JNS-13666; No of Pages 7 Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis Hui Zhang a, Li Ma b,⁎, Cheng Shu a, Yu-bo Wang a, Lian-qiang Dong a,⁎ a b
Department of Neurosurgery, Air Force General Hospital of the Chinese PLA, 30 Fucheng Road, Haidian District, Beijing 100142, China Department of Anesthesiology, Beijing Military General Hospital, Beijing 100700, China
a r t i c l e
i n f o
Article history: Received 10 December 2014 Received in revised form 3 February 2015 Accepted 21 February 2015 Available online xxxx Keywords: Diffusion MRI Apparent diffusion coefficient Glioma recurrence Radiation necrosis Diagnostic accuracy Meta-analysis
a b s t r a c t Objective: Differentiating radiation necrosis from glioma recurrence remains a great challenge. Several advanced imaging modalities have been developed to differentiate between these two entities with disparate outcomes. We conducted a meta-analysis to evaluate the diagnostic quality of diffusion MRI in differentiating glioma recurrence from radiation necrosis. Method: PubMed, Embase and Chinese Biomedical databases were systematically searched to identify published articles about evaluation of diffusion MRI for the differential diagnosis of glioma recurrence from radiation necrosis. Pooled sensitivity (SEN), specificity (SPE), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated. Results: Nine studies involving 284 patients (288 lesions) met all inclusion and exclusion criteria. Quantitative synthesis of studies showed that the pooled weighted values were determined to be SEN: 0.82 (95% CI: 0.75, 0.87); SPE: 0.84 (95% CI: 0.76, 0.91); PLR: 5.10 (95% CI: 3.27, 7.95); NLR: 0.21 (95% CI: 0.15, 0.29); and DOR: 23.90 (95% CI: 12.44, 45.89). Conclusions: This meta-analysis shows that diffusion MRI has moderate diagnostic performance in differentiating glioma recurrence from radiation necrosis using quantitative ADC. It is recommended not to use diffusion MRI alone in differentiating between glioma recurrence and radiation necrosis. Multimodal imaging trials should be implemented in the future. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Glioma is the most common primary malignant brain tumor in adults. The current standard treatment is cytoreductive surgery followed by adjunctive radiotherapy and chemotherapy. However, radiation necrosis is often an undesirable but unavoidable effect of radiotherapy, and to differentiate it from recurrence remains a great challenge. Conventional morphologic imaging technologies, such as computerized tomography (CT) and magnetic resonance imaging (MRI), are usually
Abbreviations: ADC, apparent diffusion coefficient; AUC, area under the curve; CBM, Chinese Biomedical databases; Cho, choline; CI, confidence intervals; Cr, creatine; CT, computerized tomography; DOR, diagnostic odds ratio; DTI, diffusion tensor imaging; DWI, diffusion-weighted imaging; FN, false negative; FP, false positive; I2, inconsistency index; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; NAA, N-acetyl-aspartate; NLR, negative likelihood ratio; PET, positron-emission tomography; PLR, positive likelihood ratio; QUADAS-2, Quality Assessment Tool for Diagnostic Accuracy Studies version 2;SEN, sensitivity; SPE,specificity; SPECT, single photon emission computed tomography; SROC, summary receiver-operating characteristic curve; TN, true negative; TP, true positive. ⁎ Corresponding authors. E-mail addresses: [email protected] (L. Ma), [email protected] (L. Dong).
inadequate to discriminate between glioma recurrence and radiation necrosis. Both lesions show similar strong contrast enhancement, surrounding edema, and mass effect [1,2]. To solve the problem, numerous innovative imaging technologies focusing on metabolism or blood flow have been introduced, like positron-emission tomography (PET), single photon emission computed tomography (SPECT), and some advanced MRI techniques (diffusion MRI, perfusion MRI, and magnetic resonance spectroscopy [MRS], etc.). Diffusion MRI, including diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI), is a type of MRI functional imaging which reflects the microscopic motion of water molecules within the tissue [3]. While DWI is able to evaluate the extent of water molecules' random movement, DTI can provide directional information. Apparent diffusion coefficient (ADC) is the most widely used diffusion MRI parameter, it could be used to grade gliomas, and can be easily and rapidly obtained [4]. Some studies have investigated the diagnostic value of ADC in diffusion MRI for distinguishing glioma recurrence from radiation necrosis, but the findings have been incongruent. Thus, we performed the present meta-analysis to evaluate the diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating recurrent glioma from radiation necrosis.
http://dx.doi.org/10.1016/j.jns.2015.02.038 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
2. Materials and methods 2.1. Search strategy We systematically searched PubMed, Embase and Chinese Biomedical databases (CBM) to identify relevant published articles until Oct 20, 2014 using the following keywords: (“Diffusion MR” or “diffusion weighted imaging” or “DWI” or “diffusion tensor imaging” or “DTI”) AND (glioma or brain neoplasm) AND recurrence. Additionally, the reference lists of retrieved articles were also scrutinized by hand-searching to identify other potentially eligible studies that have not been identified as aforementioned. 2.2. Inclusion and exclusion criteria The inclusion criteria were: (1) clinical trials assessing the diagnostic accuracy of diffusion MRI with quantitative ADC measurements for differentiating glioma recurrence from radiation necrosis; (2) common diagnostic test parameters could be calculated from the data reported, e.g. values of true positive (TP), false positive (FP), false negative (FN), true negative (TN), sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR) and negative likelihood ratio (NLR); (3) number of patients should be at least eight; (4) no overlapping data, or else only the largest study should be included in the final analysis; and (5) only publications in English and Chinese are included. Studies were excluded if they were: animal studies, abstracts, review articles, case reports, letters, editorials, comments and conference proceedings. The whole selection was performed by two independent authors (Zhang H and Ma L) using the inclusion criteria and exclusion criteria. Any disagreement was discussed and resolved by a third author (Dong LQ). 2.3. Data extraction and quality assessment For each included study, relevant data were extracted including basal characteristics (authors, year of publication, and country of origin), patients' demographics (mean age, sex, number, type of glioma, and type of radiotherapy) and technical aspects (imaging field strength, type of diffusion MR, b value, quantitative parameter, cut-off value, reference standard, TP, FP, FN and TN value). We assessed the methodological quality of the studies by Quality Assessment Tool for Diagnostic Accuracy Studies version 2 (QUADAS-2) which consists of 4 key domains that discuss patient selection, index test, reference standard, and flow of patients through the study and timing of the index tests and reference standard (flow and timing) [5]. Disagreements were resolved by consensus. 2.4. Statistical analysis Pooled SEN, SPE, PLR, NLR, and diagnostic odds ratios (DOR) with their 95% confidence intervals (CIs) were calculated. A value of 0.5 was added to all cells of studies that contained a count of zero to avoid potential problems in odds calculations for studies with SENs or SPEs of 100% [6,7]. The extent of heterogeneity was assessed by the chi-squared value test and the inconsistency index (I2) of DOR. A random-effects model was used to compute the common effect under the assumption that the effect is heterogeneous across all studies (P b 0.1 or I2 N 50%); otherwise, a fixed-effects model was used [8,9]. Furthermore, the Spearman correlation coefficient between the logit of SEN and the logit of (1 − SPE) was computed to assess the threshold effect that is defined as one of the primary causes of heterogeneity in test accuracy studies. A strong positive correlation would suggest a threshold effect with P b 0.05. The summary receiver-operating characteristic curve (SROC), area under the curve (AUC) and Q*index (Q*index is the point on the SROC at which SEN and SPE are equal and is the best statistical method assessing diagnostic performance) were calculated.
