Shao Yijun, Cen Harmony S, Dhananjay Anu, Pawan S J, Lei Xiaomeng, Gill Inderbir S, D'souza Anishka, Duddalwar Vinay A
Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, USA.
J Cancer Res Clin Oncol. 2025 Jun 12;151(6):186. doi: 10.1007/s00432-025-06240-8.
This study aimed to evaluate radiomic models' ability to predict hypoxia-related biomarker expression in clear cell renal cell carcinoma (ccRCC).
Clinical and molecular data from 190 patients were extracted from The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma dataset, and corresponding CT imaging data were manually segmented from The Cancer Imaging Archive. A panel of 2,824 radiomic features was analyzed, and robust, high-interscanner-reproducibility features were selected. Gene expression data for 13 hypoxia-related biomarkers were stratified by tumor grade (1/2 vs. 3/4) and stage (I/II vs. III/IV) and analyzed using Wilcoxon rank sum test. Machine learning modeling was conducted using the High-Performance Random Forest (RF) procedure in SAS Enterprise Miner 15.1, with significance at P < 0.05.
Descriptive univariate analysis revealed significantly lower expression of several biomarkers in high-grade and late-stage tumors, with KLF6 showing the most notable decrease. The RF model effectively predicted the expression of KLF6, ETS1, and BCL2, as well as PLOD2 and PPARGC1A underexpression. Stratified performance assessment showed improved predictive ability for RORA, BCL2, and KLF6 in high-grade tumors and for ETS1 across grades, with no significant performance difference across grade or stage.
The RF model demonstrated modest but significant associations between texture metrics derived from clinical CT scans, such as GLDM and GLCM, and key hypoxia-related biomarkers including KLF6, BCL2, ETS1, and PLOD2. These findings suggest that radiomic analysis could support ccRCC risk stratification and personalized treatment planning by providing non-invasive insights into tumor biology.
本研究旨在评估放射组学模型预测透明细胞肾细胞癌（ccRCC）中缺氧相关生物标志物表达的能力。
从癌症基因组图谱-肾透明细胞癌数据集中提取190例患者的临床和分子数据，并从癌症影像存档中手动分割出相应的CT影像数据。分析了一组2824个放射组学特征，并选择了稳健的、高扫描仪间再现性的特征。13种缺氧相关生物标志物的基因表达数据按肿瘤分级（1/2级与3/4级）和分期（I/II期与III/IV期）进行分层，并使用Wilcoxon秩和检验进行分析。在SAS Enterprise Miner 15.1中使用高性能随机森林（RF）程序进行机器学习建模，显著性水平为P < 0.05。
描述性单变量分析显示，几种生物标志物在高级别和晚期肿瘤中的表达显著降低，其中KLF6的降低最为明显。RF模型有效地预测了KLF6、ETS1和BCL2的表达，以及PLOD2和PPARGC1A的低表达。分层性能评估显示，高级别肿瘤中RORA、BCL2和KLF6的预测能力有所提高，各分级中ETS1的预测能力均有所提高，不同分级或分期之间的性能无显著差异。
RF模型显示，从临床CT扫描得出的纹理指标（如GLDM和GLCM）与包括KLF6、BCL2、ETS1和PLOD2在内的关键缺氧相关生物标志物之间存在适度但显著的关联。这些发现表明，放射组学分析可以通过提供对肿瘤生物学的非侵入性见解来支持ccRCC的风险分层和个性化治疗规划。
