Qian Zenghui, Li Yiming, Sun Zhiyan, Fan Xing, Xu Kaibin, Wang Kai, Li Shaowu, Zhang Zhong, Jiang Tao, Liu Xing, Wang Yinyan
Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Aging (Albany NY). 2018 Oct 22;10(10):2884-2899. doi: 10.18632/aging.101594.
We aimed to identify a radiomic signature to be used as a noninvasive biomarker of prognosis in patients with lower-grade gliomas (LGGs) and to reveal underlying biological processes through comprehensive radiogenomic investigation.
We extracted 55 radiomic features from T2-weighted images of 233 patients with LGGs (training cohort: = 85; validation cohort: = 148). Univariate Cox regression and linear risk score formula were applied to generate a radiomic-based signature. Gene ontology analysis of highly expressed genes in the high-risk score group was conducted to establish a radiogenomic map. A nomogram was constructed for individualized survival prediction.
The six-feature radiomic signature stratified patients in the training cohort into low- or high-risk groups for overall survival ( = 0.0018). This result was successfully verified in the validation cohort ( = 0.0396). Radiogenomic analysis revealed that the prognostic radiomic signature was associated with hypoxia, angiogenesis, apoptosis, and cell proliferation. The nomogram resulted in high prognostic accuracy (C-index: 0.92, C-index: 0.70) and favorable calibration for individualized survival prediction in the training and validation cohorts.
Our results suggest a great potential for the use of radiomic signature as a biological surrogate in providing prognostic information for patients with LGGs.
我们旨在识别一种放射组学特征，用作低级别胶质瘤（LGG）患者预后的非侵入性生物标志物，并通过全面的放射基因组学研究揭示潜在的生物学过程。
我们从233例LGG患者的T2加权图像中提取了55个放射组学特征（训练队列：n = 85；验证队列：n = 148）。应用单变量Cox回归和线性风险评分公式生成基于放射组学的特征。对高风险评分组中高表达基因进行基因本体分析，以建立放射基因组图谱。构建列线图用于个体生存预测。
在训练队列中，六特征放射组学特征将患者分为总生存的低风险或高风险组（P = 0.0018）。这一结果在验证队列中得到成功验证（P = 0.0396）。放射基因组分析显示，预后放射组学特征与缺氧、血管生成、细胞凋亡和细胞增殖相关。列线图在训练和验证队列中对个体生存预测具有较高的预后准确性（C指数：0.92，C指数：0.70）和良好的校准。
我们的结果表明，放射组学特征作为一种生物学替代物，在为LGG患者提供预后信息方面具有巨大潜力。