AUC values less than 0.50 indicated that the diagnostic test was meaningless. AUC values ranging from 0.51 to 0.70 meant that the diagnostic accuracy was lower. AUC values from 0.71 to 0.90 represented moderate diagnostic accuracy. AUC values more than 0.90 illustrated high diagnostic accuracy. The publication bias was tested using Deek's funnel plot asymmetry test [10]. The above mentioned statistical analyses were performed using Meta-DiSc statistical software version 1.4 [7] and Stata 12.0 (StataCorp LP, College Station, TX). 3. Results 3.1. Study selection and characteristics Our search string found 66 articles in PubMed, 336 articles in EMBASE and 13 articles in CBM. A total of 360 articles remained after removal of duplicates. After screening titles and abstracts, 34 articles were deemed fit for full-text evaluation. We did not identify any additional potentially eligible article by perusing the reference lists of relevant articles. When full-text search was done, 13 review articles and 1 irrelevant article were excluded. Eleven articles were omitted because they did not provide enough data to calculate the common diagnostic test parameters. Finally, nine studies [11–19] met all inclusion and exclusion criteria, and were included in the present meta-analysis (Fig. 1). A total of 284 patients (288 lesions) with suspected glioma recurrence after radiotherapy were comprised. Key characteristics of the included studies are demonstrated in Table 1. Eight studies were retrospective cohort studies, and only one was a prospective cohort study. The sample size in each study varied from 8 to 55 individuals, and was generally small. The MR examinations were performed on a 3.0-T scanner in 6 studies, 1.5-T in 2 studies, and both 1.5-T and 3.0-T in one study. As for the type of diffusion MRI, DWI was utilized in 6 studies, DTI in 2 study, and both DWI and DTI in one study. The ADC ratios (ratio of the ADC of the enhancing lesion compared with the non-enhancing ADC) and ADC values were measured to distinguish recurrent glioma from radiation necrosis in 7 and 2 studies respectively. The risk of bias and the applicability concerns of the included studies were shown in Fig. 2. Overall, the study quality was satisfactory. 3.2. Quantitative synthesis Spearman correlation coefficient that was performed as a test for the threshold effect turned out to be 0.167 (P = 0.667), which indicated no notable threshold effect in the accuracy estimates among individual studies. The fixed-effects model was used since other heterogeneity did not exist either in the pooled analysis (I2 = 0). The pooled weighted values were determined to be SEN: 0.82 (95% CI: 0.75, 0.87); SPE: 0.84 (95% CI: 0.76, 0.91); PLR: 5.10 (95% CI: 3.27, 7.95); NLR: 0.21 (95% CI: 0.15, 0.29); and DOR: 23.90 (95% CI: 12.44, 45.89). The forest plots from 9 studies are shown in Fig. 3. The AUC under the SROC was 0.8897 (Fig. 4). 3.3. Sensitivity analysis Among the included studies, ADC maps were created using b = 0 and 1000 s/mm2 in 8 studies. The only study [19] using b = 0 and 700 s/mm2 was excluded from the sensitivity analysis. There was an absence of notable threshold effect in the accuracy estimates among the 8 studies (P = 0.910). The corresponding pooled SEN, SPE, PLR, NLR, and DOR were 0.84 (95% CI: 0.77, 0.89), 0.83 (95% CI: 0.74, 0.90), 4.91 (95% CI: 3.14, 7.66), 0.19 (95% CI: 0.13, 0.28), and 23.64 (95% CI: 12.15, 45.99), respectively. The AUC under the SROC was 0.8913 which was not materially different from overall AUC, indicating that our results were statistically robust.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Fig. 1. Flow diagram of the study selection process.
3.4. Publication bias Deek's funnel plot asymmetry test for the overall analysis showed that no significant publication bias existed (P = 0.286). The shape of the funnel plot of the pooled DOR also did not reveal any evidence of asymmetry (Fig. 5).
