Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiating Recurrence From Radiation Injury in Postoperative Glioma: A Meta-analysis : Journal of Computer Assisted Tomography

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Neuroimaging: Brain

Diagnostic Performance of Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted Imaging in Differentiating Recurrence From Radiation Injury in Postoperative Glioma: A Meta-analysis

Zhang, Hui-Mei MD; Huo, Xiao-Bing MD; Wang, Hua-Long MD; Wang, Chen MD

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Journal of Computer Assisted Tomography 46(6):p 938-944, 11/12 2022. | DOI: 10.1097/RCT.0000000000001356
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Abstract

Gliomas account for approximately a third of nervous system tumors and the majority of malignancies. The incidence is between 5 and 6 per 100,000.1–3 The tumor is usually treated by resection and radiotherapy together with temozolomide-based chemotherapy.4 Magnetic resonance imaging (MRI) is the most commonly used follow-up method for monitoring postoperative recurrence.5–7

However, an abnormal MRI signal may represent either tumor recurrence (TR) or radiation injury (RI), and it is important to distinguish between them.8–10 Tumor recurrence is usually seen 32 to 36 weeks after radiation therapy,9 in contrast to RI which commonly appears within 3 to 12 months. The incidence of RI is between 3% and 24% and is dependent on the radiation dose.10 Magnetic resonance imaging techniques, such as T2-weighted and gadolinium-enhanced T1-weighted imaging, frequently, have difficulty in distinguishing between TR and RI.9

Other MRI techniques, including diffuse-weighted imaging (DWI), perfusion-weighted imaging (PWI), and MRI spectroscopy (MRS), are often used for analyzing cancer and necrotic tissue, and these may be better able to differentiate TR and RI lesions.11–23 However, the findings of scattered retrospective studies are not enough. A meta-analysis has the advantage of increasing the statistical power of small studies.

Here, we aimed to evaluate the effectiveness of dynamic susceptibility contrast-enhanced (DSC) PWI (DSC-PWI) for distinguishing between TR and RI in patients with glioma.

MATERIALS AND METHODS

Relevant articles in the PubMed, Embase, and Cochrane Library databases were identified up to October 2021. The research strategy was: ((((((perfusion weighted imaging) OR (PWI)) OR (perfusion MR)) OR (MR perfusion)) OR (dynamic susceptibility contrast-enhanced)) AND (glioma)) AND ((recurrence) OR (recurrent)) AND ((radiation injury) OR (radiation necrosis)). This meta-analysis was registered at https://inplasy.com/ (no. INPLASY 2021120028).

Studies eligible for inclusion were: (a) studies assessing the differential diagnosis of TR and RI in patients with glioma; (b) PWI was used as the diagnostic tool; (c) DSC was used as the PWI technique; and (d) studies in which sensitivity and specificity were provided. Reviews and case reports, as well as studies dealing with animals, with sample sizes less than 20, or lacking English titles and abstracts, were excluded.

Extraction of Data and Quality Evaluation

Data were extracted by 2 independent investigators, and disputes were decided by a third investigator. The data included information on first author, publication year, country, study design, blinding status, number of patients, tumor numbers, World Health Organization (WHO) grading, postoperative radiation dose, field strength of MRI, reference standards, cutoff values, and diagnostic performance (true-positive, false-positive, true-negative, and false-negative results).

The likelihood of bias in included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool.24

Definitions

Tumor recurrence was defined as the positive result, and RI was defined as the negative result. Tumor recurrence was diagnosed according to the histopathology of the lesion and whether there was growth of the lesion and deterioration of the patient's condition during the follow-up period. The RI diagnosis depended on histopathology and whether the size of the lesion decreased and the patient's condition improved during the follow-up period. High-grade glioma was defined as a tumor with a WHO grade of III or greater.19

Meta-analysis

RevMan v5.3 and Stata v12.0 were used for meta-analysis. The sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and relative cerebral blood volume (rCBV) values from the individual studies were pooled. A high likelihood of correct diagnosis for either TR or RI was represented by PLR greater than 5 or NLR less than 0.2. Summary receiver operating characteristic (SROC) curves were produced; the diagnosis was judged to be reliable when the area under the curve (AUC) was greater than 80%.

