Diffusion imaging could aid to differentiate between glioma progression and treatment-related abnormalities: a meta-analysis

Although of paramount importance in the follow-up and management of glioma patients, to date it is proven difficult to accurately differentiate between the TP and TRA. Because the outcome between TRA and TP is vitally different, early accurate differentiation of the two could help prevent re-intervention in TRA patients while also providing grounds for treatment in the TP patient group. This meta-analysis shows that, on a group level, the ADC and, to a lesser extent, FA values can be used to distinguish TP from TRA in post-treatment glioma patients (p = 0.005) with pooled sensitivities and specificities of 85% and 81%, and 75% and 78% for ADC and FA values, respectively. The estimated mean ADC and FA values of the patients in the TP group and in the TRA group were consistent with the theory that TP is the result of increased tumor cell proliferation, which causes a reduction in extracellular water diffusion, resulting in a lower ADC value [26, 27]. It has been hypothesized that decreased cellularity due to necrosis as a result of treatment damage (TRA) results in a lower FA value with more extracellular volume, whereas the FA value would increase due to an increased cellularity in TP when growing along existing white matter tracts [28]. The mechanisms driving TRA, however, remain partially elusive. It is believed that due to the (micro)vascular damage after radiation therapy, capillary leakage occurs, resulting in the production of cytotoxic and vasogenic edema [29]. In addition, oligodendroglial injury also plays an important role in the development of TRA [30]. Thereby, it is believed that TRA is reflected by relatively increased ADC value of the tissue [19], with a relative decrease of FA [9]. These hypotheses are corroborated by current findings.

The presented ADC data available for meta-analysis was limited when compared to the abundance of literature dealing with DWI in the follow-up of glioma. This is due to the use of different statistical measures and different outcomes in different papers. Research into different measures of the ADC value, like 5th percentile or relative ADC value could provide new information for the development of ADC maps [10, 27, 31]. However, the use of different measures might slow down further developments and future research due to limited availability of comparable data for meta-analysis. This would be disadvantageous as the ADC maps have also shown promising results with regard to identifying infiltrative patterns of glioma growth [32], predicting O6-methylguanine-DNA methyl-transferase (MGMT) methylation status [33] and predicting treatment outcomes and survival [33,34,35]. In addition, in patients who underwent laser interstitial thermal therapy for glioblastoma, the ADC value of the direct postoperative MRI scan (< 24 h) in the peritumoral region showed to be correlated with regions of later tumor recurrence [36].

The FA value is a scalar value between 0 and 1.0 and thereby is a more consistent metric between participants. Next to differentiating TP from TRA, the FA value has also been reported to be able to detect isocitrate dehydrogenase (IDH) status in oligodendroglial tumors to assess the prognosis and treatment options noninvasively and with an accuracy of about 80% [37]. Another possibility of FA value is to assess infiltration of tumor cells and predict sites of recurrence of glioma by analyzing peritumoral edema or by using distance-informed Track-weighted imaging [38, 39]. This shows promise for detecting invasion and aid in determining the clinical radiation target volume.

Additionally, there have been studies which add other metrics to the imaging diagnostics pipeline (multimodality MRI) in order to make the differentiation between recurrence and treatment-induced change more reliable. Perfusion magnetic resonance imaging (dynamic contrast enhancement, dynamic susceptibility contrast and arterial spin labeling) and magnetic resonance spectroscopy have been reported to be able to accurately distinguish between tumor tissue and radiation induced TRA [2, 20, 40]. PET-MR can be used to monitor treatment response in glioma and to detect recurrence [2]. It is important to note that all diagnostics have their own strengths and boundaries. Knowledge of the properties of these advanced imaging techniques can facilitate the synthesis of more evidence-based assessment of the tissue and help lead to accurate diagnosis of the problem at hand.

Limitations and challenges for implementation of ADC and FA analysis in individual patients

A limitation of this review concerns the fact that the review was not registered in an international database of prospectively registered systematic reviews.