4. Discussion In previously treated glioma patients, radiation change and recurrent tumor usually can't be reliably discriminated using conventional MR techniques, such as T2-weighted and gadolinium-enhanced T1-weighted imaging. Since both lesions may be manifested as new contrast-enhancing lesions at or near the site of previous foci, new imaging techniques are required [1,2,20]. DWI and DTI are new functional MR imaging techniques which may reflect micro-movement of water molecules within vivo tissues. The magnitude and direction of free water movement are quantified with DWI and DTI, by way of the ADC for magnitude, and DTI for direction [21]. Tumor recurrence exhibits areas of high cellularity, which restricts water mobility and demonstrated a low ADC value. On the contrary, radiation necrosis shows relatively increased water mobility accompanied by an increased ADC. The SEN and SPE of DWI in differentiating tumor recurrence have yet to be fully characterized, but the findings have been incongruent. Thus, we perform the present meta-analysis with the hope of resolving the incongruities in multiple studies by increasing sample size and testing efficiency while reducing random error, and improving accuracy of evaluation of the effect size. Based on the quantitative synthesis, the summary values of SEN and SPE were 0.82 and 0.84; the maximum joint SEN and SPE (Q*index) was
0.8204; and the AUC of SROC curve was 0.8897, suggesting moderate diagnostic accuracy. The DOR is a single indicator of diagnostic test accuracy that combines the SEN and SPE data into a single number. The DOR value ranges from 0 to infinity, with higher values indicating higher accuracy and better discriminatory performance of the test [22]. In the present meta-analysis, the summary DOR of diagnostic accuracy of glioma recurrence by diffusion MRI was 23.90, indicating that this MR technique may be helpful in the diagnosis of glioma recurrence. However, the SROC curve and the DOR are not meaningful in clinical practice, and likelihood ratios are easier to interpret and use, thus both PLR and NLR are also calculated to evaluate diagnostic accuracy. PLR N 10 or NLR b 0.1 could generate large and often conclusive shifts from pretest to post-test probability, indicating high accuracy. A PLR of 5.10 in this meta-analysis suggests that patients with glioma recurrence have about a 5-fold higher chance of a positive test compared with patients with radiation necrosis. On the other hand, the NLR was 0.21, suggesting that, if ADC ratio or ADC value was low of cut-off value, the probability that this patient had glioma recurrent would be 21%, which is not low enough to rule out tumor recurrent. In addition, though a higher b-value provides better contrast and higher ADC value theoretically [23], the quantitative synthesis we performed in studies with b = 1000 s/mm2 showed that diagnostic accuracy was not materially altered. So far, diagnostic accuracy of diffusion MRI is regarded to be moderate in distinguishing glioma recurrence from radiation necrosis There are some inconsistent results when diffusion MRI was compared with other advance MR techniques. Matsusue et al. [12] reported that DWI had equivalent diagnostic efficiency with MRS and perfusion MRI. In contrast, Fink et al. [16] reported that a perfusion MR and multi-voxel MRS appeared to outperform DWI for distinguishing glioma recurrence from post-treatment effects. But it is difficult to make
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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Author
Year
Country No. of No. of Study Mean lesion patients design age (yr) (range)
Zeng b et al. [11]
2007 China
55
55
R
44 30/25 HGG (23–67) (55)
EBRT
NA
NA
3.0-T
NA
DWI
0, 1000
NONE
His + Cli
Matsusue et al. [12]
2010 USA
15
15
R
47 (30–64)
9/6
EBRT, SRS
NA
11
3.0-T
16 (3–254)a
DWI
0, 1000
Bobek-Billewicz 2010 Poland et al. [13]
11
8
R
23–68
3/5
LGG (9),HGG (6) HGG (8)
EBRT
NA
2
1.5 or 3.0 T
13 (3–70)a
DWI
0, 1000
Xu et al. [14]
2010 China
35
35
R
EBRT
48–68.8
NA
3.0-T
17 (6–52)a
DTI
0, 1000
Meng et al. [15] 2011 China
22
22
R
EBRT
40–70
NA
1.5-T
NA
DWI
Fink et al. [16]
2012 USA
39
38
R
45.2 19/16 LGG (21–65) (4),HGG (31) 45 10/12 NA (13–73) 48 20/18 NA (28–70)
MR perfusion, MRS MR perfusion, MRS NONE
NA
NA
3.0-T
NA
DWI, DTI
Sha et al. [17]
2013 China
52
52
R
50.4 30/22 NA (17–78)
EBRT, SRS, IMRT EBRT
0, 1000 0, 1000
40–70
NA
3.0-T
NA
DWI
0, 1000
Di Costanzo et al. [18]
2014 Italy
29
29
R
18/11 HGG 63 (29) (38–74a
EBRT
60
29
3.0-T
1–24
DWI
0, 1000
Alexiou et al. [19]
2014 Greece
30
30
P
61.5
EBRT
60
30
1.5-T
12 (3–24)
DTI
0, 700
M/F
21/9
Comparator Histology Radiation Radiation No. of patients Imaging Time from Type of b therapy therapy receiving field radiation diffusion value imaging 2 type dose chemotherapy strength therapy MR mm /s tests (Gy) completion to the appearance of lesion
HGG (30)
Cut-off
TP
FP FN TN
NA
30
0
2
23
His + Cli
Cho/Cr, Cho/NAA, ADC ratio ADC ratio
b=1.30
9
1
1
4
His + Cli
ADC ratio
N1.59
3
1
2
5
His + Cli
ADC ratio
b1.85
17
2
3
13
MRS
His + Cli
ADC ratio
b=1.66 14
2
1
5
MR perfusion, MRS MR perfusion,
His + Cli
ADC ratio
b=1.28 21
2
8
8
His + Cli
ADC value
25
7
5
15
Cli
ADC value
b1.07 10–3 mm2/s NA
17
1
4
7
His + Cli
ADC ratio
b1.27
15
0
8
7
MR perfusion, MRS MR perfusion, SPECT
Reference standard
Prevalence Parameter
ADC, apparent diffusion coefficient; cho, choline; Cli, clinical; cr, creatine; DTI, diffusion tensor imaging; DWI, diffusion weighted imaging; EBRT, external beam radiotherapy; F, female; HGG, high grade glioma; His, histology; IOR, intraoperative radiotherapy; LGG, low grade glioma; M, male; MRS, magnetic resonance spectroscopy; NA, not available; NAA, N-acetyl-aspartate, P, prospective; R, retrospective; RS, radiosurgery; RT, radiotherapy; SRS, stereotactic radiosurgery a Medial.
H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
Table 1 Characteristics of studies included in the meta-analysis of DWI for the differential diagnosis of glioma recurrence from radiation necrosis.
H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Fig. 2. The methodological quality analysis of the included studies using QUADAS-2 tool.
