Park Chae Jung, Kim Sooyon, Han Kyunghwa, Ahn Sung Soo, Kim Dain, Park Yae Won, Chang Jong Hee, Kim Se Hoon, Lee Seung-Koo
Department of Radiology, Research Institute of Radiological Science, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Department of Applied Statistics, Yonsei University, Seoul, Korea.
Yonsei Med J. 2024 May;65(5):283-292. doi: 10.3349/ymj.2023.0323.
Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value.
In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram.
The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, =0.002) and test sets (0.88 vs. 0.76, =0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively.
Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.
组织学2级和3级的低级别胶质瘤临床预后各异,因此需要进行风险分层。本研究旨在评估扩散加权和灌注加权磁共振成像(MRI)的影像组学是否能够预测低级别胶质瘤患者的总生存期(OS),并探讨其预后价值。
在这项回顾性研究中,从2012年1月至2019年2月病理确诊的低级别胶质瘤患者的表观扩散系数图、相对脑血容量图和Ktrans图中提取影像组学特征。根据选定特征计算的影像组学风险评分(RRS)构成一个影像组学模型。进行多变量Cox回归分析,包括临床特征和RRS。比较各模型在受试者操作特征曲线下的综合面积(iAUC)。将影像组学模型与临床特征相结合,绘制列线图。
本研究纳入129例患者(中位年龄44岁;四分位间距37 - 57岁;女性63例):90例用于训练集,39例用于测试集。RRS是OS的独立危险因素,风险比为6.01。在训练集(iAUC,0.82对0.72,P = 0.002)和测试集(0.88对0.76,P = 0.04)中,与临床模型相比,临床和影像组学联合模型在OS预测方面表现更优。影像组学列线图结合临床特征在训练集和测试集中实际与预测的OS之间表现出良好的一致性,C指数分别为0.83和0.87。
将扩散加权和灌注加权MRI影像组学加入临床特征可改善低级别胶质瘤的生存预测。