BioMed Research International
Volume 2019 (2019), Article ID 8534761, 15 pages
https://doi.org/10.1155/2019/8534761

Review Article

Diagnostic and Predictive Value of Using RGD PET/CT in Patients with Cancer: A Systematic Review and Meta-Analysis

Jie Liu,1,2 Shuanghu Yuan,2,3 Linlin Wang,2,3 Xindong Sun,2,3 Xudong Hu,2,3 Xue Meng,2,3 and Jinming Yu2,3

¹School of Medicine and Life Sciences, University of Jinan-Shandong Academy of Medical Sciences, Jinan, Shandong 250022, China
²Department of Radiation Oncology, Shandong Cancer Hospital and Institute-Shandong Cancer Hospital Affiliated to Shandong University, No. 440 Jiyan Road, Jinan 250117, Shandong, China
³Shandong Academy of Medical Sciences, Jinan 250022, Shandong, China

Correspondence should be addressed to Xue Meng; mengxue5409@126.com and Jinming Yu; sdyujinming@163.com

Received 7 October 2018; Accepted 4 December 2018; Published 10 January 2019

Academic Editor: Olaf Prante

Copyright © 2019 Jie Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

The purpose of this study was to assess the diagnostic value of arginine-glycine-aspartic acid (RGD) PET/CT for tumor detection in patients with suspected malignant lesions and to determine the predictive performance of RGD PET/CT in identifying responders. Methods. The PubMed (Medline), EMBASE, Cochrane Library, and Web of Science databases were systematically searched for potentially relevant publications (last updated on July 28th, 2018) reporting the performance of RGD PET in the field of oncology. Pooled sensitivities, specificities, and diagnostic odds ratios (DORs) were calculated for parameters. The areas under the curve (AUCs) and index scores were determined from the constructed summary receiver operating characteristic (SROC) curve. We explored heterogeneity by metaregression. Results. Nine studies, five including 216 patients that determined diagnostic performance and three including 75 patients that determined the predictive value of parameters, met our inclusion criteria. The pooled sensitivity, pooled specificity, DOR, AUC, and index score of RGD PET/CT for the detection of underlying malignancy were 0.85 (0.79-0.89), 0.93 (0.90-0.96), 48.35 (18.95-123.33), 0.9262 (standard error=0.0216), and 0.8606 for SUVmax and 0.86 (0.80-0.91), 0.92 (0.88-0.94), 40.49 (14.16-115.77), 0.9312 (SE=0.0177), and 0.8665 for SUVmean, respectively. The pooled sensitivity, pooled specificity, DOR, AUC, and index score of RGD PET/CT for identifying responders were 0.80 (0.59-0.93), 0.74 (0.60-0.85), 15.76 (4.33-57.32), 0.8682 (0.0539), and 0.7988, respectively, for SUVmax at baseline. Conclusion. The interesting but preliminary data in this meta-analysis demonstrate that RGD PET/CT may be an ideal diagnostic tool for detecting underlying malignancies in patients suspected of having tumors and may be able to efficiently predict short-term outcomes.

1. Introduction

Angiogenesis, the process of new blood vessel formation from preexisting vasculature, is recognized as a key mechanism involved in tumor growth, invasion, and metastasis [1]. In 1971, Judah Folkman first stressed that a tumor could not grow larger than a few millimeters in diameter without angiogenesis occurring to transport nutrients and oxygen [2, 3]. The vascular effects of antitumor therapy on microvessel density may precede the impact on tumor size by a long interval, especially when the tumor volume cannot change significantly [4].