comparisons between diffusion MRI and other imaging technologies in a meta-analysis due to the limited literature and the huge heterogeneity in study design. However, we could get hints from results of some systematic studies on diagnostic accuracy of different imaging techniques [24–26], seen in Table 2. The authors determined that the PLR of 18FFDG PET was 3.4 (95% CI: 1.6, 7.5), of 11C-MET PET was 10.31 (95% CI: 0.76, 139.39), of 201Tl SPECT was 4.34 (95% CI: 2.31, 8.15), of 99mTcMIBI SPECT was 9.81 (95% CI: 5.63, 17.08), of IMT SPECT was 9.69 (95% CI: 2.97, 31.61), of MRS with Cho/Cr ratio was 4.25 (95% CI: 2.84, 6.35), and of MRS with Cho/NAA ratio 5.56 (95% CI: 3.32, 9.31); moreover, the NLRs were, respectively, 0.30 (95% CI: 0.03, 0.43), 0.32 (95% CI: 0.18, 0.57), 0.17 (95% CI: 0.13, 0.22), 0.14 (95% CI: 0.06, 0.35), 0.14 (95% CI: 0.08, 0.24), 0.22 (95% CI: 0.13, 0.38), and 0.14 (95% CI: 0.09, 0.24). The PLR and NLR in our study were 5.10 (95% CI: 3.27, 7.95) and 0.21 (95% CI: 0.15, 0.29) respectively, which indicated a mediocre ability of diffusion MRI for identifying glioma recurrence, and an inferior capacity for ruling out tumor recurrence. We can conclude that diffusion MRI is not fit for differentiating glioma recurrence from radiation necrosis when used alone. Though with a mediocre diagnostic accuracy in distinguishing glioma recurrence from radiation necrosis, diffusion MRI is not useless. diffusion MRI is nonradiative, less expensive, and does not require exogenous contrast medium. Most importantly, it still has its role in combined techniques. To name a case, one included article [18] reported that in serial MRI, a discrimination accuracy of 96.6% could be achieved when predictors of MRS, DWI and perfusion MRI were considered while the accuracy was 89.7% with MRS and perfusion MRI combined, 86.2% with MRS and DWI combined, only 79.3% with MRS alone. However, when multimodal imaging trials were designed, the relative value and the sequences of imaging techniques should be given full consideration. Besides, easy accessibility and cost-effectiveness should also be taken into account. The results in this meta-analysis should be interpreted with caution because of some reasons. Firstly, although the threshold effect and
heterogeneity did not found, the included studies varied a lot in many aspects. As we know, different field strength (1.5 T and 3 T), different types of coils (8, 16, 32-channel coil) and different MRI manufacturers with different methodology and post-processing could give unexpected substandard results. Specifics of MRI devices used in each included article were generally vague and may be various, heterogeneity caused by this was inevitable. Heterogeneity could also be derived from different designs (prospective or retrospective), different diffusion MRI parameters adopted (ADC ratio or ADC value) and diverse methods to determine cut-off values. Besides, there is evidence that radiation with the addition of chemotherapy causes more frequently and severe treatment related necrosis [27]. Although all patients included received radiation, different ratio of patients in each article received chemotherapy, which could also contribute to heterogeneity. Secondly, most of the studies included adopted two different reference standards (histology and clinical followed-up), which can lead to overestimation of SEN and SPE on diagnostic test [28]. Thirdly, only full-text papers published in the English and Chinese language were included in this metaanalysis, which means some eligible studies that were unpublished or reported in other languages may be left out. Thus, some inevitable publication bias may exist in the results, although Deek's funnel plot asymmetry test for the overall analysis showed no significant publication bias existed. 5. Conclusions This meta-analysis provides evidence that diffusion MRI with quantitative ADC measurements has moderate diagnostic performance in differentiating glioma recurrence from radiation necrosis. It is strongly recommended that diffusion MRI should combine with other advanced imaging technologies to improve diagnostic accuracy. This article underlines the importance of implementing multimodal imaging trials and multicentre trials in the future. In the interpretation of our results, the limitations mentioned should be kept in mind.
Fig. 3. Forest plot of SEN (A), SPE (B), PLR (C), and NLR (D) of diffusion MRI in the differentiation of recurrent glioma from radiation necrosis.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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Fig. 4. Summary receiver-operating characteristic curve (SROC).
Fig. 5. The funnel plot of publication bias.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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Table 2 Summary of meta-analysis focused on functional imaging techniques for differentiating glioma recurrence from radiation necrosis. Study
Type of imaging techniques
SEN (95% CI)
SPE (95% CI)
LR+ (95% CI)
LR — (95% CI)
AUC (SE)
Q*index
Radiation
Nihashi et al. [24]
18
0.77 [0.66, 0.85] 0.79 [0.50, 0.84] 0.88 [0.84, 0.91] 0.89 [0.84, 0.92] 0.89 [0.81, 0.95] 0.83 [0.77, 0.89] 0.88 [0.81, 0.93] 0.82 [0.75, 0.87]
0.78 [0.54, 0.91] 0.93 [0.44, 1.00] 0.86 [0.79, 0.91] 0.92 [0.86, 0.96] 0.96 [0.80, 1.00] 0.83 [0.74, 0.90] 0.86 [0.76, 0.93] 0.84 [0.76, 0.91]
3.4 [1.6, 7.5] 10.31 [0.76, 139.39] 4.34 [2.31, 8.15] 9.81 [5.63, 17.08] 9.69 [2.97, 31.61] 4.25 [2.84, 6.35] 5.56 [3.32, 9.31] 5.10 [3.27, 7.95]
0.30 [0.21, 0.43] 0.32 [0.18, 0.57] 0.17 [0.13, 0.22] 0.14 [0.06, 0.35] 0.14 [0.08, 0.24] 0.22 [0.13, 0.38] 0.14 [0.09, 0.24] 0.21 [0.15, 0.29]
NA NA 0.9315 (0.0166) 0.9497 (0.0276) 0.9546 (0.0267) 0.9001 (0.0263) 0.9185 (0.0267) 0.8897 (0.0259)
0.77 0.79 NA NA NA 0.8313 0.8516 0.8204
Y Y Y Y Y N N N
Zhang et al. [25]
Zhang et al. [26] Present study
F-FDG PET 11 C-MET PET 201 Tl SPECT 99m Tc-MIBI SPECT IMT SPECT MRS Cho/Cr MRS Cho/NAA Diffusion MRI ADC
11
C-MET, 11C-methyl-methionine; 99mTc-MIBI, technetium-99m methoxyisobutylisonitrile; ADC, apparent diffusion coefficient; Cho, choline; Cr, creatine; FDG, 18F-deoxyglucose; IMT, 3-[123I]iodo-a-methyl-L-tyrosine; N, no; NA, not available; NAA, N-acetyl-aspartate; Y, yes.
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[14] Xu JL, Li YL, Lian JM, Dou SW, Yan FS, Wu H, et al. Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology 2010;52(12):1193–9. [15] Meng MZ, Zhou SL, Miao ZC, Yuan G. MR DWI combined with (1)H-MRS analysis of multi-parameter in distinction of brain glioma recurrence and post-radiation brain injuries. Chin J Med Imaging Technol 2011;27(5):905–9. [16] Fink JR, Carr RB, Matsusue E, Iyer RS, Rockhill JK, Haynor DR, et al. Comparison of 3 Tesla proton MR spectroscopy, MR perfusion and MR diffusion for distinguishing glioma recurrence from posttreatment effects. J Magn Reson Imaging 2012;35(1): 56–63. [17] Sha L, Fan GG, Cao Q, Bian J. MR perfusion weighted imaging combined with MR diffusion weighed imaging in differentiating recurrent cerebral gliomas and radiation injury. J Chin Clin Med Imaging 2013;24(12):841–5. [18] Di Costanzo A, Scarabino T, Trojsi F, Popolizio T, Bonavita S, de Cristofaro M, et al. Recurrent glioblastoma multiforme versus radiation injury: a multiparametric 3-T MR approach. Radiol Med 2014;119(8):616–24. [19] Alexiou GA, Zikou A, Tsiouris S, Goussia A, Kosta P, Papadopoulos A, et al. Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tctetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014;32(7):854–9. [20] Caroline I, Rosenthal MA. Imaging modalities in high-grade gliomas: pseudoprogression, recurrence, or necrosis? J Clin Neurosci 2012;19(5):633–7. [21] Gerstner ER, Sorensen AG. Diffusion and diffusion tensor imaging in brain cancer. Semin Radiat Oncol 2011;21(2):141–6. [22] Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol 2003;56(11):1129–35. [23] DeLano MC, Cooper TG, Siebert JE, Potchen MJ, Kuppusamy K. High-b-value diffusion-weighted MR imaging of adult brain: image contrast and apparent diffusion coefficient map features. AJNR Am J Neuroradiol 2000;21(10):1830–6. [24] Nihashi T, Dahabreh IJ, Terasawa T. Diagnostic accuracy of PET for recurrent glioma diagnosis: a meta-analysis. AJNR Am J Neuroradiol 2013;34(5):944–50 (S1–11). [25] Zhang H, Ma L, Wu C, Xu BN. Performance of SPECT in the differential diagnosis of glioma recurrence from radiation necrosis. J Clin Neurosci 2015;22(2):229–37. [26] Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN. Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol 2014;83(12):2181–9. [27] Brandsma D, Stalpers L, Taal W, Sminia P, van den Bent MJ. Clinical features, mechanisms, and management of pseudoprogression in malignant gliomas. Lancet Oncol 2008;9(5):453–61. [28] Lijmer JG, Mol BW, Heisterkamp S, Bonsel GJ, Prins MH, van der Meulen JH, et al. Empirical evidence of design-related bias in studies of diagnostic tests. JAMA 1999; 282(11):1061–6.
