Preoperative assessment of recurrence-prone subregions within the peritumoral region of glioblastoma: a comparison between diffusion kurtosis and diffusion tensor imaging

Introduction

Glioblastoma (Gb) remains the most common and aggressive primary intracranial malignancy in adults. The current standard of care involves maximal safe surgical resection followed by adjuvant radiotherapy and temozolomide chemotherapy (1,2). Despite this multimodal approach, prognosis remains poor, with a median overall survival of approximately 15 months (3). A major contributing factor to this poor outcome is the high rate of tumor recurrence. Current surgical strategies focus on resecting the gadolinium-enhancing portion of the tumor visible on magnetic resonance imaging (MRI); however, Gb is characterized by extensive infiltration beyond the enhancing margin, indicating that the radiographic abnormality does not reflect the full tumor extent (4). Consequently, most recurrences occur within the peritumoral fluid-attenuated inversion recovery (FLAIR)-hyperintense region, with only about 10% exhibiting non-contiguous distant recurrence at first follow-up (5). Preoperative identification of high-risk subregions within this peritumoral zone is therefore clinically imperative for optimizing local therapy and improving outcomes.

Diffusion tensor imaging (DTI) has been employed to probe the peritumoral microenvironment in Gb. In a retrospective study, Bette et al. (6) reported significantly lower fractional anisotropy (FA) values in areas of peritumoral edema that later developed recurrence compared to non-recurrent regions. More recently, Metz et al. (7) applied a free water correction algorithm to DTI data and found significant differences in corrected FA maps between edematous tissue that subsequently recurred and tissue that did not. These studies suggest that DTI-derived metrics may capture early microstructural changes predictive of recurrence.

Diffusion kurtosis imaging (DKI), an extension of DTI, quantifies non-Gaussian water diffusion and may more accurately reflect tissue complexity (8). DKI has shown promise in glioma grading, predicting isocitrate dehydrogenase (IDH) mutation status (9-11), and outperforming DTI in detecting microstructural alterations in various neurological conditions (12-14). However, the potential of DKI histogram analysis for stratifying recurrence risk within peritumoral non-enhancing regions of Gb remains underexplored. Therefore, this study aims to evaluate and compare the value of preoperative DKI and DTI histogram parameters in stratifying recurrence risk within the non-enhancing peritumoral edema region in patients with Gb. We present this article in accordance with the STARD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0070/rc).

Methods

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective study was approved by the Research and Clinical Trial Ethics Committee of The First Affiliated Hospital of Zhengzhou University (No. 2019-KY-231), and individual consent for this analysis was waived due to the retrospective nature. The present study was designed to compare recurrence-prone and non-recurrent subregions within the preoperative peritumoral region in a spatially matched, within-patient manner, rather than to construct a patient-level model for predicting recurrence occurrence.

Patients

Between November 2018 and October 2023, a total of 95 patients with newly diagnosed, pathologically confirmed glioblastoma (Gb) were retrospectively identified for this study. The initial inclusion criteria required provision of a complete preoperative MRI protocol, undergoing gross total surgical resection and follow-up scans for recurrence assessment. Recurrence is determined based on pathological findings after secondary surgery or the latest neuro-oncological response assessment criteria (15). Patients were excluded based on the following criteria: (I) any antitumor therapy (e.g., surgery, radiotherapy, or chemotherapy) prior to the preoperative MRI scan; (II) poor image quality with severe motion or susceptibility artifacts; (III) incomplete imaging data, such as missing essential preoperative sequences or inadequate follow-up scans; (IV) absence of tumor recurrence during the follow-up period, or recurrence located exclusively outside the preoperative non-enhancing peritumoral region. Following this selection process, a final cohort of 55 patients was included for analysis.

MRI data acquisition

MRI examinations were performed for all patients using a 3 T MAGNETOM Prisma system (Siemens Healthineers, Erlangen, Germany) equipped with a 64-channel head-neck coil. The imaging protocol and corresponding parameters are detailed below: (I) T2-weighted imaging (T2WI): repetition time (TR) =4,090.0 ms; echo time (TE) =99.0 ms; field of view (FOV) =220×220 mm²; acquisition matrix =733×733; 20 slices; slice thickness =5.0 mm, the acquisition time was 34 seconds. (II) T2 dark-fluid: TR =8,000.0 ms; TE =81.0 ms; FOV =220×220 mm²; acquisition matrix =314×314; 20 slices; slice thickness =5.0 mm, the acquisition time was 1 minute and 38 seconds. (III) T1-weighted imaging (T1WI): TR =250.0 ms; TE =2.46 ms; FOV =220×220 mm²; acquisition matrix =314×314; 20 slices; slice thickness =5.0 mm, the acquisition time was 37 seconds. (IV) Diffusion-weighted imaging (DWI): acquired using a spin-echo echo-planar imaging sequence. Parameters: TR =2,500 ms; TE =71 ms; FOV =220×220 mm²; 60 slices; slice thickness =2.2 mm, the acquisition time was 54 seconds. Diffusion encoding included five non-zero b-values (500, 1,000, 1,500, 2,000, and 2,500 s/mm²), each applied along 30 directions, alongside one acquisition at b=0 s/mm².

