Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO/ISUP grading of clear cell renal cell carcinoma: a two-center validation study

March 9, 2026BMC Medical ImagingOpen Access

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Key Points

Assess the effectiveness of DLRN models for predicting nuclear grade in clear cell renal cell carcinoma using multiphase CT imaging.
Used multiphase CT imaging data for model training and validation.
Developed deep learning radiomics models to analyze imaging features.
Carried out a two-center validation to ensure model reliability.”],
results
observed improvements in predictive accuracy for nuclear grade classification using DLRN models compared to conventional methods.
Observed improvements in predictive accuracy for nuclear grade classification using DLRN models compared to conventional methods.
The DLRN model showed a strong correlation with actual nuclear grades post-surgery.
Demonstrated that non-invasive CT-based assessments can effectively replace more invasive grading procedures.

Validation of CT-based models predicts nuclear grade in renal cancer, suggesting a non-invasive tool for assessment.

The DLRN model based on multiphase CT imaging provides an accurate, non-invasive tool for preoperative prediction of ccRCC nuclear grade.

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Yang et al. (2026) studied this question.

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