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Contents lists available at ScienceDirect
Journal of the Neurological Sciences journal homepage: www.elsevier.com/locate/jns
Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necrosis Hui Zhang a, Li Ma b,⁎, Cheng Shu a, Yu-bo Wang a, Lian-qiang Dong a,⁎ a b
Department of Neurosurgery, Air Force General Hospital of the Chinese PLA, 30 Fucheng Road, Haidian District, Beijing 100142, China Department of Anesthesiology, Beijing Military General Hospital, Beijing 100700, China
a r t i c l e
i n f o
Article history: Received 10 December 2014 Received in revised form 3 February 2015 Accepted 21 February 2015 Available online xxxx Keywords: Diffusion MRI Apparent diffusion coefficient Glioma recurrence Radiation necrosis Diagnostic accuracy Meta-analysis
a b s t r a c t Objective: Differentiating radiation necrosis from glioma recurrence remains a great challenge. Several advanced imaging modalities have been developed to differentiate between these two entities with disparate outcomes. We conducted a meta-analysis to evaluate the diagnostic quality of diffusion MRI in differentiating glioma recurrence from radiation necrosis. Method: PubMed, Embase and Chinese Biomedical databases were systematically searched to identify published articles about evaluation of diffusion MRI for the differential diagnosis of glioma recurrence from radiation necrosis. Pooled sensitivity (SEN), specificity (SPE), negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated. Results: Nine studies involving 284 patients (288 lesions) met all inclusion and exclusion criteria. Quantitative synthesis of studies showed that the pooled weighted values were determined to be SEN: 0.82 (95% CI: 0.75, 0.87); SPE: 0.84 (95% CI: 0.76, 0.91); PLR: 5.10 (95% CI: 3.27, 7.95); NLR: 0.21 (95% CI: 0.15, 0.29); and DOR: 23.90 (95% CI: 12.44, 45.89). Conclusions: This meta-analysis shows that diffusion MRI has moderate diagnostic performance in differentiating glioma recurrence from radiation necrosis using quantitative ADC. It is recommended not to use diffusion MRI alone in differentiating between glioma recurrence and radiation necrosis. Multimodal imaging trials should be implemented in the future. © 2015 Elsevier B.V. All rights reserved.
1. Introduction Glioma is the most common primary malignant brain tumor in adults. The current standard treatment is cytoreductive surgery followed by adjunctive radiotherapy and chemotherapy. However, radiation necrosis is often an undesirable but unavoidable effect of radiotherapy, and to differentiate it from recurrence remains a great challenge. Conventional morphologic imaging technologies, such as computerized tomography (CT) and magnetic resonance imaging (MRI), are usually
Abbreviations: ADC, apparent diffusion coefficient; AUC, area under the curve; CBM, Chinese Biomedical databases; Cho, choline; CI, confidence intervals; Cr, creatine; CT, computerized tomography; DOR, diagnostic odds ratio; DTI, diffusion tensor imaging; DWI, diffusion-weighted imaging; FN, false negative; FP, false positive; I2, inconsistency index; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; NAA, N-acetyl-aspartate; NLR, negative likelihood ratio; PET, positron-emission tomography; PLR, positive likelihood ratio; QUADAS-2, Quality Assessment Tool for Diagnostic Accuracy Studies version 2;SEN, sensitivity; SPE,specificity; SPECT, single photon emission computed tomography; SROC, summary receiver-operating characteristic curve; TN, true negative; TP, true positive. ⁎ Corresponding authors. E-mail addresses: [email protected] (L. Ma), [email protected] (L. Dong).
inadequate to discriminate between glioma recurrence and radiation necrosis. Both lesions show similar strong contrast enhancement, surrounding edema, and mass effect [1,2]. To solve the problem, numerous innovative imaging technologies focusing on metabolism or blood flow have been introduced, like positron-emission tomography (PET), single photon emission computed tomography (SPECT), and some advanced MRI techniques (diffusion MRI, perfusion MRI, and magnetic resonance spectroscopy [MRS], etc.). Diffusion MRI, including diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI), is a type of MRI functional imaging which reflects the microscopic motion of water molecules within the tissue [3]. While DWI is able to evaluate the extent of water molecules' random movement, DTI can provide directional information. Apparent diffusion coefficient (ADC) is the most widely used diffusion MRI parameter, it could be used to grade gliomas, and can be easily and rapidly obtained [4]. Some studies have investigated the diagnostic value of ADC in diffusion MRI for distinguishing glioma recurrence from radiation necrosis, but the findings have been incongruent. Thus, we performed the present meta-analysis to evaluate the diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating recurrent glioma from radiation necrosis.
