Relative cerebral blood volume and fractional anisotropy for differentiating recurrent glioma from radiation injury: a systematic review

Introduction

High grade gliomas, notably Glioblastoma, are the most common primary brain tumors with poor prognosis (1). Clinically, the standard treatment plan for high-grade gliomas is to safely remove the tumor to the maximum extent possible, follow by post-operative radiotherapy and targeted chemotherapy which results in a modest improvement in survival (2). After standard treatment, many glioma patients were found to have new or enlarged lesions in primary tumor sites or radiation therapy areas during their follow-up visits. Such findings may indicate post treatment reactions or tumor recurrence. Pseudo-progression and radiation necrosis (RN) are considered common post treatment reactions. Safe and effective method for differentiating recurrent glioma from radiation injury is still in a working progress. Usually, subsequent to initial treatment, the follow-up decision was guided by longitudinal magnetic resonance imaging (MRI) at regular intervals (3). However, Radiologists like us found the conventional magnetic resonance T1weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR) imaging, and gadolinium-enhanced T1-weighted imaging techniques examination, post-treatment reactions such as pseudo progression and radiation damage may manifest in imaging changes similar to that of tumor recurrence. Such changes make it challenging to distinguish whether a newly enhanced lesion originates from the resected area or the irradiated site is due to recurrent glioma or radiation injury. Advanced MRI methods such as diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI) and perfusion MRI have allowed for more refined and sensitive assessment of tumor characteristics and grade than conventional MRI (4-8). However, their role in differentiating recurrent glioma from radiation Injury is still in its infancy.

Typically, DWI measures random water molecular movement in tissues by calculating the apparent diffusion coefficient (ADC). ADC usually reflects isotropic water diffusion regardless of directional orientation. Previous study found no significant differences in ADC values among different glial tumors from either the peritumoral oedema or the solid tumor region (4). The complexity of myelinated and unmyelinated axons, cellular membrane, and proteins, as well as intracellular compartments in the central nervous system (CNS) could substantially impact the water diffusion pattern (9). Therefore, a new technique of DTI has been proposed as a mechanism underlying water diffusion directionality and anisotropy within tumor tissue (5). The fractional anisotropy (FA) and the magnitude of diffusion (MD) are the two most frequently investigated DTI metrics. FA value holds more sensitive for evaluating microstructure. FA has been linked to various tissue characteristics, including axonal architecture, vascularity, cellular density, fiber tracts, and neuronal structures. Several studies have demonstrated its potential to distinguish between different types of gliomas (6-8). Prior to now, recurrent glioma and radiation injury have been distinguished using FA (10-12). Conversely, studies have reported conflicting results concerning FA’s effectiveness in distinguishing recurrent glioma from radiation injury (8,13).

Perfusion MRI is used to evaluate the physiological and hemodynamic status of blood vessels in the areas of interest (14). The more commonly used perfusion MRI method can be performed using dynamic susceptibility contrast (DSC) enhanced MRI. Relative cerebral blood volume (rCBV) is usually calculated using data from DSC MRI. Some studies also used rCBV to identify Glioma recurrence and radiation damage (15,16).

The current study was conducted to systematically evaluate the efficacy of the rCBV and FA in distinguishing glioma recurrence from radiation-induced brain injury. As radiologists, we aimed to provide reliable imaging parameters to complete standardized structure report (17) with efficient and accurate distinction of glioma recurrence from radiation-induced cerebral injury. Such high-quality reports may allow clinicians to better evaluate individualized treatment decisions and contributing to improved clinical outcomes. We present this article in accordance with the PRISMA reporting checklist (available at https://tro.amegroups.com/article/view/10.21037/tro-25-29/rc).

Methods

Literature search

To achieve a comprehensive review, we looked for all studies published in English on the topic of differentiation of recurrent glioma and radiation-related brain injury using MRI examination of rCBV combined with FA that were published before 1 September 2025. Relevant studies were retrieved from PubMed, Cochrane library, and Web of Science with the keywords of “rCBV”, “FA”, “glioma”, and “radiation injury”. The search formula(((glioma) AND (radiation injury)) AND ((relative cerebral blood volume) OR (rCBV) OR (cerebral blood volume) OR (fractional anisotropy) OR (mean diffusivity))) was adopted in our searches. The search strategy can be seen in Table S1.