Heterogeneity was assessed by I2 tests, with I2 values greater than 50% suggesting significant heterogeneity. Random-effects models were used for significant heterogeneity, while fixed-effects models were used for significant homogeneity. A P value less than 0.05 indicated significance. The sources of heterogeneity were investigated using sensitivity, subgroup, and meta-regression analyses.

Publication bias was evaluated by the Deeks funnel plot and the Egger test, with a P value less than 0.05 indicating bias.

RESULTS

Baseline Information

We found 128 studies on the initial search. After step-by-step selection, 13 studies were finally included in the analysis (Fig. 1, Table 1). These studies included 513 patients with 522 lesions. Of the 528 lesions, 329 were TRs and 193 were RIs. All studies were retrospective in nature. Six of the studies were from Asian institutions, while 7 were produced by European or American institutions. All studies used the rCBV value from PWI as the diagnostic tool. The details of the field strength, reference standards, WHO grades, and cutoff values of rCBV are shown in Table 1.

F1
FIGURE 1:
Flowchart diagram of our meta-analysis. Figure 1 can be viewed online in color at www.jcat.org.
TABLE 1 - Characteristics of Studies Included in Meta-analysis
Studies Year Country Patients, n Tumors, n Who Grade RD, Gy FS RS Cutoff
Barajas et al 11 2009 United States 57 66 IV 59.4–60 1.5 S, B, F 1.75
Cha et al 12 2014 Korea 35 35 IV Not given 3 S, B, F 1.8
Di Costanzo et al 13 2014 Italy 29 29 IV 60 3 S, F Not given
Fink et al 14 2012 United States 40 40 II-IV 54–65 3 S, B, F 2.08
Hojjati et al 15 2018 United States 22 22 IV 58.62 3 S, F 3.32
Hu et al 16 2009 United States 40 40 III/IV 63 3 S, B, F 0.71
Ozsunar et al 17 2016 Turkey 32 32 II-IV Not given 1.5 S, B, F 1.3
Qiao et al 18 2019 China 42 42 III/IV Not given Not given S, B, F 1.83
Seeger et al 19 2016 Germany 40 40 III/IV 60 1.5 S, B, F 2.25
Sha et al 20 2013 China 52 52 II-IV 40–60 3 S, B, F 1.375
Wang et al 21 2018 China 69 69 II-IV 40–60 3 S, F 2.26
Xu et al 22 2011 China 35 35 II-IV 48–68.8 3 S, B 2.15
Young et al 23 2013 United States 20 20 IV 59.4–60 1.5/3 S, B, F 2.4
B indicates biopsy; F, follow-up; FS, field strength; RD, radiation dose; RS, reference standard; S, surgery.

The raw data detailing the true-positive, false-positive, true-negative, and false-negative results are shown in Table 2. Furthermore, 9 studies compared the rCBV values between TR and RI groups.11–14,18–22

TABLE 2 - Raw Data of Diagnostic Performance of Studies Included in This Meta-analysis
True Positive False Positive False Negative True Negative rCBV Values
Recurrence RI
Barajas et al 11 36 6 10 14 2.38 1.57
Cha et al 12 9 4 2 20 2.15 1.4
Di Costanzo et al 13 18 1 3 7 1.73 0.86
Fink et al 14 26 1 4 9 3.62 1.31
Hojjati et al 15 18 1 0 3 Not given Not given
Hu et al 16 22 0 2 16 Not given Not given
Ozsunar et al 17 19 3 3 7 Not given Not given
Qiao et al 18 26 1 7 8 2.68 1.33
Seeger et al 19 19 4 4 13 3.91 1.73
Sha et al 20 21 0 9 22 2.43 0.67
Wang et al 21 24 3 11 31 3.39 1.39
Xu et al 22 16 3 4 12 4.36 1.28
Young et al 23 16 1 0 3 Not given Not given

Risk of Bias

The QUADAS-2 tool was used to assess the possibility of bias (Figs. 2A, B). Of the 13 included studies, 9 did not indicate whether patients were enrolled in a consecutive manner.11,13–20 None of the studies provided clear information on the use of blinding. All studies described the reference standard used to confirm the diagnosis.

F2
FIGURE 2:
Representation of the methodological quality (A) graph and (B) summary. Figure 2 can be viewed online in color at www.jcat.org.