http://dx.doi.org/10.1016/j.jns.2015.02.038 0022-510X/© 2015 Elsevier B.V. All rights reserved.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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2. Materials and methods 2.1. Search strategy We systematically searched PubMed, Embase and Chinese Biomedical databases (CBM) to identify relevant published articles until Oct 20, 2014 using the following keywords: (“Diffusion MR” or “diffusion weighted imaging” or “DWI” or “diffusion tensor imaging” or “DTI”) AND (glioma or brain neoplasm) AND recurrence. Additionally, the reference lists of retrieved articles were also scrutinized by hand-searching to identify other potentially eligible studies that have not been identified as aforementioned. 2.2. Inclusion and exclusion criteria The inclusion criteria were: (1) clinical trials assessing the diagnostic accuracy of diffusion MRI with quantitative ADC measurements for differentiating glioma recurrence from radiation necrosis; (2) common diagnostic test parameters could be calculated from the data reported, e.g. values of true positive (TP), false positive (FP), false negative (FN), true negative (TN), sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR) and negative likelihood ratio (NLR); (3) number of patients should be at least eight; (4) no overlapping data, or else only the largest study should be included in the final analysis; and (5) only publications in English and Chinese are included. Studies were excluded if they were: animal studies, abstracts, review articles, case reports, letters, editorials, comments and conference proceedings. The whole selection was performed by two independent authors (Zhang H and Ma L) using the inclusion criteria and exclusion criteria. Any disagreement was discussed and resolved by a third author (Dong LQ). 2.3. Data extraction and quality assessment For each included study, relevant data were extracted including basal characteristics (authors, year of publication, and country of origin), patients' demographics (mean age, sex, number, type of glioma, and type of radiotherapy) and technical aspects (imaging field strength, type of diffusion MR, b value, quantitative parameter, cut-off value, reference standard, TP, FP, FN and TN value). We assessed the methodological quality of the studies by Quality Assessment Tool for Diagnostic Accuracy Studies version 2 (QUADAS-2) which consists of 4 key domains that discuss patient selection, index test, reference standard, and flow of patients through the study and timing of the index tests and reference standard (flow and timing) [5]. Disagreements were resolved by consensus. 2.4. Statistical analysis Pooled SEN, SPE, PLR, NLR, and diagnostic odds ratios (DOR) with their 95% confidence intervals (CIs) were calculated. A value of 0.5 was added to all cells of studies that contained a count of zero to avoid potential problems in odds calculations for studies with SENs or SPEs of 100% [6,7]. The extent of heterogeneity was assessed by the chi-squared value test and the inconsistency index (I2) of DOR. A random-effects model was used to compute the common effect under the assumption that the effect is heterogeneous across all studies (P b 0.1 or I2 N 50%); otherwise, a fixed-effects model was used [8,9]. Furthermore, the Spearman correlation coefficient between the logit of SEN and the logit of (1 − SPE) was computed to assess the threshold effect that is defined as one of the primary causes of heterogeneity in test accuracy studies. A strong positive correlation would suggest a threshold effect with P b 0.05. The summary receiver-operating characteristic curve (SROC), area under the curve (AUC) and Q*index (Q*index is the point on the SROC at which SEN and SPE are equal and is the best statistical method assessing diagnostic performance) were calculated.
AUC values less than 0.50 indicated that the diagnostic test was meaningless. AUC values ranging from 0.51 to 0.70 meant that the diagnostic accuracy was lower. AUC values from 0.71 to 0.90 represented moderate diagnostic accuracy. AUC values more than 0.90 illustrated high diagnostic accuracy. The publication bias was tested using Deek's funnel plot asymmetry test [10]. The above mentioned statistical analyses were performed using Meta-DiSc statistical software version 1.4 [7] and Stata 12.0 (StataCorp LP, College Station, TX). 3. Results 3.1. Study selection and characteristics Our search string found 66 articles in PubMed, 336 articles in EMBASE and 13 articles in CBM. A total of 360 articles remained after removal of duplicates. After screening titles and abstracts, 34 articles were deemed fit for full-text evaluation. We did not identify any additional potentially eligible article by perusing the reference lists of relevant articles. When full-text search was done, 13 review articles and 1 irrelevant article were excluded. Eleven articles were omitted because they did not provide enough data to calculate the common diagnostic test parameters. Finally, nine studies [11–19] met all inclusion and exclusion criteria, and were included in the present meta-analysis (Fig. 1). A total of 284 patients (288 lesions) with suspected glioma recurrence after radiotherapy were comprised. Key characteristics of the included studies are demonstrated in Table 1. Eight studies were retrospective cohort studies, and only one was a prospective cohort study. The sample size in each study varied from 8 to 55 individuals, and was generally small. The MR examinations were performed on a 3.0-T scanner in 6 studies, 1.5-T in 2 studies, and both 1.5-T and 3.0-T in one study. As for the type of diffusion MRI, DWI was utilized in 6 studies, DTI in 2 study, and both DWI and DTI in one study. The ADC ratios (ratio of the ADC of the enhancing lesion compared with the non-enhancing ADC) and ADC values were measured to distinguish recurrent glioma from radiation necrosis in 7 and 2 studies respectively. The risk of bias and the applicability concerns of the included studies were shown in Fig. 2. Overall, the study quality was satisfactory. 3.2. Quantitative synthesis Spearman correlation coefficient that was performed as a test for the threshold effect turned out to be 0.167 (P = 0.667), which indicated no notable threshold effect in the accuracy estimates among individual studies. The fixed-effects model was used since other heterogeneity did not exist either in the pooled analysis (I2 = 0). The pooled weighted values were determined to be SEN: 0.82 (95% CI: 0.75, 0.87); SPE: 0.84 (95% CI: 0.76, 0.91); PLR: 5.10 (95% CI: 3.27, 7.95); NLR: 0.21 (95% CI: 0.15, 0.29); and DOR: 23.90 (95% CI: 12.44, 45.89). The forest plots from 9 studies are shown in Fig. 3. The AUC under the SROC was 0.8897 (Fig. 4). 3.3. Sensitivity analysis Among the included studies, ADC maps were created using b = 0 and 1000 s/mm2 in 8 studies. The only study [19] using b = 0 and 700 s/mm2 was excluded from the sensitivity analysis. There was an absence of notable threshold effect in the accuracy estimates among the 8 studies (P = 0.910). The corresponding pooled SEN, SPE, PLR, NLR, and DOR were 0.84 (95% CI: 0.77, 0.89), 0.83 (95% CI: 0.74, 0.90), 4.91 (95% CI: 3.14, 7.66), 0.19 (95% CI: 0.13, 0.28), and 23.64 (95% CI: 12.15, 45.99), respectively. The AUC under the SROC was 0.8913 which was not materially different from overall AUC, indicating that our results were statistically robust.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
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Fig. 1. Flow diagram of the study selection process.
3.4. Publication bias Deek's funnel plot asymmetry test for the overall analysis showed that no significant publication bias existed (P = 0.286). The shape of the funnel plot of the pooled DOR also did not reveal any evidence of asymmetry (Fig. 5).
