国际神经病学神经外科学杂志2025，Vol.52Issue(6)：48-55,8.DOI:10.16636/j.cnki.jinn.1673-2642.2025.06.007
基于多模态磁共振成像栖息地影像组学预测低级别胶质瘤患者预后
Application of multimodal magnetic resonance imaging-based habitat radiomics in predicting the prognosis of patients with low-grade glioma
摘要
Abstract
Objective To identify the functional subregions characterizing tumor heterogeneity from magnetic resonance imaging(MRI)sequence using the K-means clustering algorithm,and to construct a habitat risk score(HRS)model for predicting the prognosis of patients with low-grade glioma(LGG).Methods Clinical and imaging data were collected from 143 patients with LGG.The unsupervised K-means clustering algorithm was used to cluster the functional subregions of glioma habitats,and the radiomic features of different subregions were extracted.HRS was established for different subregions,and its correlation with overall survival(OS)was analyzed.External validation was performed for HRS.The multivariate Cox regression analysis was used to establish a clinical model and a habitat-clinical model,and the time-dependent receiver operating characteristic(ROC)curve was used to assess the performance of different models in predicting the prognosis of LGG patients.Results The K-means clustering algorithm identified the optimal partition of 3 subregions,and the Kaplan-Meier survival curve for median survival time showed that HRS2 constructed based on Habitat 2 subregion(the area with high perfusion and cellular density)was significantly associated with OS(P=0.001).The multivariate Cox regression analysis showed that age(hazard ratio[HR]=1.033),WHO grade(HR=1.290),and HRS2(HR=2.498)were influencing factors for the prognosis of LGG.A habitat-clinical model was established based on the above results,and external validation was performed for this model.For the training cohort,the clinical model and the habitat-clinical model had an area under the ROC curve(AUC)of 0.711 and 0.855,respectively,in predicting the OS of LGG patients,while in the validation cohort,the two models had an AUC of 0.709 and 0.857,respectively.Conclusions Habitat technology can quantify tumor heterogeneity by segmenting different tumor subregions.HRS developed based on high-risk subregions is an influencing factor for the prognosis of LGG,and the habitat-clinical model has a better effect than the clinical model in prognostic assessment.关键词
低级别胶质瘤/栖息地/影像组学/异质性/磁共振/脑肿瘤Key words
low-grade glioma/habitat/radiomics/heterogeneity/magnetic resonance imaging/brain tumor分类
医药卫生引用本文复制引用
李文菲,鲍欣然,顾涛,李彦国..基于多模态磁共振成像栖息地影像组学预测低级别胶质瘤患者预后[J].国际神经病学神经外科学杂志,2025,52(6):48-55,8.基金项目
河北省医学科学研究课题计划(20250236). （20250236）
