Department of Adult Health, School of Nursing, University of Texas at Austin, 1710 Red River St, Austin, TX, D010078712, USA.
Department of Diagnostic Medicine, Dell School of Medicine, University of Texas at Austin, Austin, TX, USA.
Sci Rep. 2022 Nov 5;12(1):18783. doi: 10.1038/s41598-022-22387-7.
Diffuse gliomas are incurable brain tumors, yet there is significant heterogeneity in patient survival. Advanced computational techniques such as radiomics show potential for presurgical prediction of survival and other outcomes from neuroimaging. However, these techniques ignore non-lesioned brain features that could be essential for improving prediction accuracy. Gray matter covariance network (connectome) features were retrospectively identified from the T1-weighted MRIs of 305 adult patients diagnosed with diffuse glioma. These features were entered into a Cox proportional hazards model to predict overall survival with 10-folds cross-validation. The mean time-dependent area under the curve (AUC) of the connectome model was compared with the mean AUCs of clinical and radiomic models using a pairwise t-test with Bonferroni correction. One clinical model included only features that are known presurgery (clinical) and another included an advantaged set of features that are not typically known presurgery (clinical +). The median survival time for all patients was 134.2 months. The connectome model (AUC 0.88 ± 0.01) demonstrated superior performance (P < 0.001, corrected) compared to the clinical (AUC 0.61 ± 0.02), clinical + (AUC 0.79 ± 0.01) and radiomic models (AUC 0.75 ± 0.02). These findings indicate that the connectome is a feasible and reliable early biomarker for predicting survival in patients with diffuse glioma. Connectome and other whole-brain models could be valuable tools for precision medicine by informing patient risk stratification and treatment decision-making.
弥漫性神经胶质瘤是无法治愈的脑肿瘤，但患者的生存率存在显著差异。高级计算技术，如放射组学，显示出从神经影像学预测生存和其他结果的潜力。然而，这些技术忽略了非病变大脑特征，这些特征对于提高预测准确性可能至关重要。从 305 名被诊断为弥漫性神经胶质瘤的成年患者的 T1 加权 MRI 中回顾性地确定了灰质协方差网络（连接组）特征。这些特征被输入 Cox 比例风险模型，通过 10 折交叉验证预测总体生存率。使用配对 t 检验和 Bonferroni 校正，将连接组模型的平均时间依赖性曲线下面积（AUC）与临床和放射组学模型的平均 AUC 进行比较。一个临床模型仅包含已知术前的特征（临床），另一个模型包含一组不常见的术前已知特征（临床+）。所有患者的中位生存时间为 134.2 个月。与临床（AUC 0.61 ± 0.02）、临床+（AUC 0.79 ± 0.01）和放射组学模型（AUC 0.75 ± 0.02）相比，连接组模型（AUC 0.88 ± 0.01）表现出更好的性能（P<0.001，校正）。这些发现表明，连接组是预测弥漫性神经胶质瘤患者生存的一种可行且可靠的早期生物标志物。连接组和其他全脑模型可以通过告知患者风险分层和治疗决策，成为精准医学的有价值工具。
