology for cT1 solid renal cell carcinoma (RCC) by combining clinical features with qualitative and quantitative CT parameters, and developed a nomogram model.
methodsWe conducted a retrospective study of 776 cT1 solid RCC patients treated with partial nephrectomy (PN) or radical nephrectomy (RN) between 2018 and 2022. All patients underwent four-phase contrast-enhanced CT scans and the CT parameters were obtained by two experienced radiologists using region of interest (ROI). Aggressive pathology was defined as patients with nuclear grade III-IV; upstage to pT3a; type II papillary renal cell carcinoma (pRCC), collecting duct or renal medullary carcinoma, unclassified RCC or sarcomatoid/rhabdoid features. Univariate and multivariate logistic analyses were used to determine significant predictors and develop the nomogram model. To evaluate the accuracy and clinical utility of the nomogram model, we used the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis (DCA), risk stratification, and subgroup analysis.
resultsOf the 776 cT1 solid RCC patients, 250 (32.2%) had aggressive pathological features. The interclass correlation coefficient (ICC) of CT parameters accessed by two reviewers ranged from 0.758 to 0.982. Logistic regression analyses showed that neutrophil-to-lymphocyte ratio (NLR), distance to the collecting system, CT necrosis, tumor margin irregularity, peritumoral neovascularity, and RER-NP were independent predictive factors associated with aggressive pathology. We built the nomogram model using these significant variables, which had an area under the curve (AUC) of 0.854 in the ROC curve.
conclusionsOur research demonstrated that preoperative four-phase contrast-enhanced CT was critical for predicting aggressive pathology in cT1 solid RCC, and the constructed nomogram was useful in guiding patient treatment and postoperative follow-up.
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