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Objectives <p>This study aimed to develop and validate a computed tomography (CT)-based intratumoral and peritumoral radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized clear cell renal cell carcinoma (ccRCC).</p> Methods <p>This two-center study included 447 patients with localized ccRCC. Patients from Center A were randomly split into a training set (<i>n</i> = 281) and an internal validation set (IVS) (<i>n</i> = 114) in a 7:3 ratio, while 52 patients from Center B formed the external validation set (EVS). Radiomics features from preoperative CT were obtained from the internal area of tumor (IAT), the internal and peritumoral areas of the tumor at 3&#xa0;mm (IPAT 3&#xa0;mm), and 5&#xa0;mm (IPAT 5&#xa0;mm). The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a radiomics score to develop radiomics model (RM). A clinical model (CM) was also established using significant clinical factors. Furthermore, a fusion model (FM) was developed by integrating independent predictors from both clinical factors and the radiomics score (Radscore) through multivariate Cox proportional hazards regression. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell’s concordance index (C-index), and decision curve analysis (DCA).</p> Results <p>Compared to both the IAT model and the IPAT 3&#xa0;mm model, the IPAT 5&#xa0;mm radiomics model demonstrated superior predictive performance for tumor recurrence (C-index: 0.924 vs. 0.915–0.923 in the IVS; 0.952 vs. 0.920–0.944 in the EVS). Therefore, the IPAT 5&#xa0;mm radiomics score was incorporated into the development of the fusion model. The FM exhibited outstanding predictive accuracy, achieving a C-index of 0.938 in the IVS, significantly outperforming the CM (0.889, <i>P</i> = 0.03). Notably, in the EVS, the RM surpassed both the CM and FM (C-index: 0.952 vs. 0.904–0.940, <i>P</i> &gt; 0.05). Furthermore, decision curve analysis indicated that the FM provided the highest net clinical benefit in the IVS, while both the FM and RM demonstrated substantially greater net benefit than the CM in the EVS.</p> Conclusions <p>The radiomics model and the fusion model, which integrate both intratumoral and peritumoral features, offer accurate prediction of recurrence risk in patients with localized ccRCC. These models have the potential to aid in personalized treatment planning, optimized surveillance strategies, and treatment strategies for patients with clear cell renal cell carcinoma.</p>

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A CT-based intratumoral and peritumoral radiomics nomogram for postoperative recurrence risk stratification in localized clear cell renal cell carcinoma

  • 期刊论文

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  • 发表日期：

    2025年5月16日

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  • Xiaoxia Li,
  • Yi Guo,
  • Shunfa Huang,
  • Funan Wang,
  • Chenchen Dai,
  • Jianjun Zhou,
  • Dengqiang Lin

摘要

Objectives

This study aimed to develop and validate a computed tomography (CT)-based intratumoral and peritumoral radiomics nomogram to improve the stratification of postoperative recurrence risk in patients with localized clear cell renal cell carcinoma (ccRCC).

Methods

This two-center study included 447 patients with localized ccRCC. Patients from Center A were randomly split into a training set (n = 281) and an internal validation set (IVS) (n = 114) in a 7:3 ratio, while 52 patients from Center B formed the external validation set (EVS). Radiomics features from preoperative CT were obtained from the internal area of tumor (IAT), the internal and peritumoral areas of the tumor at 3 mm (IPAT 3 mm), and 5 mm (IPAT 5 mm). The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a radiomics score to develop radiomics model (RM). A clinical model (CM) was also established using significant clinical factors. Furthermore, a fusion model (FM) was developed by integrating independent predictors from both clinical factors and the radiomics score (Radscore) through multivariate Cox proportional hazards regression. Model performance was assessed with Kaplan-Meier curves, time-dependent area under the curve (time-AUC), Harrell’s concordance index (C-index), and decision curve analysis (DCA).

Results

Compared to both the IAT model and the IPAT 3 mm model, the IPAT 5 mm radiomics model demonstrated superior predictive performance for tumor recurrence (C-index: 0.924 vs. 0.915–0.923 in the IVS; 0.952 vs. 0.920–0.944 in the EVS). Therefore, the IPAT 5 mm radiomics score was incorporated into the development of the fusion model. The FM exhibited outstanding predictive accuracy, achieving a C-index of 0.938 in the IVS, significantly outperforming the CM (0.889, P = 0.03). Notably, in the EVS, the RM surpassed both the CM and FM (C-index: 0.952 vs. 0.904–0.940, P > 0.05). Furthermore, decision curve analysis indicated that the FM provided the highest net clinical benefit in the IVS, while both the FM and RM demonstrated substantially greater net benefit than the CM in the EVS.

Conclusions

The radiomics model and the fusion model, which integrate both intratumoral and peritumoral features, offer accurate prediction of recurrence risk in patients with localized ccRCC. These models have the potential to aid in personalized treatment planning, optimized surveillance strategies, and treatment strategies for patients with clear cell renal cell carcinoma.