Cluster-based prognostication in glioblastoma: Unveiling heterogeneity based on diffusion and perfusion similarities

胶质母细胞瘤 层次聚类 聚类分析 医学 比例危险模型 磁共振弥散成像 扩散成像 脑血流 灌注 内科学 肿瘤科 核医学 磁共振成像 放射科 计算机科学 人工智能 癌症研究
作者
Martha Foltyn‐Dumitru,Tobias Keßler,Felix Sahm,Wolfgang Wick,Sabine Heiland,Martin Bendszus,Philipp Kickingereder,Marianne Schell
出处
期刊:Neuro-oncology [Oxford University Press]
卷期号:26 (6): 1099-1108 被引量:2
标识
DOI:10.1093/neuonc/noad259
摘要

Abstract Background While the association between diffusion and perfusion magnetic resonance imaging (MRI) and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival. Methods A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient normalized relative cerebral blood volume and relative cerebral blood flow were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using partition around medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership. Results Using the training dataset (231/289) we identified 2 stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (P = 0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (P ≤ 0.004 each). Conclusions Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion, and perfusion MRI in predicting survival rates of glioblastoma patients.
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