Aim Heterogeneity assessment of contrast-enhancing tumor regions on pre-radiotherapy magnetic resonance imaging (MRI) holds prognostic significance in glioblastoma (GBM), given its association with tumor progression and overall survival (OS). This study aimed to evaluate the prognostic value of radiomic features extracted from contrast-enhancing tumor regions on pre-radiotherapy MRI across multiple imaging sequences. Methods A total of 74 GBM patients from The Cancer Imaging Archive were analyzed and categorized into short- and long-term survival groups based on their median survival of 18.8 months. Radiomic features were extracted from original, wavelet-filtered images, and Laplacian of Gaussian (LoG)-filtered images both individually and in combination across four MRI sequences: T1-weighted contrast-enhanced (T1WCE), T1W, T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Feature selection was conducted using least absolute shrinkage and selection operator (Lasso) and radiomic scores (Rad-scores) were constructed. The capability of Rad-scores in stratifying OS was evaluated using receiver operating characteristic (ROC) and calibration curve analyses, along with Kaplan–Meier survival analysis. Furthermore, clinical characteristics were integrated to determine whether this combination enhanced the stratification performance. Results ROC curve analysis of the Rad-scores demonstrated that wavelet features markedly improved performance compared to the combination of original and LoG features (AUC: 0.90 [0.81–0.96] vs. 0.83 [0.72–0.90]; BH-adjusted p = 0.0183) in differentiating between short- and long-term survivors. Combining wavelet and LoG-filtered features significantly improved survival stratification, outperforming the combination of original and LoG features (AUC: 0.92 [0.84–0.97] vs. 0.83 [0.70–0.90]; BH-adjusted p = 0.0229). Additionally, Kaplan–Meier survival analyses and log-rank tests further confirmed significant correlations between all radiomic feature groups and OS. Integrating clinical characteristics did not notably enhance the discriminatory power (p > 0.05). All the Rad-scores and the combined Rad-sores with clinical data, revealed good agreement between the predicted and actual OS probabilities (p > 0.05), except for the combination of original and LoG features and wavelet features with clinical characteristics (p <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40001_2025_3333_Article_IEq1.gif" Format="GIF" Height="11" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$<$</EquationSource> <EquationSource Format="MATHML"><math> <mo><</mo> </math></EquationSource> </InlineEquation> 0.05). Conclusion Radiomic analysis of contrast-enhancing tumors on pre-radiotherapy MRI sequences provides strong prognostic insights in GBMs. These findings demonstrate the potential of integrating wavelet-filtered radiomic features for risk stratification; prospective validation is required before clinical implementation.

Correlation of contrast-enhancing tumor region on pre-radiotherapy MRI with overall survival in glioblastoma

期刊论文
出版模式：

开放获取
发表日期：

2025年11月14日

查看全文

Atefeh Mahmoudi,
Arash Zare Sadeghi,
Hamed Iraji,
Maedeh Barahman,
Elmira Yazdani,
Seied Rabi Mahdavi

摘要

Aim

Heterogeneity assessment of contrast-enhancing tumor regions on pre-radiotherapy magnetic resonance imaging (MRI) holds prognostic significance in glioblastoma (GBM), given its association with tumor progression and overall survival (OS). This study aimed to evaluate the prognostic value of radiomic features extracted from contrast-enhancing tumor regions on pre-radiotherapy MRI across multiple imaging sequences.

Methods

A total of 74 GBM patients from The Cancer Imaging Archive were analyzed and categorized into short- and long-term survival groups based on their median survival of 18.8 months. Radiomic features were extracted from original, wavelet-filtered images, and Laplacian of Gaussian (LoG)-filtered images both individually and in combination across four MRI sequences: T1-weighted contrast-enhanced (T1WCE), T1W, T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Feature selection was conducted using least absolute shrinkage and selection operator (Lasso) and radiomic scores (Rad-scores) were constructed. The capability of Rad-scores in stratifying OS was evaluated using receiver operating characteristic (ROC) and calibration curve analyses, along with Kaplan–Meier survival analysis. Furthermore, clinical characteristics were integrated to determine whether this combination enhanced the stratification performance.

Results

ROC curve analysis of the Rad-scores demonstrated that wavelet features markedly improved performance compared to the combination of original and LoG features (AUC: 0.90 [0.81–0.96] vs. 0.83 [0.72–0.90]; BH-adjusted p = 0.0183) in differentiating between short- and long-term survivors. Combining wavelet and LoG-filtered features significantly improved survival stratification, outperforming the combination of original and LoG features (AUC: 0.92 [0.84–0.97] vs. 0.83 [0.70–0.90]; BH-adjusted p = 0.0229). Additionally, Kaplan–Meier survival analyses and log-rank tests further confirmed significant correlations between all radiomic feature groups and OS. Integrating clinical characteristics did not notably enhance the discriminatory power (p > 0.05). All the Rad-scores and the combined Rad-sores with clinical data, revealed good agreement between the predicted and actual OS probabilities (p > 0.05), except for the combination of original and LoG features and wavelet features with clinical characteristics (p $<$ $<$ 0.05).

Conclusion

Radiomic analysis of contrast-enhancing tumors on pre-radiotherapy MRI sequences provides strong prognostic insights in GBMs. These findings demonstrate the potential of integrating wavelet-filtered radiomic features for risk stratification; prospective validation is required before clinical implementation.