Integrin-mediated cell adhesion plays an important role in many essential normal cellular and pathological functions [5]. Integrin αvβ3, which has been widely studied, is significantly upregulated in tumor cells and activated endothelial cells but not in resting vessel cells in normal regions [6]. Therefore, imaging integrin αvβ3 expression may be valuable for diagnosis and assessing suitable patients for particular treatments. Imaging of various tripeptide RGD sequence-containing integrins has been extensively evaluated because of their high affinity and specificity with regard to integrin αvβ3. Compared to other methods of imaging αvβ3, the PET/CT approach is likely to be widely applied in tumor patients because of its high sensitivity to low amounts of tracer and its unlimited depth penetration [7]. Indeed, there have been great efforts to develop radiolabeled RGD peptides, and ¹⁸F, as a radioisotope for labeling peptides, is commonly used because its half-life is suitable for routine clinical use [8]. In addition, ¹⁸F-Galacto-RGD [9–14], ¹⁸F-Fluciclatide-RGD [15, 16], ⁶⁸Ga-NOTA-PRGD2 [17], ¹⁸F-Alfatide [18], ¹⁸F-FPPRGD2 [19], and ¹⁸F-Alfatide II [20, 21] have been under clinical investigation for their relevance to the diagnostic ability of RGD PET/CT, but only sensitivity has been studied to date. Among them, ¹⁸F-Alfatide II and ⁶⁸Ga-NOTA-PRGD2 show advantages over the others in terms of easy preparation, fast labeling, and in vivo pharmacokinetics [22]. The results of the majority of the studies have demonstrated positive potential, though some studies have had small sample sizes. To further evaluate the diagnostic ability of RGD PET/CT, we conducted a meta-analysis of the clinical literature to obtain data regarding the sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR).

Even when treated by the standard therapy of concurrent chemoradiotherapy (CCRT), one-third of patients with advanced non-small cell lung cancer (NSCLC) still experience local treatment failure [23]. Thus, finding an effective prediction tool to select patients likely to respond to therapy would help guide individualized treatment. Although morphologic treatment effects cannot be detected by conventional imaging techniques at early time points but only after several weeks or months [24, 25], imaging tumor angiogenesis may be used for patient risk stratification before starting therapy [22]. Therefore, we performed a meta-analysis to evaluate the role of RGD PET/CT in predicting the short-term outcomes of therapy among NSCLC patients.

2. Methods

2.1. Literature Search Strategy

All relevant articles were retrieved from the PubMed (Medline), EMBASE, Cochrane Library, and Web of Science databases. The databases were searched based on the following keywords and text words: (“RGD” OR “alfatide”) and (“PET” OR “positron emission tomography”) and (“neoplasms” OR “tumor” OR “cancer” OR “neoplasm” OR “tumour”). The search was last updated on July 28th, 2018. There were no beginning date restrictions.

2.2. Inclusion and Exclusion Criteria

Published articles were selected according to the following inclusion criteria: (1) original articles revealing the performance of RGD PET or PET/CT for the diagnosis of neoplasms or predicting the efficacy of treatment; (2) studies in which the final diagnoses of patients were confirmed by histopathology or comprehensive assessment containing clinical and radiologic follow-up; (3) studies in which RGD PET or PET/CT was used as the single reference standard for neoplasm diagnosis; (4) studies in which the short-term efficacy of cancer therapy was based on tumor regression grading (TRG), response evaluation criteria in solid tumors (RECIST), or changes in the volumes of residual lesions on MRI; (5) studies in which RGD PET or PET/CT was performed at baseline or during therapy for the prediction of a curative effect; (6) articles with sufficient data to acquire true positive (TP), false positive (FP), false negative (FN), and true negative (TN) rates; (7) articles published in English. The exclusion criteria included the following: (1) duplicate studies; (2) letters, case reports, reviews, comments, and meeting abstracts; (3) laboratory studies, animal studies, or studies unrelated to the diagnosis or prediction of cancer; (4) studies with patients who had been treated before for diagnostic purposes; (5) studies that were not related to the prediction of short-term outcomes due to inconsistent long-term outcomes and too few studies.

2.3. Data Extraction

The following data were extracted from the studies: basic information of the studies (names of the first authors, country of origin, year of publication, and study design), population characteristics (number of subjects or lesions, sex distribution, and age distribution), technical aspects (imaging methods, parameters, or PET technique), the effect index (the TP, FP, FN, and TN rates for the PET imaging), and methods of tumor determination or response criteria.

