The value of dual-energy CT radiomics in evaluating vascular maturity and prognosis in clear cell renal cell carcinoma

This study aims to develop an integrated model based on dual-energy CT (DECT) radiomic features to predict vascular maturity and progression-free survival (PFS) in patients with clear cell renal cell carcinoma (ccRCC). A total of 87 ccRCC patients who underwent resection were randomly divided into training (n = 60) and validation (n = 27) groups at a 7:3 ratio. Radiomics models (single/multi-energy sequences), a clinical model, and a combined model were developed to predict vascular maturity (CD34+/α-SMA+ cells). Additionally, 17 patients were recruited from Center II as an external validation cohort for the multi-energy sequence models. A nomogram combining the radiomic score (Rad-score) and clinical features was constructed to visually predict patients’ PFS. Kaplan-Meier survival analysis assessed PFS differences between high and low CD34+/α-SMA+ cell groups. The median values of CD34+/α-SMA+ cells served as the cutoff for vascular maturity classification. The combined model demonstrated excellent performance, achieving an AUC of 0.967 in the validation set and 0.764 in the external validation set. Among the radiomics models, the multi-energy sequences model performed best, with an AUC of 0.833 in the validation set and 0.736 in the external validation set. Kaplan-Meier survival analysis revealed statistically significant PFS differences between CD34+/α-SMA+ cell groups. The combined model integrating Rad-score and clinical features showed promising predictive ability for assessing vascular maturity and PFS between CD34+/α-SMA+ groups in patients with ccRCC.

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Publication Details

Journal
Discover Oncology
Published
2025-11-10
DOI
https://doi.org/10.1007/s12672-025-03856-7
Primary Topic
Renal cell carcinoma treatment
Type
article
Field-Weighted Citation Impact
0.00

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article

The value of dual-energy CT radiomics in evaluating vascular maturity and prognosis in clear cell renal cell carcinoma

Peiji Song, Ruobing Li, Bing Xue, Aimei Ouyang et al.
Discover Oncology
Renal cell carcinoma treatment
article

The value of dual-energy CT radiomics in evaluating vascular maturity and prognosis in clear cell renal cell carcinoma

Peiji Song, Ruobing Li, Bing Xue, Aimei Ouyang, Kun Li, Zhongyuan Li, Haitao Sun
article en

Abstract

This study aims to develop an integrated model based on dual-energy CT (DECT) radiomic features to predict vascular maturity and progression-free survival (PFS) in patients with clear cell renal cell carcinoma (ccRCC). A total of 87 ccRCC patients who underwent resection were randomly divided into training (n = 60) and validation (n = 27) groups at a 7:3 ratio. Radiomics models (single/multi-energy sequences), a clinical model, and a combined model were developed to predict vascular maturity (CD34+/α-SMA+ cells). Additionally, 17 patients were recruited from Center II as an external validation cohort for the multi-energy sequence models. A nomogram combining the radiomic score (Rad-score) and clinical features was constructed to visually predict patients’ PFS. Kaplan-Meier survival analysis assessed PFS differences between high and low CD34+/α-SMA+ cell groups. The median values of CD34+/α-SMA+ cells served as the cutoff for vascular maturity classification. The combined model demonstrated excellent performance, achieving an AUC of 0.967 in the validation set and 0.764 in the external validation set. Among the radiomics models, the multi-energy sequences model performed best, with an AUC of 0.833 in the validation set and 0.736 in the external validation set. Kaplan-Meier survival analysis revealed statistically significant PFS differences between CD34+/α-SMA+ cell groups. The combined model integrating Rad-score and clinical features showed promising predictive ability for assessing vascular maturity and PFS between CD34+/α-SMA+ groups in patients with ccRCC.

Discover OncologyVol. 16(1)
Weifang Medical University (CN), People's Liberation Army 401 Hospital (CN), Jinan Central Hospital (CN), Shandong First Medical University (CN)
Department of Science and Technology of Shandong Province
Openalex Percentile: Top 52%
Renal cell carcinoma treatment
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