Diagnostic Performance

Nine studies compared rCBV values between the TR and RI groups.11–14,18–22 The pooled rCBV values were significantly greater in the TR group relative to the RI group (mean difference, 1.60; 95% confidence interval [CI], 1.09–2.11, P < 0.00001; Fig. 3). The heterogeneity was significant (I2 = 88%). The sensitivity analysis showed that removing individual studies had no impact on the detected heterogeneity. The Egger test found no evidence of publication bias (P = 0.367), nor was publication bias detected by the Deeks funnel plot asymmetry test (P = 0.496).

F3
FIGURE 3:
The pooled rCBV results between patients with TR and RI. Figure 3 can be viewed online in color at www.jcat.org.

The pooled values for sensitivity, specificity, PLR, and NLR were 83% (95 CI, 77%–88%; Fig. 4A), 85% (95 CI, 77%–91%; Fig. 4B), 5.60 (95 CI, 3.61–8.70; Fig. 4C), and 0.20 (95% CI, 0.14–0.27; Fig. 4D), respectively. The heterogeneity of sensitivity (I2 = 33.18%), specificity (I2 = 24.01%), PLR (I2 = 0.00%), and NLR (I2 = 6.68%) was not significant. The AUC value is 0.91 (95% CI, 0.88–0.93; Fig. 4E). The SROC curve lacked a shoulder, suggesting the absence of a threshold effect. The Fagan plot is seen in Figure 4F. The posttest PLR and NLR probabilities were estimated at 58% and 5%, respectively, when the pretest probability was 20%. The accuracy of diagnosis was found to be high.

F4
FIGURE 4:
The pooled results (A) sensitivity, (B) specificity, (C) PLR, (D) NLR, (E) SROC, and (F) Fagan diagram in this meta-analysis. Figure 4 can be viewed online in color at www.jcat.org.

Meta-Regression and Subgroup Analyses

The meta-regression analysis was conducted based on the factors of MRI field strength, tumor WHO grade, and country (Table 3). The sensitivity and specificity were not affected by any of these factors. Table 4 lists the results of the subgroup analyses. The 3.0 T MRI, high-grade glioma, and Europe/America patient subgroups showed simultaneous PLR greater than 5 and NLR less than 0.2. Different rCBV cutoff value subgroups did not show simultaneous PLR greater than 5 and NLR less than 0.2.

TABLE 3 - Results of Meta-Regression
Sensitivity Specificity
Estimate Coefficient P Estimate Coefficient P
Field strength
 1.5 T 1 1
 3.0 T 0.83 (0.66–0.93) 1.58 0.93 0.96 (0.88–0.99) 3.09 0.05
WHO grades
 II-IV 1 1
 High grade only 0.89 (0.79–0.95) 2.14 0.23 0.78 (0.54–0.91) 1.25 0.40
Countries
 Asia 1 1
 Europe/America 0.91 (0.83–0.95) 2.30 0.06 0.80 (0.56–0.92) 1.37 0.54

TABLE 4 - Subgroup Analysis
Studies, n Sensitivity (95% CI) Specificity (95% CI) PLR (95% CI) NLR (95% CI) AUC
Field strength
 1.5 T 4 84% (76%–90%) 73% (59%–83%) 3.06 (1.95–4.83) 0.22 (0.14–0.35) 0.86
 3.0 T 8 84% (74%–90%) 90% (82%–94%) 8.13 (4.71–14.04) 0.18 (0.12–0.29) 0.94
WHO grade
 II–IV 5 78% (69%–86%) 89% (76%–95%) 7.00 (3.19–15.13) 0.24 (0.17–0.35) 0.88
 High grade only 8 87% (79%–92%) 84% (73%–91%) 5.33 (3.06–9.31) 0.16 (0.09–0.27) 0.92
Countries
 Asia 6 75% (66%–82%) 90% (81%–95%) 7.18 (3.76–13.71) 0.28 (0.28–0.39) 0.84
 Europe/America 7 89% (81%–94%) 85% (72%–93%) 5.89 (2.89–11.97) 0.13 (0.07–0.25) 0.93
Cutoff value
 <2 6 80% (73%–86%) 89% (72%–97%) 7.58 (2.57–22.32) 0.22 (0.16–0.31) 0.82
 ≥2 6 88% (74%–95%) 82% (70%–90%) 4.98 (2.88–8.67) 0.15 (0.07–0.32) 0.90