2.4. Quality Assessment of Included Studies

Two reviewers independently assessed the quality of the included studies. Studies testing the diagnostic value of RDG PET or PET/CT were assessed by QUADAS-2. The scale consists of the following four domains: patient selection, index test, reference standard and flow, and timing. Each section includes assessment of the risk of bias (“low”, “high”, or “unclear”) and the applicability of diagnostic accuracy [29]. Studies investigating the predictive value of RDG PET or PET/CT were evaluated using the Newcastle-Ottawa Scale (NOS). The scale involves the following three items: subject selection criteria, comparability of subjects, and outcomes. High-quality articles have total scores of more than 5 points, with a maximum total score of 9 points [30].

2.5. Statistical Analysis

Pooled estimates of the sensitivity, specificity, and DOR with 95% confidence intervals (CIs) were determined based on bivariate analysis of patients or lesions. We evaluated heterogeneity among the studies through the likelihood ratio I² index. We assigned categories of low, moderate, and high heterogeneity to I² values of 25%, 50%, and 75% [31]. The DerSimonian-Laird method (random effect model) was applied for the meta-analysis if heterogeneity existed; otherwise, the Mantel-Haenszel method (fixed effects model) was employed. Summary receiver operating characteristic (SROC) curves with the AUC and the estimate were obtained. We analyzed the diagnostic abilities of the four parameters and heterogeneity by metaregression. Publication biases were assessed by Deek’s funnel plots. The main analyses were performed in Meta-Disc 1.4.

3. Results

3.1. Search Results

Using a previously established retrieval strategy, we initially identified 1310 relevant papers from the databases, as follows: 461 in EMBASE, 299 in PubMed, 3 in the Cochrane Library, and 547 in Web of Science. In total, 1216 manuscripts were excluded for the following reasons: 346 were duplicates; 269 were reviews, case reports, or meeting abstracts; and 601 were about basic experiments or animal experiments. The remaining 94 articles were subjected to further full-text assessment. After careful reading, 85 of the papers were excluded for the following reasons: 73 were unrelated to the diagnostic or predictive value of RGD PET/CT; 10 lacked sufficient data to acquire or calculate TP, FP, FN, and TN rates; and 2 had endpoints that were not short-term responses. Ultimately, 9 studies were included. Six articles with 9 sets of data and a total of 216 patients were eligible for inclusion to assess the diagnostic value of RGD PET/CT, and 3 articles with 75 patients were suitable for evaluating the predictive value of RGD PET/CT (Figure 1) [8, 26–28, 32–35].

Figure 1: Flow diagram of the selection process of eligible studies.

3.2. Characteristics of the Included Studies and Quality Assessment

The main characteristics of the studies are shown in Table 1. Three of the selected articles each diagnosed both the primary tumor and the lymph node metastasis status of patients with lung cancer, one of which also examined the diagnostic potential of ⁶⁸Ga-Alfatide II PET/CT in differentiating between NSCLC and tuberculosis patients. Six studies used different RGD radioligands. We selected the number of lesions or metastases for analysis rather than the number of patients when both were counted in the studies. Several parameters (, , tumor-to-normal tissue ratios, and visual analysis) were used as diagnostic parameters. Eight sets of data were selected for , five sets of data for , three sets of data for visual analysis, and only one set of data for tumor/blood ratios (T/N). Tumor lesions or metastases were all finally diagnosed by histopathology. The patient-based diagnostic parameters of RGD PET/CT for the studies are shown in Table 2. The diagnostic sensitivity ranged from 75.00% to 100%, and the diagnostic specificity ranged from 44.44% to 100%.

Table 1: Main characteristics of five studies for diagnosis of tumor included in this meta-analysis.

