ArticleAbdominal radiology (New York)2026
Preoperative identification of adverse pathology in clinically localized clear cell renal cell carcinoma: development and external validation of a contrast-enhanced ultrasound-based model.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo develop and externally validate a model combining contrast-enhanced ultrasound (CEUS), clinical, and laboratory features to identify adverse pathology in clinically localized clear cell renal cell carcinoma (ccRCC).
methodsThis retrospective study included 370 surgically treated patients with cT1-T2 ccRCC. Center 1 patients were randomly split into training (n = 210) and internal test (n = 91) sets; Center 2 provided external validation (n = 69). Adverse pathology comprised WHO/ISUP grade 3-4, tumor necrosis, invasion, or nodal metastasis. Two blinded readers assessed images. All 41 preoperative candidates entered logistic least absolute shrinkage and selection operator regression with stratified ten-fold cross-validation and the one-standard-error rule. Internal validation used 200 bootstrap resamples of the complete modeling procedure.
resultsAdverse pathology was present in 138 patients. Seven predictors were retained, including four CEUS features. Areas under the receiver operating characteristic curve (AUCs) were 0.810 (95% CI, 0.752-0.868), 0.875 (0.805-0.944), and 0.799 (0.688-0.910) in the training, internal test, and external cohorts, respectively; optimism-corrected training AUC was 0.747. External predictions were compressed (calibration slope, 2.380). At the training-derived threshold of 0.3051, adverse pathology was present in 61.5% (24/39) of external patients with positive results and 6.7% (2/30) with negative results, compared with 37.7% overall.
conclusionThe model showed moderate external discrimination and may support preoperative assessment by identifying patients at lower risk. Prospective multicenter studies are needed to validate individual risk estimates and assess clinical impact.
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