As shown in the present meta-analysis, the ADC and FA values can be used to distinguish TP and TRA in research settings on a group level. A prominent and characteristic limitation of DWI/DTI is the lack of validated diagnostic criteria on an individual level. This partly explains the different sensitivity and specificity values between research populations and further research is warranted in order to be able to specify and validate individual diagnostic criteria and improve the scope of use for DWI/DTI metrics.

Another general limitation for diffusion metrics is that the interpretation of values without context is highly ambiguous and the ADC/FA values are influenced by other factors such as clinical data (e.g., age, atrophy, other white matter defects), measurement purpose (i.e., detect IDH status or discriminate TRA from TP) as well as scanner type and protocol) which all can lead to significant intra-individual differences in ADC/FA values. Significant differences in ADC values can occur on an individual level depending on the scanner and scanning protocol used, thereby not only reflecting a difference between TP and TRA but also a difference on group level inflicted by scanner type. This is inherent to current standards for reporting diffusion weights with only b-values, rather than reporting the duration of, and time between, the pulsed magnetic field gradients encoding motion [41]. In order to tackle this shortcoming, we suggest standardizing acquisition or reporting these additional timings according to the FAIR-principles. Longitudinal single center studies using the same DWI protocol to follow, e.g., treatment response, do not experience this issue as long as they use their institutional reference values. Apart from only reporting b-values rather than DWI gradient timing, the low b-value used in current research (b0) does not take into consideration signal present from blood flow or perfusion which has not yet been attenuated. b-values above approximately 100 s/mm2 have been shown to attenuate these signals, and only signal from diffusing water remains[41]. Future studies might consider calculating DWI metrics using b-values of 100 s/mm2 as the lower value in order to avoid perfusion and blood flow effects to assess possible differences in diagnostic outcome and reliability.

It should also be taken into consideration that, despite the general recommendation that the radiological follow-up of glioma should be performed by experienced radiologists using with a multiparametric MRI protocol containing up-to-date sequences. Using FA parameters in the follow-up of glioma implies that all MRI scanners have access to this modality. This is, however, not always the case, as the value of the DTI sequences is still being investigated. Diffusion-weighted imaging, on the other hand, is available as a standard sequence and therefore widely available for the follow-up of glioma. Therefore, it is recommended to focus future research on harmonization of imaging protocols and diffusion metrics in order to create more interchangeable ADC values. This will allow for more reliable diagnoses based on diffusion metrics.

The different means of tissue mapping (ROI/manual segmentation) are another variable thwarting normalization of the process. Advanced analysis is warranted in order to determine the best course of action for tissue mapping. Furthermore, the co-occurrence of TP and necrotic changes [42, 43] impact the diffusion values which further complicates usage and standardized application in the daily clinical setting. It is also important to note that Xu et al. and Park et al. included WHO 2 glioma patients, which are defined as low-grade gliomas and could respond differently to treatment and possibly influence imaging results, as this paper focuses on high-grade gliomas.

Additionally, the quality of the majority of the papers on this subject was insufficient as most authors did not report standard metrics (e.g., mean, standard deviation, 95% CI) though instead reported values which produced significant results in relatively small datasets. Future research should focus on the publication of study data following the FAIR principles (i.e., findable, accessible, interoperable, re-usable). The FAIR principles underline the importance of the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention as the data which has to be dealt with shows an increase in volume and complexity and is created significantly faster than before [44].

Finally, the clinical implementation of the reported results of this meta-analysis will be hampered by inter-subject variability of the ADC/FA maps. Therefore, an externally validated prediction model with diagnostic criteria on an individual level is necessary and cannot be derived from group analyses. This could be achieved by using a uniform method of normalization on a prospective cohort, for example by obtaining the ADC value from the same region in a standardized atlas, using a normalized scanning protocol with a standardized method of tissue mapping.

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