Number
Study
Country
Year
Numbers of patients  
 (lesions)
Neoplasm
Sex  
 (M/F)
Mean age
Imaging
Study design
PET technique
Confirmation

1
Andrei Iagaru
America
2014
8 (30)
Assessable breast cancer lesions
0/8
54.3±8.8
¹⁸F-FPPRGD2 PET/CT
Prospective
329.3MBq
60min
Histopathology

2
Song Gao
China
2015
26
Assessable lung cancer lesions
15/11
61.62±7.98
¹⁸F-alfatide RGD PET/CT
Prospective
213.34±29.8MBq 60min
Histopathology

3
16 (152)
Assessable lymph nodes
–
–

4
Fei Kang
China
2015
34
Identify NSCLC from lung tuberculosis
19/15
42.4±15.6
⁶⁸Ga-Alfatide II RGD PET/CT
Prospective
1.85 MBq/kg 60min
Histopathology

5
17
Assessable lymph nodes
–
–

6
Kun Zheng
China
2015
91
Suspected lung lesions
48/43
56.5 ± 14.9
⁶⁸Ga-NOTA-PRGD2 PET/CT
Prospective
111MBq
30min
Histopathology and follow-up

7
159
Assessable lymph nodes
–
–

8
Yue Zhou
China
2017
13(196)
Assessable lymph nodes
6/7
57±12
¹⁸F-alfatide RGD PET/CT
Prospective
212.15±30.8MBq
60min
Histopathology

9
Jiang Wu
China
2018
44(53)
Assessable breast cancer lesions
0/44
50.73±8.01
¹⁸F-Alfatide II RGD PET/CT
Prospective
306 ± 80MBq
60min
Histopathology

M/F: the ratio of male to female.

Table 2: Results of RGD PET/CT for four parameters in the diagnosis of suspected carcinoma.

Study
Year
Imaging
SUVmax
SUVmean
T/N
Visual

TP 
 (Sen)
FP
FN
TN 
 (Spe)
TP 
 (Sen)
FP
FN
TN 
 (Spe)
TP 
 (Sen)
FP
FN
TN 
 (Spe)
TP 
 (Sen)
FP
FN
TN 
 (Spe)

Andrei Iagaru
2014
¹⁸F-FPPRGD2 PET/CT
22
(95.7%)
0
1
7
(100%)

Song Gao₁
2015
¹⁸F-alfatide RGD PET/CT
17
(100%)
5
0
4
(44.44%)

Song Gao₂
2015
¹⁸F-alfatide RGD PET/CT

13 (92.86%)
6
1
132 (95.65%)

Fei Kang₁
2015
⁶⁸Ga-Alfatide II RGD PET/CT
16
(76.9%)
1
5
12
(90.48%)
18 (84.62%)
3
3
10
(76.19%)

18
(85.71%)
2
3
11
(84.62%)

Fei Kang₂
2015
⁶⁸Ga-Alfatide II RGD PET/CT
6
(75%)
0
2
9
(100%)

Kun Zheng₁
2015
⁶⁸Ga-NOTA-PRGD2 PET/CT
55
(80.9%)
4
13
19
(82.6%)
57 (83.8%)
2
11
21
(91.3%)

Kun Zheng₂
2015
⁶⁸Ga-NOTA-PRGD2 PET/CT
27
(77.14%)
3
8
121
(97.58%)

Yue Zhou
2017
¹⁸F-alfatide RGD PET/CT
18
(90%)
7
2
169
(96%)
17 (85%)
14
3
162
(92.1%)
17
(85%)
7
3
169
(96%)
20
(100%)
9
0
167
(94.9%)

Jiang Wu
2018
¹⁸F-alfatide II RGD PET/CT
37 (88.1%)
5
5
6
(54.5%)
37 (88.1%)
5
5
6
(54.5%)

39
(92.9%)
4
3
7
(63.6%)

Subscript 1: the set of data for the diagnosis of carcinoma in situ. Subscript 2: the set of data for the diagnosis of metastasis. Sen: sensitivity; Spe: specificity. TP: true-positive, FP: false-positive, FN: false-negative, and TN: true-negative.

