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How Robust Are Prognostic Radiomic Features? Comparing Selection Techniques on Pooled Glioblastoma Cohorts

Yannick Sodonougbo, Marlene Schnieders, Alexia Rizoudis, Santiago Cepeda, Frank Krämer, Dominik Müller · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26095 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们分析了从治疗前的MRI与705IDH野生类型 glioblastoma患者的整体生存的463射线,解剖和临床特征的关联,但这多少源自方法本身是罕见的研究,使用三种技术:在中间分裂后,一个Log-rank测试,单变的Cox回归(两种Benjamini-Hochberg纠正)和一个XGBoost回归器解释与SHAP。Log-rank测试选择了99个特征,Cox回归72和SHAP其前20名;五个都选择了三个:年龄,周围肿瘤分裂的特征,连接周围肿瘤组件的数量和两个图像水平的最大强度的回归器。两个预测生存测试共用51个特征,大约一半的上下

Machine translation (Meta M2M100); the English original follows.

Abstract

Radiomic survival studies in glioblastoma report heterogeneous prognostic features, but how much of this stems from the methodology itself is rarely examined. We analysed the association of 463 radiomic, anatomical and clinical features from pre-treatment MRI with overall survival in 705 IDH-wildtype glioblastoma patients from three public cohorts, using three techniques: a log-rank test after median split, univariate Cox regression (both Benjamini-Hochberg corrected) and an XGBoost regressor interpreted with SHAP. The log-rank test selected 99 features, Cox regression 72 and SHAP its top 20; five were selected by all three: age, a peritumoural T2 texture feature, the number of connected peritumoural mask components and two image-level intensity maxima. The two survival tests shared 51 features, about half of the SHAP top 20 were also selected by either test, and where selections overlapped, effect directions agreed. Many selections were PyRadiomics diagnostic descriptors that are not restricted to tumour voxels. Analytical pipeline choices, including ranking method, pre-filtering and descriptor handling, can substantially influence which features appear prognostic.

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Topics: Glioblastoma, Univariate, Radiomics, Proportional hazards model, Medicine, Ranking (information retrieval)

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