Scholarlink

<p>Occult pathological T3a (pT3a) upstaging in cT1b–T2a clear cell renal cell carcinoma (ccRCC) correlated with poor prognosis and necessitated modifications in surgical planning. However, predicting it preoperatively remains challenging. In this multicenter study involving 1661 patients across five institutions and the KiTS23 dataset, RENALNet, a 3D deep learning framework trained on nephrographic-phase CT, was developed and validated. RENALNet outperformed radiomics models, further enhancing diagnostic accuracy when combined with radiologists of varying experience. Grad-CAM visualizations concentrated on anatomically significant invasion sites, improving interpretability. Risk scores derived from RENALNet were found to correlate with Ki-67 proliferation indices and effectively stratified 5-year progression-free survival, demonstrating both biological and prognostic relevance. Transcriptomic analysis revealed that high RENALNet risk was associated with gene expression signatures enriched in pathways such as epithelial–mesenchymal transition, IL6–JAK–STAT3 signaling, and PI3K–Akt signaling, highlighting its link to tumor aggressiveness. RENALNet thus offers a biologically interpretable framework for risk stratification in ccRCC, supporting surgical decision-making and advancing the integration of radiogenomic deep learning into precision oncology.</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

Noninvasive prediction of occult pT3a upstaging in localized ccRCC with radiogenomic insights and prognostic relevance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月3日

查看全文
  • Shichao Li,
  • Chuhuai Wang,
  • Feng Li,
  • Mengmeng Gao,
  • Kangwen He,
  • Ziling Zhou,
  • Weinuo Qu,
  • Yaqi Shen,
  • Qian Chu,
  • Shan Wu,
  • Jinrong Qu,
  • Yudong Zhang,
  • Zhen Li

摘要

Occult pathological T3a (pT3a) upstaging in cT1b–T2a clear cell renal cell carcinoma (ccRCC) correlated with poor prognosis and necessitated modifications in surgical planning. However, predicting it preoperatively remains challenging. In this multicenter study involving 1661 patients across five institutions and the KiTS23 dataset, RENALNet, a 3D deep learning framework trained on nephrographic-phase CT, was developed and validated. RENALNet outperformed radiomics models, further enhancing diagnostic accuracy when combined with radiologists of varying experience. Grad-CAM visualizations concentrated on anatomically significant invasion sites, improving interpretability. Risk scores derived from RENALNet were found to correlate with Ki-67 proliferation indices and effectively stratified 5-year progression-free survival, demonstrating both biological and prognostic relevance. Transcriptomic analysis revealed that high RENALNet risk was associated with gene expression signatures enriched in pathways such as epithelial–mesenchymal transition, IL6–JAK–STAT3 signaling, and PI3K–Akt signaling, highlighting its link to tumor aggressiveness. RENALNet thus offers a biologically interpretable framework for risk stratification in ccRCC, supporting surgical decision-making and advancing the integration of radiogenomic deep learning into precision oncology.