Image processing and analysis

DKI data were processed using NeuDiLab software (Diffusion Imaging in Python, https://dipy.org) to generate DKI- and DTI-derived parametric maps. All preoperative structural images and post-recurrence contrast-enhanced images were automatically coregistered to the preoperative diffusion parametric map space using ITK-SNAP (version 3.8.0, https://www.itksnap.org). Considering that postoperative anatomical changes, particularly brain shift, may compromise registration accuracy, the fusion results for each case were visually reviewed by two radiologists (J.B. and X.M., with 10 and 5 years of experience, respectively), and no manual adjustment was performed. The edema region on preoperative FLAIR images and the contrast-enhancing lesion at first recurrence were initially segmented using an nnU-Net model trained on the BraTS 2020 Challenge dataset (16). The segmentation results were then jointly reviewed by the two radiologists, and the final regions of interest (ROIs) were determined by consensus. Cases with poor segmentation or obviously unreliable image fusion were excluded from further analysis. The area of overlap between the preoperative FLAIR-hyperintense edema and the contrast-enhancing lesion at first recurrence was defined as the recurrence-prone region, whereas the remaining edema was defined as the non-recurrent region, as illustrated in Figure 1. This ROI definition enabled a spatially matched within-patient comparison and minimized the effect of interindividual heterogeneity. The parametric maps and corresponding ROIs were subsequently imported into MATLAB (version R2017b; MathWorks, Natick, MA, USA) for histogram analysis, including the 10th, 25th, 50th, 75th, and 90th percentiles, mean, maximum, minimum, variance, skewness, and kurtosis.

Figure 1 A 68-year-old male patient with glioblastoma. (A) Preoperative FLAIR image showing the peritumoral hyperintense region. (B) Contrast-enhanced T1-weighted image at first recurrence showing the recurrent lesion, as assessed according to the RANO criteria. (C) Final ROI demonstrating the overlap between the preoperative FLAIR-hyperintense region and the contrast-enhancing lesion at first recurrence; this overlapping area was defined as the recurrence-prone region, whereas the remaining FLAIR-hyperintense area was defined as the non-recurrent region. Representative parametric maps used for histogram analysis are shown for FA (D), RK (E), and MK (F). FA, fractional anisotropy; FLAIR, fluid-attenuated inversion recovery; MK, mean kurtosis; RANO, Response Assessment in Neuro-Oncology; RK, radial kurtosis; ROI, region of interest.

Statistical analysis

All statistical analyses were performed using SPSS software (version 26.0, IBM Corp., Armonk, NY, USA). Continuous variables are presented as mean ± standard deviation. Normality of data distribution was assessed using the Shapiro-Wilk test. Subsequently, the differences in histogram parameters between recurrent and non-recurrent regions were compared using a paired samples t-test. We performed Benjamini-Hochberg multiple correction on the P values for parameters with significant differences. Receiver operating characteristic (ROC) analysis was performed to evaluate the predictive performance of parameters that showed significant differences. The area under the curve (AUC) was calculated to quantify their ability to differentiate between recurrent and non-recurrent regions. A two-tailed P value of less than 0.05 was considered statistically significant.

Results

A total of 55 patients were included in the final analysis. The mean age was 51.8 years, including 33 males and 22 females. All patients were IDH-wildtype. All patients developed recurrence within 1 year. Among the included patients, 8 underwent repeat surgery, whereas recurrence in the remaining 47 patients was determined based on imaging follow-up alone. All patients received standard postoperative treatment. Furthermore, all included patients were adults and had a measurable preoperative peritumoral FLAIR-hyperintense region, which was required for ROI definition in this study.