4. Discussion In previously treated glioma patients, radiation change and recurrent tumor usually can't be reliably discriminated using conventional MR techniques, such as T2-weighted and gadolinium-enhanced T1-weighted imaging. Since both lesions may be manifested as new contrast-enhancing lesions at or near the site of previous foci, new imaging techniques are required [1,2,20]. DWI and DTI are new functional MR imaging techniques which may reflect micro-movement of water molecules within vivo tissues. The magnitude and direction of free water movement are quantified with DWI and DTI, by way of the ADC for magnitude, and DTI for direction [21]. Tumor recurrence exhibits areas of high cellularity, which restricts water mobility and demonstrated a low ADC value. On the contrary, radiation necrosis shows relatively increased water mobility accompanied by an increased ADC. The SEN and SPE of DWI in differentiating tumor recurrence have yet to be fully characterized, but the findings have been incongruent. Thus, we perform the present meta-analysis with the hope of resolving the incongruities in multiple studies by increasing sample size and testing efficiency while reducing random error, and improving accuracy of evaluation of the effect size. Based on the quantitative synthesis, the summary values of SEN and SPE were 0.82 and 0.84; the maximum joint SEN and SPE (Q*index) was
0.8204; and the AUC of SROC curve was 0.8897, suggesting moderate diagnostic accuracy. The DOR is a single indicator of diagnostic test accuracy that combines the SEN and SPE data into a single number. The DOR value ranges from 0 to infinity, with higher values indicating higher accuracy and better discriminatory performance of the test [22]. In the present meta-analysis, the summary DOR of diagnostic accuracy of glioma recurrence by diffusion MRI was 23.90, indicating that this MR technique may be helpful in the diagnosis of glioma recurrence. However, the SROC curve and the DOR are not meaningful in clinical practice, and likelihood ratios are easier to interpret and use, thus both PLR and NLR are also calculated to evaluate diagnostic accuracy. PLR N 10 or NLR b 0.1 could generate large and often conclusive shifts from pretest to post-test probability, indicating high accuracy. A PLR of 5.10 in this meta-analysis suggests that patients with glioma recurrence have about a 5-fold higher chance of a positive test compared with patients with radiation necrosis. On the other hand, the NLR was 0.21, suggesting that, if ADC ratio or ADC value was low of cut-off value, the probability that this patient had glioma recurrent would be 21%, which is not low enough to rule out tumor recurrent. In addition, though a higher b-value provides better contrast and higher ADC value theoretically [23], the quantitative synthesis we performed in studies with b = 1000 s/mm2 showed that diagnostic accuracy was not materially altered. So far, diagnostic accuracy of diffusion MRI is regarded to be moderate in distinguishing glioma recurrence from radiation necrosis There are some inconsistent results when diffusion MRI was compared with other advance MR techniques. Matsusue et al. [12] reported that DWI had equivalent diagnostic efficiency with MRS and perfusion MRI. In contrast, Fink et al. [16] reported that a perfusion MR and multi-voxel MRS appeared to outperform DWI for distinguishing glioma recurrence from post-treatment effects. But it is difficult to make
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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Author
Year
Country No. of No. of Study Mean lesion patients design age (yr) (range)
Zeng b et al. [11]
2007 China
55
55
R
44 30/25 HGG (23–67) (55)
EBRT
NA
NA
3.0-T
NA
DWI
0, 1000
NONE
His + Cli
Matsusue et al. [12]
2010 USA
15
15
R
47 (30–64)
9/6
EBRT, SRS
NA
11
3.0-T
16 (3–254)a
DWI
0, 1000
Bobek-Billewicz 2010 Poland et al. [13]
11
8
R
23–68
3/5
LGG (9),HGG (6) HGG (8)
EBRT
NA
2
1.5 or 3.0 T
13 (3–70)a
DWI
0, 1000
Xu et al. [14]
2010 China
35
35
R
EBRT
48–68.8
NA
3.0-T
17 (6–52)a
DTI
0, 1000
Meng et al. [15] 2011 China
22
22
R
EBRT
40–70
NA
1.5-T
NA
DWI
Fink et al. [16]
2012 USA
39
38
R
45.2 19/16 LGG (21–65) (4),HGG (31) 45 10/12 NA (13–73) 48 20/18 NA (28–70)
MR perfusion, MRS MR perfusion, MRS NONE
NA
NA
3.0-T
NA
DWI, DTI
Sha et al. [17]
2013 China
52
52
R
50.4 30/22 NA (17–78)
EBRT, SRS, IMRT EBRT
0, 1000 0, 1000
40–70
NA
3.0-T
NA
DWI
0, 1000
Di Costanzo et al. [18]
2014 Italy
29
29
R
18/11 HGG 63 (29) (38–74a
EBRT
60
29
3.0-T
1–24
DWI
0, 1000
Alexiou et al. [19]
2014 Greece
30
30
P
61.5
EBRT
60
30
1.5-T
12 (3–24)
DTI
0, 700
M/F
21/9
Comparator Histology Radiation Radiation No. of patients Imaging Time from Type of b therapy therapy receiving field radiation diffusion value imaging 2 type dose chemotherapy strength therapy MR mm /s tests (Gy) completion to the appearance of lesion
HGG (30)
Cut-off
TP
FP FN TN
NA
30
0
2
23
His + Cli
Cho/Cr, Cho/NAA, ADC ratio ADC ratio
b=1.30
9
1
1
4
His + Cli
ADC ratio
N1.59
3
1
2
5
His + Cli
ADC ratio
b1.85
17
2
3
13
MRS
His + Cli
ADC ratio
b=1.66 14
2
1
5
MR perfusion, MRS MR perfusion,
His + Cli
ADC ratio
b=1.28 21
2
8
8
His + Cli
ADC value
25
7
5
15
Cli
ADC value
b1.07 10–3 mm2/s NA
17
1
4
7
His + Cli
ADC ratio
b1.27
15
0
8
7
MR perfusion, MRS MR perfusion, SPECT
Reference standard
Prevalence Parameter
ADC, apparent diffusion coefficient; cho, choline; Cli, clinical; cr, creatine; DTI, diffusion tensor imaging; DWI, diffusion weighted imaging; EBRT, external beam radiotherapy; F, female; HGG, high grade glioma; His, histology; IOR, intraoperative radiotherapy; LGG, low grade glioma; M, male; MRS, magnetic resonance spectroscopy; NA, not available; NAA, N-acetyl-aspartate, P, prospective; R, retrospective; RS, radiosurgery; RT, radiotherapy; SRS, stereotactic radiosurgery a Medial.
H. Zhang et al. / Journal of the Neurological Sciences xxx (2015) xxx–xxx
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
Table 1 Characteristics of studies included in the meta-analysis of DWI for the differential diagnosis of glioma recurrence from radiation necrosis.
