Exploring the associations between features from multi-parametric MR images in Glioblastoma using radiomics
Abstract Background This study aimed to analyze the associations and substitutability of multi-parametric MRI images in glioblastoma (Gb) using the radiomics method. Methods Utilizing the University of Pennsylvania Health System Glioblastoma dataset from The Cancer Imaging Archive, we extracted quan...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-025-01788-w |
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| Summary: | Abstract Background This study aimed to analyze the associations and substitutability of multi-parametric MRI images in glioblastoma (Gb) using the radiomics method. Methods Utilizing the University of Pennsylvania Health System Glioblastoma dataset from The Cancer Imaging Archive, we extracted quantitative features from T1-weighted, T2-weighted, T2 fluid attenuated inversion recovery (T2-FLAIR), and post-contrast T1-weighted (T1-Gd) images. Feature associations were assessed using Spearman rank correlation with Benjamini-Hochberg correction for multiple comparisons. The substitution analysis was subsequently performed by developing prognostic signatures based on individual MRI sequences and then evaluating the performance of substituted signatures, wherein features from one sequence were replaced by their counterparts from another. Discriminative power was evaluated by the area under the receiver-operating-characteristic curve (AUC). Results Significant feature associations were observed across different MRI sequences. The strongest correlation was identified between T2-weighted and T2-FLAIR images, where 93% of features were significantly and positively correlated (mean absolute correlation coefficient [CC]: 0.57 ± 0.21). A substantial association was also noted between T1-weighted and T1-Gd images, with 86% of features significantly correlated (mean absolute CC: 0.49 ± 0.23). The correlation between T2-weighted and T1-Gd images was less pronounced (75% of features; mean absolute CC: 0.44). In the substitution analysis for prognostication, a signature based on T1-weighted images achieved an AUC of 0.73 (95% CI, 0.60–0.84). Replacing its features with those from T2-weighted images resulted in a signature with a slightly lower AUC of 0.65 (95% CI, 0.51–0.77), a modest difference of 0.08 (95% CI, -0.05–0.21). Conversely, substituting features from a T2-weighted image-based signature with their T1-Gd counterparts resulted in a more substantial decrease in AUC (difference: 0.10, 95% CI, -0.05–0.25). Conclusions Our radiomics analysis indicated potential substantial information redundancy among certain multi-parametric MRI sequences for Gb characterization, particularly between T2-weighted and T2-FLAIR images. Nevertheless, sequences providing unique pathophysiological contrast, such as T1-Gd, appeared to hold distinct prognostic value that was not substituted. While these findings suggested the feasibility of abbreviated multi-parametric MR protocols for specific radiomics applications, they simultaneously underscored that rigorous, task-specific validation was an indispensable prerequisite for any consideration of widespread clinical adoption. |
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| ISSN: | 1471-2342 |