Comparative analysis of DTI histogram parameters in recurrent versus non-recurrent regions

A comparative analysis revealed statistically significant disparities in multiple DTI-derived histogram parameters between histologically confirmed recurrent and non-recurrent regions (Table 1). Specifically, maximum axial diffusivity (AD_max), AD_variance, FA₉₀ₜₕ, FA_max, FA_variance, FA_skewness, FA_kurtosis, maximum mean diffusivity (MD_max), MD_variance, MD_skewness, maximum radial diffusivity (RD_max), RD_variance, and RD_skewness exhibited statistically significant reductions within recurrent foci. Conversely, AD_10th, AD_min, FA_min, MD_10th, MD_25th, MD_min, RD_10th, RD_25th and RD_min were significantly elevated in territories of recurrence. Among the evaluated metrics, FA_max demonstrated the highest discriminatory power within the DTI framework, yielding an AUC of 0.834. Detailed results are shown in Table 2.

Comparative analysis of DKI histogram parameters between recurrent and non-recurrent regions

Significant disparities were observed in multiple DKI-derived histogram parameters when comparing recurrent and non-recurrent regions (Table 3). Specifically, maximum axial kurtosis (AK_max), AK_kurtosis, mean kurtosis (MK)_90th, MK_max, MK_variance, radial kurtosis (RK)_75th, RK_90th, RK_mean, RK_max, RK_variance, RK_kurtosis and RK_kurtosis were significantly reduced within recurrent foci compared to non-recurrent areas. Conversely, AK_min, MK_min, and RK_min demonstrated significant elevation within recurrent regions. Among these parameters, RK_max yielded the highest AUC value of 0.868, indicating superior discriminatory performance. Detailed results are shown in Table 4.

ROC analysis was employed to evaluate the diagnostic efficacy of individual histogram parameters derived from both diffusion models. The parameter RK_max from the DKI model achieved the highest AUC value of 0.868, with comprehensive results visually presented in Figures 2,3 and supplementary materials (Figures S1-S3 and Tables S1-S4).

Figure 2 ROC analysis of the best-performing histogram parameters derived from diffusion kurtosis imaging and diffusion tensor imaging, including RK_max and FA_max. DKI, diffusion kurtosis imaging; DTI, diffusion tensor imaging; FA, fractional anisotropy; max, maximum; RK, radial kurtosis; ROC, receiver operating characteristic.

Figure 3 Comparison of the best-performing histogram parameters between recurrence-prone and non-recurrent regions. (A) FA_max derived from diffusion tensor imaging. (B) RK_max derived from diffusion kurtosis imaging. DKI, diffusion kurtosis imaging; DTI, diffusion tensor imaging; FA, fractional anisotropy; max, maximum; RK, radial kurtosis.

Discussion

This study used preoperative multimodal diffusion MRI histogram analysis, combined with retrospective registration of recurrent lesions, to investigate microstructural differences between recurrence-prone and non-recurrent subregions within the peritumoral edema of Gb. Our results showed that histogram parameters derived from preoperative diffusion models, particularly those from DKI, could effectively distinguish regions within the peritumoral edema that subsequently developed recurrence from those that did not. Multiple parameters from both DTI and DKI demonstrated significant differences between these two types of regions, with the DKI-derived parameter RK_max showing the highest predictive performance (AUC =0.868). In addition, only patients with documented recurrence were included because the aim of this study was to identify imaging differences between edema subregions that later became recurrent and those that remained non-recurrent within the same patient. This analysis required the contrast-enhancing lesion at first recurrence after surgery as a spatial reference standard. Patients without recurrence during follow-up were therefore not included, because such a recurrence-defined reference region was unavailable.

The predictive value of RK_max may be attributed to its sensitivity to microstructural integrity (17). Due to the highly infiltrative nature of Gb, the contrast-enhanced portions of the tumor visible on MRI do not encompass the entire tumor parenchyma. Current surgical management primarily targets the resection of these contrast-enhanced regions, yet histopathological evidence confirms that infiltrating neoplastic cells persist within peritumoral edematous regions and serve as the origin for most recurrences (18,19). Tumor infiltration reduces the integrity of normal brain structures (20), and k-values generally exhibit a positive correlation with brain structural integrity (17). Consequently, higher RK_75th, RK_90th and RK_max values in non-recurrent areas may represent zones where architectural integrity remains relatively preserved. Similarly, FA values reflect the integrity and directionality of white matter fiber tracts (21). Tumor infiltration disrupts these tracts, leading to reduced FA values. The higher FA_90th and FA_max values observed in non-recurrent regions correspond to areas with comparatively maintained white matter architecture.

