Prospective Longitudinal Analysis of Physiologic MRI-Based Tumor Habitat Predicts Short-Term Patient Outcomes in IDH-Wildtype Glioblastoma

Academic Background

Glioblastoma (GBM) is a highly malignant brain tumor characterized by significant intratumoral heterogeneity, which is evident not only in gene expression and histopathology but also in macroscopic structure. This heterogeneity leads to diverse treatment responses and the development of tumor resistance, resulting in a poor prognosis for GBM patients. Early and accurate prediction of tumor progression is crucial for timely adjustment of treatment strategies, such as reoperation or the use of bevacizumab. However, the coexistence of tumor recurrence and radiation injury in post-treatment glioblastoma complicates the prediction of progression.

In recent years, tumor habitat analysis based on multiparametric physiologic MRI (e.g., Cerebral Blood Volume, CBV, and Apparent Diffusion Coefficient, ADC) has become a research hotspot. This method groups similar voxels within the tumor to identify distinct tumor subregions, providing valuable clinical insights into tumor progression and treatment resistance. However, although previous studies have proposed tumor habitat analysis based on CBV and ADC, its clinical efficacy in predicting early tumor progression and patient outcomes has not been validated in prospective studies.

Source of the Paper

This paper was co-authored by Hye Hyeon Moon, Ji Eun Park, Nakyoung Kim, and others from the Department of Radiology and Neurosurgery at Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. The study was published in 2025 in the journal Neuro-Oncology under the title “Prospective longitudinal analysis of physiologic MRI-based tumor habitat predicts short-term patient outcomes in IDH-wildtype glioblastoma.”

Research Process

Study Subjects and Inclusion Criteria

The study enrolled 79 patients with IDH-wildtype glioblastoma treated at Asan Medical Center between January 2020 and June 2022. Inclusion criteria included: age ≥18 years, histopathological diagnosis of IDH-wildtype glioblastoma, receipt of standard treatment (maximal safe resection followed by concurrent chemoradiotherapy and adjuvant temozolomide), and MRI examinations including diffusion-weighted imaging (DWI) and dynamic susceptibility contrast imaging (DSC) postoperatively and at three consecutive time points.

Imaging Data Acquisition and Processing

The study utilized multiparametric MRI techniques, including CBV and ADC mapping, to identify different regions within the tumor. Imaging data were co-registered and resampled, and voxels were classified into three habitats using the k-means clustering algorithm: hypervascular cellular habitat (high CBV, low ADC), hypovascular cellular habitat (low CBV, low ADC), and nonviable tissue (low CBV, high ADC). The habitat risk score was calculated based on increases in hypervascular and hypovascular cellular habitats.

Data Analysis and Statistical Methods

The study employed Cox proportional hazards models to analyze the associations between spatiotemporal habitats, habitat risk scores, and time-to-progression (TTP) and overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve analysis was used to compare the performance of habitat risk scores and tumor volume in predicting TTP and OS.

Main Findings

Associations Between Habitats and Prognosis

The study found that increases in both hypervascular and hypovascular cellular habitats were significantly associated with shorter TTP and OS. In multivariate analysis, the hypovascular cellular habitat was an independent predictor of both TTP and OS. The habitat risk score successfully stratified patients into low-, intermediate-, and high-risk groups and demonstrated a significantly higher AUC for predicting 12-month TTP compared to changes in tumor volume.

Predictive Performance of Habitat Risk Score

The habitat risk score performed exceptionally well in predicting early tumor progression, particularly at the 12-month follow-up, with an AUC of 0.762, significantly higher than the AUC for tumor volume changes (0.646). Additionally, the habitat risk score showed a higher AUC for predicting OS, although it did not reach statistical significance.

Prediction of Tumor Progression Sites

Through quantitative analysis of the Dice index, the study found a significant correlation between tumor progression sites and regions with short-term increases in the hypovascular cellular habitat. This suggests that the hypovascular cellular habitat can noninvasively identify tumor progression sites, providing potential imaging guidance for future tissue sampling.

Conclusions and Significance

This study prospectively validated the efficacy of multiparametric physiologic MRI-based tumor habitat analysis in predicting early tumor progression and clinical outcomes in patients with IDH-wildtype glioblastoma. The increase in hypovascular cellular habitat was proven to be a robust imaging biomarker for identifying tumor progression sites. The habitat risk score outperformed traditional tumor volume assessments in predicting early tumor progression, offering a valuable tool for patient risk stratification.

Research Highlights

1. Predictive Value of Hypovascular Cellular Habitat: The study found that increases in the hypovascular cellular habitat were significantly associated with shorter TTP and OS, indicating its critical role in tumor progression and treatment resistance.
2. Superiority of Habitat Risk Score: The habitat risk score outperformed changes in tumor volume in predicting early tumor progression, particularly at the 12-month follow-up, demonstrating significant predictive performance.
3. Identification of Tumor Progression Sites: Through quantitative analysis of the Dice index, the study successfully linked tumor progression sites to regions with increases in the hypovascular cellular habitat, providing potential imaging guidance for future tissue sampling.

Additional Valuable Information

The study also explored the biological hypothesis of the hypovascular cellular habitat, suggesting that it may reflect hypoxic regions within the tumor microenvironment, closely associated with the maintenance of glioma stem cells (GSCs) and treatment resistance. Furthermore, the study emphasized the need for further validation of precise correlations between imaging and pathology to enhance the clinical application value of tumor habitat analysis.

This study provides new perspectives and tools for the early prediction of progression and management of patients with IDH-wildtype glioblastoma, holding significant scientific value and clinical potential.