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Published on: December 23, 2025
Diffusion and Perfusion Heterogeneity for Survival Stratification in Post-Treatment Glioblastoma
Purpose:
The prognostic value of diffusion- and perfusion-derived tumor-mask heterogeneity for overall survival in post-treatment glioblastoma was evaluated using a public MRI dataset.
Materials And Methods:
The University of California San Diego Post-Treatment Glioblastoma (UCSD-PTGBM) dataset was used to construct a first-timepoint cohort of 133 subjects. Twenty tumor-mask features were extracted from high b-value apparent diffusion coefficient (ADC) and dynamic susceptibility contrast (DSC) perfusion maps. Prognostic associations were assessed using univariate and adjusted Cox regression. A benchmark compared clinical, diffusion, perfusion, and combined models using cross-validated concordance indices and permutation testing.
Results:
ADC standard deviation ( ADC std ) showed the strongest univariate prognostic association (hazard ratio 1.56, false discovery rate q = 0.0003, concordance index 0.621) and remained independently significant after clinical adjustment (HR 1.48, p < 0.001). Mean transit time standard deviation ( MTT std ) was the strongest perfusion-derived feature (HR 1.38, q = 0.025, concordance index 0.578). ADC std and MTT std showed low correlation (Spearman r = 0.24). In cross-validation, neither imaging feature alone significantly improved discrimination over the clinical baseline (clinical plus ADC, ΔC = +0.058, p = 0.071; clinical plus MTT, ΔC = +0.035, p = 0.194). Only the model combining clinical variables, ADC std and MTT std achieved a significant improvement (concordance index 0.619; ΔC = +0.072, p = 0.029).
Conclusion:
ADC heterogeneity was the numerically strongest imaging signal, while DSC perfusion heterogeneity was weaker and less consistent. Only the combined model significantly outperformed the clinical baseline, but not ADC alone, leaving perfusion's contribution unproven.
Insights
Tumor heterogeneity in apparent diffusion coefficient (ADC) MRI scans shows prognostic value for glioblastoma survival. Combining ADC and perfusion imaging improves survival prediction when added to clinical factors.
Area of Science:
- Radiology and Medical Imaging
- Oncology
- Neuroscience
Background:
- Glioblastoma is an aggressive brain tumor with poor prognosis.
- Accurate prognostic markers are crucial for post-treatment management.
- MRI-derived tumor heterogeneity offers potential as a prognostic indicator.
Purpose of the Study:
- To evaluate the prognostic value of diffusion- and perfusion-derived tumor heterogeneity for overall survival in post-treatment glioblastoma.
- To assess the added predictive value of imaging features to clinical variables.
Main Methods:
- Utilized the UCSD-PTGBM dataset with 133 post-treatment glioblastoma subjects.
- Extracted 20 tumor-mask features from apparent diffusion coefficient (ADC) and dynamic susceptibility contrast (DSC) perfusion maps.
- Employed Cox regression for prognostic association and cross-validation for model comparison.
Main Results:
- ADC standard deviation (ADCstd) demonstrated the strongest univariate prognostic association and remained significant after clinical adjustment.
- Mean transit time standard deviation (MTTstd) was the top perfusion-derived feature, showing low correlation with ADCstd.
- A combined model including clinical variables, ADCstd, and MTTstd significantly improved survival prediction compared to clinical factors alone.
Conclusions:
- ADC heterogeneity is a strong imaging signal for glioblastoma prognosis.
- DSC perfusion heterogeneity is less consistent; its independent contribution is unproven.
- Combined diffusion and perfusion imaging, alongside clinical data, offers the most robust prognostic model.