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Fig. 2. The methodological quality analysis of the included studies using QUADAS-2 tool.
comparisons between diffusion MRI and other imaging technologies in a meta-analysis due to the limited literature and the huge heterogeneity in study design. However, we could get hints from results of some systematic studies on diagnostic accuracy of different imaging techniques [24–26], seen in Table 2. The authors determined that the PLR of 18FFDG PET was 3.4 (95% CI: 1.6, 7.5), of 11C-MET PET was 10.31 (95% CI: 0.76, 139.39), of 201Tl SPECT was 4.34 (95% CI: 2.31, 8.15), of 99mTcMIBI SPECT was 9.81 (95% CI: 5.63, 17.08), of IMT SPECT was 9.69 (95% CI: 2.97, 31.61), of MRS with Cho/Cr ratio was 4.25 (95% CI: 2.84, 6.35), and of MRS with Cho/NAA ratio 5.56 (95% CI: 3.32, 9.31); moreover, the NLRs were, respectively, 0.30 (95% CI: 0.03, 0.43), 0.32 (95% CI: 0.18, 0.57), 0.17 (95% CI: 0.13, 0.22), 0.14 (95% CI: 0.06, 0.35), 0.14 (95% CI: 0.08, 0.24), 0.22 (95% CI: 0.13, 0.38), and 0.14 (95% CI: 0.09, 0.24). The PLR and NLR in our study were 5.10 (95% CI: 3.27, 7.95) and 0.21 (95% CI: 0.15, 0.29) respectively, which indicated a mediocre ability of diffusion MRI for identifying glioma recurrence, and an inferior capacity for ruling out tumor recurrence. We can conclude that diffusion MRI is not fit for differentiating glioma recurrence from radiation necrosis when used alone. Though with a mediocre diagnostic accuracy in distinguishing glioma recurrence from radiation necrosis, diffusion MRI is not useless. diffusion MRI is nonradiative, less expensive, and does not require exogenous contrast medium. Most importantly, it still has its role in combined techniques. To name a case, one included article [18] reported that in serial MRI, a discrimination accuracy of 96.6% could be achieved when predictors of MRS, DWI and perfusion MRI were considered while the accuracy was 89.7% with MRS and perfusion MRI combined, 86.2% with MRS and DWI combined, only 79.3% with MRS alone. However, when multimodal imaging trials were designed, the relative value and the sequences of imaging techniques should be given full consideration. Besides, easy accessibility and cost-effectiveness should also be taken into account. The results in this meta-analysis should be interpreted with caution because of some reasons. Firstly, although the threshold effect and
heterogeneity did not found, the included studies varied a lot in many aspects. As we know, different field strength (1.5 T and 3 T), different types of coils (8, 16, 32-channel coil) and different MRI manufacturers with different methodology and post-processing could give unexpected substandard results. Specifics of MRI devices used in each included article were generally vague and may be various, heterogeneity caused by this was inevitable. Heterogeneity could also be derived from different designs (prospective or retrospective), different diffusion MRI parameters adopted (ADC ratio or ADC value) and diverse methods to determine cut-off values. Besides, there is evidence that radiation with the addition of chemotherapy causes more frequently and severe treatment related necrosis [27]. Although all patients included received radiation, different ratio of patients in each article received chemotherapy, which could also contribute to heterogeneity. Secondly, most of the studies included adopted two different reference standards (histology and clinical followed-up), which can lead to overestimation of SEN and SPE on diagnostic test [28]. Thirdly, only full-text papers published in the English and Chinese language were included in this metaanalysis, which means some eligible studies that were unpublished or reported in other languages may be left out. Thus, some inevitable publication bias may exist in the results, although Deek's funnel plot asymmetry test for the overall analysis showed no significant publication bias existed. 5. Conclusions This meta-analysis provides evidence that diffusion MRI with quantitative ADC measurements has moderate diagnostic performance in differentiating glioma recurrence from radiation necrosis. It is strongly recommended that diffusion MRI should combine with other advanced imaging technologies to improve diagnostic accuracy. This article underlines the importance of implementing multimodal imaging trials and multicentre trials in the future. In the interpretation of our results, the limitations mentioned should be kept in mind.
Fig. 3. Forest plot of SEN (A), SPE (B), PLR (C), and NLR (D) of diffusion MRI in the differentiation of recurrent glioma from radiation necrosis.
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Fig. 4. Summary receiver-operating characteristic curve (SROC).
Fig. 5. The funnel plot of publication bias.
Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
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Table 2 Summary of meta-analysis focused on functional imaging techniques for differentiating glioma recurrence from radiation necrosis. Study
Type of imaging techniques
SEN (95% CI)
SPE (95% CI)
LR+ (95% CI)
LR — (95% CI)
AUC (SE)
Q*index
Radiation
Nihashi et al. [24]
18
0.77 [0.66, 0.85] 0.79 [0.50, 0.84] 0.88 [0.84, 0.91] 0.89 [0.84, 0.92] 0.89 [0.81, 0.95] 0.83 [0.77, 0.89] 0.88 [0.81, 0.93] 0.82 [0.75, 0.87]
0.78 [0.54, 0.91] 0.93 [0.44, 1.00] 0.86 [0.79, 0.91] 0.92 [0.86, 0.96] 0.96 [0.80, 1.00] 0.83 [0.74, 0.90] 0.86 [0.76, 0.93] 0.84 [0.76, 0.91]
3.4 [1.6, 7.5] 10.31 [0.76, 139.39] 4.34 [2.31, 8.15] 9.81 [5.63, 17.08] 9.69 [2.97, 31.61] 4.25 [2.84, 6.35] 5.56 [3.32, 9.31] 5.10 [3.27, 7.95]
0.30 [0.21, 0.43] 0.32 [0.18, 0.57] 0.17 [0.13, 0.22] 0.14 [0.06, 0.35] 0.14 [0.08, 0.24] 0.22 [0.13, 0.38] 0.14 [0.09, 0.24] 0.21 [0.15, 0.29]
NA NA 0.9315 (0.0166) 0.9497 (0.0276) 0.9546 (0.0267) 0.9001 (0.0263) 0.9185 (0.0267) 0.8897 (0.0259)
0.77 0.79 NA NA NA 0.8313 0.8516 0.8204
Y Y Y Y Y N N N
Zhang et al. [25]
Zhang et al. [26] Present study
F-FDG PET 11 C-MET PET 201 Tl SPECT 99m Tc-MIBI SPECT IMT SPECT MRS Cho/Cr MRS Cho/NAA Diffusion MRI ADC
11
C-MET, 11C-methyl-methionine; 99mTc-MIBI, technetium-99m methoxyisobutylisonitrile; ADC, apparent diffusion coefficient; Cho, choline; Cr, creatine; FDG, 18F-deoxyglucose; IMT, 3-[123I]iodo-a-methyl-L-tyrosine; N, no; NA, not available; NAA, N-acetyl-aspartate; Y, yes.
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Please cite this article as: Zhang H, et al, Diagnostic accuracy of diffusion MRI with quantitative ADC measurements in differentiating glioma recurrence from radiation necros..., J Neurol Sci (2015), http://dx.doi.org/10.1016/j.jns.2015.02.038
