Background: Extracting multiregional radiomic features from multiparametric MRI for predicting pretreatment survival in isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) patients is a promising approach. Methods: MRI data from 49 IDH wild-type glioblastoma patients pre-treatment were utilized. Diffusion and perfusion maps were generated, and tumor subregions segmented. Radiomic features were extracted for each tissue type and map. Feature selection on 1862 radiomic features identified 25 significant features. The Cox proportional-hazards model with LASSO regularization was used to perform survival analysis. Internal and external validation used a 38-patient training cohort and an 11-patient validation cohort. Statistical significance was set at p < 0.05. Results: Age and six radiomic features (shape and first and second order) from T1W, diffusion, and perfusion maps contributed to the final model. Findings suggest that a small necrotic subregion, inhomogeneous vascularization in the solid non-enhancing subregion, and edema-related tissue damage in the enhancing and edema subregions are linked to poor survival. The model’s C-Index was 0.66 (95% C.I. 0.54–0.80). External validation demonstrated good accuracy (AUC > 0.65) at all time points. Conclusions: Radiomics analysis, utilizing segmented perfusion and diffusion maps, provide predictive indicators of survival in IDH wild-type glioblastoma patients, revealing associations with microstructural and vascular heterogeneity in the tumor.

中文翻译：

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胶质母细胞瘤的生存结果预测：MRI 放射组学的见解

背景：从多参数 MRI 中提取多区域放射组学特征来预测异柠檬酸脱氢酶 (IDH) 野生型胶质母细胞瘤 (GBM) 患者的治疗前生存率是一种有前景的方法。方法：利用 49 名 IDH 野生型胶质母细胞瘤患者治疗前的 MRI 数据。生成扩散和灌注图，并对肿瘤亚区域进行分割。提取每种组织类型和图谱的放射组学特征。对 1862 个放射组学特征的特征选择确定了 25 个显着特征。使用 LASSO 正则化的 Cox 比例风险模型进行生存分析。内部和外部验证使用了 38 名患者的训练队列和 11 名患者的验证队列。统计显着性设定为 p < 0.05。结果：年龄和来自 T1W、扩散和灌注图的六种放射组学特征（形状、一阶和二阶）对最终模型有贡献。研究结果表明，小坏死分区、实体非增强分区中的不均匀血管化以及增强和水肿分区中与水肿相关的组织损伤与生存率低相关。该模型的 C 指数为 0.66 (95% CI 0.54–0.80)。外部验证在所有时间点都表现出良好的准确性（AUC > 0.65）。结论：放射组学分析利用分段灌注和扩散图，为 IDH 野生型胶质母细胞瘤患者的生存提供预测指标，揭示与肿瘤微结构和血管异质性的关联。