Hao Min, Yan Junyu, Wang Xiaochun, Tan Yan, Zhang Hui, Yang Guoqiang
Department of Radiology, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
College of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China.
PeerJ. 2025 Aug 20;13:e19906. doi: 10.7717/peerj.19906. eCollection 2025.
To investigate the practicability of a radiomics signature combined with clinical factors and molecular biomarkers for predicting overall survival (OS) in glioma patients.
Training ( = 331) and internal validation ( = 83) sets were retrospectively collected from the Cancer Image Archive/The Cancer Genome Atlas (TCIA/TCGA), and 165 patients from our hospital for an external validation set. The least absolute shrinkage and selection operator (LASSO) was developed to select features. A radiomics model was established for predicting OS based on contrast-enhanced T1-weighted imaging (CE-T1WI) and T2 fluid attenuated inversion recovery (T2FLAIR) images. The risk stratification value of the radiomics signature was explored using Kaplan-Meier survival analysis and the log-rank test. The integrated prediction model with selected clinical factors, molecular biomarkers, and radiomics features was constructed through multivariate Cox regression analysis. Radiomics prognostic performance and benefit were assessed for all cohorts.
The radiomics signature based on the combined sequences indicated exceptional predictive ability for OS in three cohorts and stratified glioma patients significantly into high-risk and low-risk groups ( < 0.0001). A nomogram incorporating O6-methylguanine-DNA-methyltransferase (MGMT), isocitrate dehydrogenase (IDH), pathological grade, age, and radiomics signature showed excellent evaluation performance and good calibration for predicting OS in the training (C-index = 0.774), internal (C-index = 0.750), and external (C-index = 0.776) validation cohorts.
The radiomics signature demonstrates superior predictive performance for OS in glioma patients and significant subgroup risk stratification efficiency. Moreover, the comprehensive model combining clinical factors, molecular biomarkers, and radiomics features further achieves a robust assessment of survival prognosis.
探讨将放射组学特征与临床因素及分子生物标志物相结合用于预测胶质瘤患者总生存期（OS）的可行性。
从癌症影像存档库/癌症基因组图谱（TCIA/TCGA）中回顾性收集训练集（n = 331）和内部验证集（n = 83），并收集我院165例患者作为外部验证集。采用最小绝对收缩和选择算子（LASSO）进行特征选择。基于对比增强T1加权成像（CE-T1WI）和T2液体衰减反转恢复序列（T2FLAIR）图像建立预测OS的放射组学模型。使用Kaplan-Meier生存分析和对数秩检验探索放射组学特征的风险分层值。通过多变量Cox回归分析构建包含所选临床因素、分子生物标志物和放射组学特征的综合预测模型。评估所有队列的放射组学预后性能和获益情况。
基于联合序列的放射组学特征在三个队列中对OS显示出卓越的预测能力，并将胶质瘤患者显著分层为高风险和低风险组（P < 0.0001）。包含O6-甲基鸟嘌呤-DNA甲基转移酶（MGMT）、异柠檬酸脱氢酶（IDH）、病理分级、年龄和放射组学特征的列线图在训练集（C指数 = 0.774）、内部验证集（C指数 = 0.750）和外部验证集（C指数 = 0.776）中对预测OS显示出优异的评估性能和良好的校准度。
放射组学特征在胶质瘤患者的OS预测中表现出卓越的性能及显著的亚组风险分层效率。此外，结合临床因素、分子生物标志物和放射组学特征的综合模型进一步实现了对生存预后的可靠评估。
