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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

Indonesian

Studi kelangsungan hidup radiomik pada glioblastoma melaporkan karakteristik prognostik heterogen, tetapi seberapa banyak ini berasal dari metodologi itu sendiri jarang diperiksa. Kami menganalisis asosiasi 463 karakteristik radiomik, anatomi dan klinis dari MRI pra-obat dengan kelangsungan hidup keseluruhan pada 705 pasien glioblastoma jenis liar IDH dari tiga kohorta publik, menggunakan tiga teknik: tes log-rank setelah split median, regresi Cox univariate (baik Benjamini-Hochberg diperbaiki) dan regresor XGBoost yang diinterpretasikan dengan SHAP. Tes log-rank memilih 99 karakteristik, regresi Cox 72 dan SHAP top 20; lima dipilih oleh tiga: usia, karakteristik tekstur peritumoral T2, jumlah komponen masker peritumoral yang terhubung dan dua tingkat intensitas maksimum gambar. Dua

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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