Inclusion and exclusion criteria

All published studies were independently assessed by two researchers. A full-text analysis was employed to assess whether the studies met our inclusion- and/or exclusion criteria. All of our included studies where (I) the participants enrolled in the studies had histopathological confirmation of glioma; (II) the subjects received total or subtotal tumor resection followed by radiation therapy and were suspected of recurrent glioma on follow-up MRI; (III) the diagnostic method in the literature was DTI or DSC perfusion MRI; (IV) the outcome measures of the study applied to (I) rCBV, (II) FA, (III) glioma, (IV) radiation injury; (V) the data in the literature were complete. We excluded studies (I) without comprehensive statistical analysis or relevant supporting data; (II) of duplicate publications; (II) of a partially overlapping patient population or nonhuman studies; (IV) of conference proceedings, systematic reviews, and meta-analytical studies.

Study selection and data extraction

Two researchers independently screened the retrieved studies according to the predefined inclusion and exclusion criteria. A cross-check was subsequently conducted to ensure consistency. In cases of disagreement, a third researcher was consulted to review the article, and all three investigators engaged in discussion to reach a consensus. Relevant data from the included studies were then extracted by two researchers. Data included first author(s), publication year, and country, sample population size, patient demographics, study design, MRI acquisition details and parameters, software used to process data.

Evaluation of literature quality

Newcastle-Ottawa scale (NOS) was used to assess the quality of included studies in Review Manager 5.4 software. The NOS is a tool designed to assess the quality (risk of bias) of cohort and case-control studies included in a systematic review and meta-analysis. These study designs may not be randomized; they may be inherently more susceptible to bias. The NOS helps reviewers judge how well a study was conducted and how trustworthy its findings are.

Statistical methods

Review Manager 5.4 was used in this study to quantitatively synthesize data from studies related to the conditions of interest. Random-effects meta-analyses were conducted, and the standardized mean difference (SMD) along with the corresponding 95% confidence intervals (CIs) was used to estimate effect sizes for the analyzed parameters. A random-effects model was used in this meta-analysis to account for potential heterogeneity among the included studies, as they varied in study design, population characteristics, and intervention protocols. This approach assumes that the true treatment effect may differ across studies, and therefore provides a more conservative and generalizable summary estimate. Interstudy heterogeneity was assessed using the I² statistic, which quantifies the percentage of total variation across studies due to heterogeneity rather than chance. I² values range from 0% to 100%, with 0% indicating no observed heterogeneity. Heterogeneity was categorized as follows: none (I²<25%), low (25%≤I²<50%), moderate (50%≤I²<75%), and high (I²≥75%) (18).

Results

Literature search and selection flowchart

A total of 160 eligible primary studies published prior to 1 September 2025, were identified in this meta-analysis, 66 of the 160 were acquired from PubMed, 89 from Web of Science, 5 from Cochrane library. After the screening, 30 duplicates were excluded. Seventeen reviews including meta-analyses or case reports, 3 nonhuman studies and 77 unrelated studies, as well as 15 articles without adequate parameter value to synthesis were also excluded. Finally, 18 studies were included for qualitative and quantitative data synthesis (10-12,19-33). Of the 18, 13 studies used rCBV (19,21,22,24-31,33) and 2 studied used FA (10,12) to differentiate between glioma recurrence and radiation Injury. 3 studies used rCBV combined with FA (11,20,32). The process of identifying and selecting studies is depicted in Figure 1. The 18 articles were included in this study. The basic characteristics of the included studies are given in (Tables 1,2).

Figure 1 Flowchart depicting study selection. FA, fractional anisotropy; rCBV, relative cerebral blood volume.

Bias risk across studies by NOS

The quality assessment results were used descriptively to evaluate the overall quality of the included studies and explore potential sources of heterogeneity. These results are presented in Table 3.