DISCUSSION

The current meta-analysis assessed the efficacy of DSC-PWI for the differential diagnosis of TR and RI in patients with glioma. Tumor recurrence is usually associated with higher rCBV values than seen with RI. Dynamic susceptibility contrast-enhanced PWI is effective for evaluating the physiological status and hemodynamics of the blood vessels.25,26 Tumor recurrence after surgery and radiation therapy requires supplies of both nutrients and oxygen. It follows that the blood volume and degree of perfusion associated with the tumor will increase. The features of RIs in brain tissue differ from those of TR lesions. Radiation causes widespread tissue damage, leading to tissue necrosis and vascular occlusion.27 As necrotic tissue does not require nutrients or oxygen, blood perfusion of RIs will decrease. However, significant heterogeneity of this end point (I2 = 88%) was also detected. It is possible that this heterogeneity may have been caused by the use of different MRI instruments, the assignment of different WHO grades, or different radiation doses administered after surgery.

The pooled sensitivity and specificity indicated that DSC-PWI are useful methods for differentiating between TR and RI in patients with glioma. Moreover, the diagnostic accuracy should be evaluated via the PLR, NLR, and SROC values. The pooled PLR (5.60) and NLR (0.20) values in this meta-analysis demonstrated that a higher rCBV value (more than the cutoff) may indicate TR, while a lower rCBV value (less than the cutoff) may indicate RI. The SROC value (0.91) indicated that the overall diagnostic accuracy of DSC-PWI was high.

The heterogeneity of the sensitivity, specificity, PLR, and NLR was low. These findings indicate that these pooled diagnostic results are stable. Considering the different MRI field strengths, 3.0 T MRI was found to be better able to diagnose both TR and RI. Furthermore, we found that DSC-PWI was better able to differentiate between TR and RI in patients with high-grade glioma. High-grade gliomas have more blood supply and thus greater numbers of arteries relative to low-grade tumors.28 As DSC-PWI is effective for assessing hemodynamics, it may be more sensitive in the case of high-grade glioma.27,28

In addition to PWI, other functional MRI techniques, such as DWI and MRS, are also used for the differential diagnosis of TR and RI in patients with glioma.14,19,29 A previous study found that the AUC of the apparent diffusion coefficient was only 0.726 when using DWI as the diagnostic tool for differentiating TR and RI.14 Liu et al29 also demonstrated that the mean apparent diffusion coefficient values were comparable between TR and RI (1.57 vs 1.61, P = 0.70). These results may indicate that PWI is superior to DWI when differentiating TR and RI. Although the DWI procedure is more rapid than that of PWI as it does not require the administration of gadolinium contrast, the investment of the additional time in PWI would be both beneficial and worthwhile.14

Magnetic resonance imaging spectroscopy is also a useful method for differentiating TR and RI in patients with glioma.28,30 However, the MRS analysis is complex because it usually requires the analysis of multivoxel parameters, including the choline/creatinine, choline/N-acetyl-aspartate acid, and N-acetyl-aspartate acid/creatinine ratios.31 The use of a single-voxel MRS parameter is inferior to DSC-PWI in differentiating between TR and RI.19

Both DSC and dynamic contrast-enhanced techniques provide similar measurements of angiogenesis, which can help in diagnosis of brain tumors.32 Dynamic contrast-enhanced imaging can provide absolute measurements of plasma volume and Ktrans, which could be useful as biomarkers for angiogenesis. However, it cannot provide the same spatial coverage and temporal resolution compared with DSC imaging.32 Dynamic susceptibility contrast-enhanced perfusion-weighted imaging can provide semiquantitative measurements of rCBV from the whole brain with high temporal resolution and a short acquisition time.32

This meta-analysis has several limitations. First, all the included studies were retrospective in nature. Therefore, there is a relatively high risk of bias, and additional prospective studies would be necessary to verify and expand these results. Second, the different MRI parameters, which included the instrument manufacturers, field strength, repetition time, echo time, field of view, and matrix, were not the same among these studies. These findings further increase the risk of bias. Although we performed the subgroup analysis based on the 1.5 and 3.0 T MRI, the heterogeneity of each subgroup still cannot be eliminated. Third, the reference standards and cutoff values were not the same among these studies, and the use of different standards and cutoffs may influence the diagnostic accuracy.

In conclusion, it was found that the use of DSC-PWI is effective for the differential diagnosis of TR and RI in patients with glioma.

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Keywords:

DSC-PWI; MRI; glioma; recurrence; radiation injury

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