Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Rd, Suzhou 215163, People's Republic of China.
Phys Med Biol. 2018 Feb 6;63(3):035044. doi: 10.1088/1361-6560/aaa609.
Our objective was to identify prognostic imaging biomarkers for hepatocellular carcinoma in contrast-enhanced computed tomography (CECT) with biological interpretations by associating imaging features and gene modules. We retrospectively analyzed 371 patients who had gene expression profiles. For the 38 patients with CECT imaging data, automatic intra-tumor partitioning was performed, resulting in three spatially distinct subregions. We extracted a total of 37 quantitative imaging features describing intensity, geometry, and texture from each subregion. Imaging features were selected after robustness and redundancy analysis. Gene modules acquired from clustering were chosen for their prognostic significance. By constructing an association map between imaging features and gene modules with Spearman rank correlations, the imaging features that significantly correlated with gene modules were obtained. These features were evaluated with Cox's proportional hazard models and Kaplan-Meier estimates to determine their prognostic capabilities for overall survival (OS). Eight imaging features were significantly correlated with prognostic gene modules, and two of them were associated with OS. Among these, the geometry feature volume fraction of the subregion, which was significantly correlated with all prognostic gene modules representing cancer-related interpretation, was predictive of OS (Cox p = 0.022, hazard ratio = 0.24). The texture feature cluster prominence in the subregion, which was correlated with the prognostic gene module representing lipid metabolism and complement activation, also had the ability to predict OS (Cox p = 0.021, hazard ratio = 0.17). Imaging features depicting the volume fraction and textural heterogeneity in subregions have the potential to be predictors of OS with interpretable biological meaning.
我们的目标是通过将影像学特征与基因模块相关联，在对比增强 CT（CECT）中识别肝癌的预后影像学生物标志物。我们回顾性分析了 371 名具有基因表达谱的患者。对于 38 名具有 CECT 成像数据的患者，进行了自动肿瘤内分区，得到了三个空间上不同的亚区。我们从每个亚区中总共提取了 37 个描述强度、几何形状和纹理的定量成像特征。经过稳健性和冗余分析后选择了影像学特征。从聚类中获得的基因模块因其预后意义而被选择。通过构建影像学特征与基因模块之间的关联图，并进行 Spearman 秩相关分析，获得了与基因模块显著相关的影像学特征。这些特征通过 Cox 比例风险模型和 Kaplan-Meier 估计进行评估，以确定它们对总生存期（OS）的预后能力。有 8 个影像学特征与预后基因模块显著相关，其中 2 个与 OS 相关。在这些特征中，与代表癌症相关解释的所有预后基因模块均显著相关的亚区几何特征体积分数与 OS 相关（Cox p=0.022，风险比=0.24）。与代表脂质代谢和补体激活的预后基因模块相关的纹理特征亚区聚类显著度也具有预测 OS 的能力（Cox p=0.021，风险比=0.17）。描绘亚区体积分数和纹理异质性的影像学特征具有成为具有可解释生物学意义的 OS 预测因子的潜力。