Meta-analysis of rCBV

Pooling data from 16 studies showed that rCBV in the recurrent glioma group was significantly higher than one in the radiation injury group (SMD =1.48, 95% CI: 1.13, 1.84, P<0.00001), with a substantial degree of heterogeneity (I²=74%) (Figure 2). Given the high heterogeneity and substantial interstudy variability, a funnel plot was used to visually assess publication bias. A random-effects model was applied for analysis. The asymmetry observed in the funnel plot suggests there is publication bias (Figure 3).

Figure 2 Forest plot of rCBV in recurrent glioma versus radiation injury groups (11,19-33). CI, confidence interval; IV, inverse-variance; rCBV, relative cerebral blood volume; RI, radiation injury; SD, standard deviation; Std., standard; TR, tumor recurrence.

Figure 3 Funnel plot showing standardized mean difference in rCBV for recurrent glioma vs. radiation injury groups. rCBV, relative cerebral blood volume; SE, standard error; SMD, standardized mean difference.

Meta-analysis of FA

Data from 5 studies showed that FA in recurrent glioma group was significantly higher than one in radiation injury group (SMD =1.13, 95% CI: 0.52, 1.73, P=0.0003). A moderate level of heterogeneity (I²=72%) is observed (Figure 4).

Figure 4 Forest plot of FA values in the recurrent glioma and radiation injury groups (10-12,20,32). CI, confidence interval; FA, fractional anisotropy; IV, inverse-variance; SD, standard deviation; Std., standard.

Discussion

Pseudo progression is defined as significant enhancement and increased degree of edema on magnetic resonance imaging, which is different from true tumor progression (34). Many studies grouped these two post treatment reactions of pseudo-progression and RN into one lesion type of radiation injury (35). Our study also categorized the two post treatment reactions as radiation injury. On imaging, previous research found there is a noticeable reduction in tumor enhancement and a decrease in the surrounding edema on FLAIR sequences. This appearance may mimic a favorable treatment response; however, it is referred to as a “pseudoresponse” because it results from changes in vascular permeability rather than an actual reduction in tumor burden (36). At this moment, our study also categorized pseudoresponse as radiation injury, which warrants further investigation going forward.

The lesions of tumor recurrence and radiation injury share same clinical symptoms and similar features in conventional imaging, but their prognosis is completely different (37). Present day method to accurately distinguish the two types of lesions is through pathology changes of surgically extracted tissues from lesions. However, this method increases mortality risks, causes unnecessary pain, and adds the economic burden of secondary surgery to patients suffering from radiation damage. Hence, it is essential to find accurate and non-invasive methods to clinically distinguishing these two types of lesions.

Our data showed that lesions of recurrent glioma have significantly higher rCBV and FA compared to the radiation injury groups. Nevertheless, only three articles in this search used both rCBV and FA to distinguish tumor recurrence from RN (11,20,32) . Compared with rCBV, FA is rarely used to distinguish recurrent glioma and radiation injury. One likely reason is the lack of standardized methods for selecting, capturing, and post-processing regions of interest (ROIs) in DTI (13). Although previous study reported that quantitative positron emission tomography (PET)/MRI parameters in combination with DSC perfusion MRI (pMRI) provide the best diagnostic utility in distinguishing RN from tumor recurrence (TR) in patients with treated glioblastoma multiforme (GBM) compared to PET/CT (38), in our study the rCBV and FA measurements can increase the accuracy of MRI diagnosis to quantitatively differentiate recurrent glioma from radiation injury patients. Some people believe that the reason FA is higher in recurrent glioma areas than one in radiation injuries areas is due to the increase in the number of cells in high-grade gliomas (10-12).

Recently, treatment response assessment maps (TRAMs) derived from MRI have been developed to differentiate between recurrent malignant glioma and therapy-related changes. TRAMs are generated using two contrast-enhanced T1-weighted sequences and visualize the dynamics of gadolinium uptake and clearance over time. Areas with viable tumor tissue typically show rapid contrast wash-out, reflecting high vascularity and metabolic activity. In contrast, therapy-induced scar tissue tends to exhibit gradual contrast accumulation due to slower perfusion and lower cellular turnover (39). Future study should be conducted to compare PET/MRI parameters in combination with DSC pMRI and FA measurements, as well as TRAMs.

