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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

Japanese

グリオブラストーマにおける放射線生存研究は、異質な予測特性を報告しますが、この方法論自体からどれだけのものは稀に調査されます。我々は、治療前のMRIから463の放射線、解剖学および臨床特性と全体的な生存の関連を分析しました。705IDH野生型グリオブラストマ患者の3つの公的コホートから、3つの技術を使用して、ロゴランクテスト、メディア分裂後、ユニバリアットコックス回帰(両方のベンジャミーニー・ホフバーグ修正)およびSHAPで解釈されたXGBoost回帰器。ロゴランクテストは、99の特性、コックス回帰72およびSHAPのトップ20を選択

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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