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<p>This study identifies amide proton transfer (APT) imaging radiomic features that correlate with glioblastoma survival and explore the relationship of combined radiomic data and biomarkers such as age and MGMT methylation status to survival prognosis. In a retrospective study of 66 glioblastoma patients (2022–2023), SlicerBrats was used for automatic tumor segmentation and 504 radiomic features were extracted from APT images. A two-step feature reduction process was applied: (1) Pearson correlation reduced the features to 59 by mitigating multicollinearity, and (2) a Cox model with best subset selection identified the optimal features using the lowest Akaike Information Criterion (AIC). The Goodness-of-Fit (GOF) test was used to evaluate model adequacy. Bootstrapping with 1000 iterations was used to compute the confidence interval for the concordance index (C-index). Univariate Cox models assessed individual radiomics, while models with multiple features evaluated radiomics alone, with age, MGMT methylation, and both. Kaplan-Meier and log-rank tests were performed to stratify survival groups. The radiomics-only model showed the highest C-index (0.74), AIC (191.35), and GOF (<i>p</i> &lt; 0.005). Adding age maintained the C-index (0.74) but raised AIC to 193.16 (<i>p</i> = 0.01). The radiomics + MGMT model achieved a C-index of 0.73 and the lowest AIC (190.15). Kaplan-Meier curves and log-rank tests confirmed all models significantly distinguished survival groups (<i>p</i> &lt; 0.05). APT-derived radiomic features, especially from edema and necrosis, correlate with glioblastoma survival time. Adding MGMT methylation improved predictive accuracy, achieving the lowest partial AIC and a C-index of 0.73. Further validation in larger cohorts is recommended.</p>

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Radiomic correlates with overall survival in glioblastoma: amide proton transfer (APT) imaging-based feature analysis and integration of clinical factors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Saima Safdar,
  • Nathaniel Barry,
  • Michael Bynevelt,
  • Adriano Polpo,
  • Suki Gill,
  • Pejman Rowshanfarzad,
  • Martin A. Ebert

摘要

This study identifies amide proton transfer (APT) imaging radiomic features that correlate with glioblastoma survival and explore the relationship of combined radiomic data and biomarkers such as age and MGMT methylation status to survival prognosis. In a retrospective study of 66 glioblastoma patients (2022–2023), SlicerBrats was used for automatic tumor segmentation and 504 radiomic features were extracted from APT images. A two-step feature reduction process was applied: (1) Pearson correlation reduced the features to 59 by mitigating multicollinearity, and (2) a Cox model with best subset selection identified the optimal features using the lowest Akaike Information Criterion (AIC). The Goodness-of-Fit (GOF) test was used to evaluate model adequacy. Bootstrapping with 1000 iterations was used to compute the confidence interval for the concordance index (C-index). Univariate Cox models assessed individual radiomics, while models with multiple features evaluated radiomics alone, with age, MGMT methylation, and both. Kaplan-Meier and log-rank tests were performed to stratify survival groups. The radiomics-only model showed the highest C-index (0.74), AIC (191.35), and GOF (p < 0.005). Adding age maintained the C-index (0.74) but raised AIC to 193.16 (p = 0.01). The radiomics + MGMT model achieved a C-index of 0.73 and the lowest AIC (190.15). Kaplan-Meier curves and log-rank tests confirmed all models significantly distinguished survival groups (p < 0.05). APT-derived radiomic features, especially from edema and necrosis, correlate with glioblastoma survival time. Adding MGMT methylation improved predictive accuracy, achieving the lowest partial AIC and a C-index of 0.73. Further validation in larger cohorts is recommended.