Scholarlink

Purpose <p>To investigate the association between CT radiomics-based tumor heterogeneity and recurrence-free survival (RFS) in high-risk clear cell renal cell carcinoma (ccRCC) after nephrectomy, and to determine whether integrating CT radiomics with clinicopathological model enhances recurrence risk prediction for adjuvant treatment decisions.</p> Methods <p>This retrospective study included 194 patients with high-risk ccRCC undergoing nephrectomy. A radiomics model based on random survival forest was developed in the training set, using radiomics features extracted from pre-operative corticomedullary phase images. The performance of radiomics, Leibovich score, and the combined model were evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic curves (time-AUC), time-dependent Brier scores, and decision curve analysis in external test set.</p> Results <p>During follow-up, 62 patients experienced recurrence. The radiomics model demonstrated superior predictive performance compared to the Leibovich score, with higher time-dependent AUCs (1-year: 0.882 vs. 0.781; 2-year: 0.865 vs. 0.762; 3-year: 0.793 vs. 0.797; all <i>p</i> &lt; 0.05) and better calibration (lower Brier scores) in the test set. Decision curve analysis demonstrated that the combined model provided the highest net benefit, particularly for 2- to 3-year recurrence risk predictions.</p> Conclusions <p>For high-risk ccRCC, CT radiomics provides incremental prognostic value beyond conventional clinicopathological models, enabling more precise recurrence risk stratification. This approach bridges imaging and precision oncology, with potential to optimize surveillance protocols and adjuvant therapy trial design.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Radiomics-based tumor heterogeneity augments clinicopathological models for predicting recurrence in high-risk clear cell renal cell carcinoma after nephrectomy

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月12日

查看全文
  • Zhan Feng,
  • Piao Yang,
  • Yaoyao Wu,
  • Zhi Li,
  • Zhengyu Hu,
  • Wenting Lan

摘要

Purpose

To investigate the association between CT radiomics-based tumor heterogeneity and recurrence-free survival (RFS) in high-risk clear cell renal cell carcinoma (ccRCC) after nephrectomy, and to determine whether integrating CT radiomics with clinicopathological model enhances recurrence risk prediction for adjuvant treatment decisions.

Methods

This retrospective study included 194 patients with high-risk ccRCC undergoing nephrectomy. A radiomics model based on random survival forest was developed in the training set, using radiomics features extracted from pre-operative corticomedullary phase images. The performance of radiomics, Leibovich score, and the combined model were evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic curves (time-AUC), time-dependent Brier scores, and decision curve analysis in external test set.

Results

During follow-up, 62 patients experienced recurrence. The radiomics model demonstrated superior predictive performance compared to the Leibovich score, with higher time-dependent AUCs (1-year: 0.882 vs. 0.781; 2-year: 0.865 vs. 0.762; 3-year: 0.793 vs. 0.797; all p < 0.05) and better calibration (lower Brier scores) in the test set. Decision curve analysis demonstrated that the combined model provided the highest net benefit, particularly for 2- to 3-year recurrence risk predictions.

Conclusions

For high-risk ccRCC, CT radiomics provides incremental prognostic value beyond conventional clinicopathological models, enabling more precise recurrence risk stratification. This approach bridges imaging and precision oncology, with potential to optimize surveillance protocols and adjuvant therapy trial design.