Lower AK_min values in non-recurrent regions may correspond to pure vasogenic edema without tumor cell infiltration. Histologically, peritumoral edema comprises two distinct components: regions infiltrated by neoplastic cells and pure vasogenic edema (22,23). The less complex microenvironment in pure vasogenic edema may result in lower k_min values. Furthermore, tumor cell infiltration increases voxel-level cell density, supporting the observation of smaller d_max in infiltrated regions. By comparison, peritumoral abnormalities in brain metastases and central nervous system (CNS) lymphomas tend to be more homogeneous on MRI, whereas Gb more often demonstrates heterogeneous peritumoral changes, likely reflecting the coexistence of vasogenic edema and tumor infiltration (24,25).

These findings underscore the potential clinical utility of these parameters for recurrence risk stratification. Regions where parameter values exceed the RK_max threshold or remain below the AK_min threshold observed in recurrent areas might be designated as lower risk. Preserving such regions during surgery could improve functional outcomes (26). In radiotherapy planning, contemporary protocols typically target the residual enhancing region plus a 1–1.5 cm margin (27), yet most patients develop recurrence within six months after treatment completion (28). Preoperative risk stratification is therefore critical. Areas with parameter values within the recurrence-associated range could be targeted for dose escalation, while minimizing damage to healthy tissues. As reviewed by Castellano et al., MRI holds broad prospects for guiding radiotherapy planning in glioma patients (26).

Comparative analysis showed that RK_max from DKI had the largest effect size and the highest AUC among the evaluated parameters in this cohort. The principal limitation of DTI lies in its assumption of Gaussian water diffusion (20). In biological tissue, membranes, proteins, and water exchange mechanisms restrict water movement, resulting in non-Gaussian diffusion. DKI extends DTI by modeling non-Gaussian diffusion, enabling more precise characterization of tissue microstructure. AK and RK quantify diffusion restriction along axial and radial directions, respectively. Because diffusion is predominantly restricted radially, RK shows increased sensitivity to microstructural changes, particularly in white matter, which may account for its larger effect size.

Conventional MRI lacks specificity to differentiate tumor-infiltrated edema from pure vasogenic edema (6). Advanced diffusion sequences quantify microstructural alterations by measuring water molecule diffusion and providing architectural information on white matter integrity, yielding information undetectable with conventional MRI. These techniques have been validated and are increasingly used for treatment response assessment in glioma patients (7,29-31).

This study has several limitations. First, it was a retrospective single-center study with a relatively limited sample size, and further validation in larger multicenter cohorts is needed. Second, although recurrence was histopathologically confirmed in patients who underwent repeat surgery, most recurrence determinations in this cohort were based on imaging follow-up rather than tissue confirmation. Histopathological confirmation would have been preferable, as imaging-based response assessment after Gb treatment may be influenced by treatment-related changes and is inherently less definitive than pathology (32,33). In the present study, the advanced imaging techniques under investigation were the preoperative diffusion-based sequences DTI and DKI. However, additional advanced post-treatment imaging techniques, such as perfusion MRI, metabolic and physiologic MRI, or delayed contrast-based approaches, were not systematically incorporated into recurrence adjudication (32-34). Third, postoperative anatomical changes may also have affected image registration and ROI delineation. In addition, because the final ROIs were determined by consensus between two readers rather than by independent assessments, formal interobserver agreement metrics were not available. Finally, because a relatively large number of histogram parameters were evaluated, whereas no multivariable modeling or validation was performed in order to avoid overfitting and unstable parameter estimates, the present findings should be considered exploratory. Although DKI-derived parameters showed higher discriminatory performance than the best-performing DTI parameter in this cohort, further validation is still required.

Conclusions

Preoperative DKI histogram analysis may help identify recurrence-prone subregions within the peritumoral region of Gb. In this cohort, certain DKI-derived parameters showed higher discriminatory performance than the best-performing DTI-derived parameter. These findings are exploratory and require confirmation in larger studies with multivariable modeling and validation.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0070/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0070/dss

Funding: This study was supported by the National Natural Science Foundation of China (Nos. 62106229, 62476252, 82502477, and 82402395).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0070/coif). All authors report this study was supported by the National Natural Science Foundation of China (Nos. 62106229, 62476252, 82502477, and 82402395). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This retrospective study was approved by the Research and Clinical Trial Ethics Committee of The First Affiliated Hospital of Zhengzhou University (No. 2019-KY-231), and individual consent for this analysis was waived due to the retrospective nature.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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