In our study, 5 articles showed that FA value in the glioma recurrence group was higher than one in the radiation injury group. Once possible explanation could be the significant infiltration of glioma tumor cells that disrupted the fibrous structure of normal brain tissue and led to a decrease in water dispersion directionality.

Second, we may not have included all the relevant studies in our research due to the limit of our literature search. The FA analysis is limited to only 5 studies. The results with nonsignificant pooled results (P=0.0003) and wide 95% CI (0.52, 1.73) in contrasts with the stronger rCBV findings suggested insufficient FA evidence to support its diagnostic utility. Additionally, the funnel plots indicate publication bias, likely due to the exclusion of non-English studies or small negative studies, which may inflate effect sizes.

The high heterogeneity observed (I²=74% for rCBV; I²=72% for FA) underscores the need to address variability across studies. Clinical heterogeneity, such as differences in treatment regimens (e.g., radiation doses, chemotherapy types), differences in tumor biological and clinical characteristics (e.g., tumor infiltration patterns, glioma grades, range of tumor resection, and the time of discovering suspicious lesions), follow-up intervals, etc., may confound the results and affect the accuracy of quantitative MRI. Variations in subject number, demographics of study population, study design, diagnostic standards, analysis software, as well as the methods for evaluating lesions contributes to the heterogeneity of studies. MRI technical heterogeneity may include MRI scanners’ differences, variability in MRI protocols (e.g., field strength, acquisition parameters), ROI selection methods, DTI post-processing discrepancies, timing of post-treatment imaging, and post-processing software across studies, which we have not systematically addressed in this study. A sensitivity analysis stratifying by these variables could strengthen the findings.

In our study, the average threshold of rCBV in the recurrent glioma group and radiation injury group was respectively 3.896±1.704 and 1.416±0.648 based on the results of 16 studies for rCBV synthesis. The averaged lower bound of reported mean rCBV values in the recurrent glioma group was 2.192 (3.896–1.704=2.192); however, this value was not derived from formal threshold analysis and should be interpreted only as a descriptive reference rather than a validated diagnostic cutoff. In clinical implementation, a radiologist can integrate these findings into their structured reporting. When rCBV is larger than 2.192, the radiologist should think of recurrent glioma. When rCBV is smaller than 2.192, recurrent glioma cannot be ruled out. Most included studies did not provide sufficient data to reconstruct diagnostic 2×2 tables; therefore, pooled estimates of sensitivity and specificity could not be calculated. This limitation also restricted further exploration of the sources of heterogeneity. The proposed clinical workflow should add FA measurement, or add PET/MRI pin combination with DSC pMRI and FA measurements, as well as TRAMs towards differentiating recurrent glioma from radiation injury.

Conclusions

Our findings indicate that rCBV and FA may be useful imaging biomarkers for distinguishing recurrent glioma from radiation injury. Nevertheless, the available evidence is limited by study heterogeneity and incomplete reporting of diagnostic accuracy data. Future large-scale prospective studies are warranted to further validate their clinical utility. To mitigate heterogeneity, we recommend MRI standardization strategies and subgroup analyses by glioma grade, treatment type in future studies. In the future research, combining different imaging modalities is also needed to distinguish recurrent glioma from radiation injury and evaluate overall diagnostic performance toward improving diagnostic accuracy. Synthesizing multiparametric data into decision-making algorithms and proposing cutoff values for rCBV/FA could be a logical extension of the current work.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tro.amegroups.com/article/view/10.21037/tro-25-29/rc

Peer Review File: Available at https://tro.amegroups.com/article/view/10.21037/tro-25-29/prf

Funding: This study was supported by Shenzhen Municipal Science and Technology Innovation Commission Science and Technology Research and Development Grant (No. JCYJ20220531094801003). The funder of the study had no role in the study design or the collection, and analysis, as well as the interpretation of data.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tro.amegroups.com/article/view/10.21037/tro-25-29/coif). The authors have no 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.

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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