The main characteristics of the three studies that investigated the prediction of short-term effects are presented in Table 3. These studies enrolled patients with locally advanced rectal cancer, GBM after surgical resection, and advanced NSCLC. The parameter was SUVmax before CCRT. Xiaohui Luan et al. investigated the predictive value of multiple parameters, including SUVpeak and T/N. Overall, short-term response criteria differed according to tumor type. The predictive parameters of RGD PET/CT from the studies are listed in Table 4. The prediction sensitivity ranged from 73% to 100%, and the prediction specificity ranged from 66.7% to 88.9%.

Table 3: Main characteristics of three studies for prediction of short-term outcome included in this meta-analysis.

Study
Country
Year
Number of Patients
Neoplasm
Sex 
 (M/F)
Mean Age
Imaging at Baseline
Study Design
Treatment
Response Criteria

Nadia Withofs [26]
Belgium
2015
32
Locally advanced rectal cancer
23/9
63 ± 8
¹⁸F-FPRGD2 PET/CT
Prospective
CCRT
TRG
(0 VS. 1-3)

Hui Zhang [27]
China
2015
25
GBM after surgical resection
15/10
49.5 ± 19.5
¹⁸F-alfatide RGD PET/CT
Prospective
CCRT
△ 
(cutoff=58%)

Xiaohui Luan [28]
China
2016
18
Advanced NSCLC
14/4
62 ± 12.04
¹⁸F-alfatide RGD PET/CT
Prospective
CCRT
RECIST
(CR, PR VS. SD, PD)

△: the change of volume on MRI from baseline (T1) to the eleventh week (T3) after the start of CCRT. M/F: the ratio of male to female.

Table 4: Result of RGD PET/CT for SUVmax in the prediction of short-term outcomes.

Study
Year
Imaging at Baseline
TP (Sen)
FP
FN
TN (Spe)

Nadia Withofs
2015
¹⁸F-FPRGD2 PET/CT
5
(100%)
9
0
18
(66.7%)

Hui Zhang
2015
¹⁸F-alfatide RGD PET/CT
8
(72.7%)
3
3
11
(78.57%)

Xiaohui Luan
2016
¹⁸F-alfatide RGD PET/CT
7
(77.8%)
1
2
8
(88.9%)

Sen: sensitivity; Spe: specificity. TP: true-positive, FP: false-positive, FN: false-negative, and TN: true-negative.

The quality of the studies that investigated diagnostic potential was assessed by QUADAS-2, and these studies were determined to have a low risk of bias (Figure 2). The quality of the studies that investigated the prediction of short-term outcomes was assessed by the NOS, with scores ranging from 7 to 8; this result indicates that the studies were of high quality (Table 5). There was no overlap among the cohorts enrolled in the articles. In addition, all the included studies were prospective.

Table 5: The Newcastle-Ottawa Scale was used to assess the quality of the studies for prediction.

Selection
Comparability
Outcome
Quality score

Study
Representativeness of the exposed cohort
Selection of the nonexposed cohort
Ascertainment of exposure
Demonstration that outcome of interest was not present at start of study
Comparability of cohorts on the basis of the design or analysis
Assessment of outcome
Was follow-up long enough for outcomes to occur
Adequacy of follow-up of cohorts

Nadia Withofs et al

8

Hui Zhang et al

7

Xiaohui Luan et al

7

means the score in the term.

Figure 2: Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the quality of the studies for diagnostics.

3.3. Heterogeneity and Threshold Effect Assessment

Heterogeneity among the studies was determined using the Chi-square test. There was clear heterogeneity in the sensitivity and specificity of the parameters for diagnosis, but there was no significant heterogeneity for the predictive value. Furthermore, there was no threshold effect among the studies (Table 6). Thus, the DerSimonian-Laird method (random effects model) was adopted to estimate pooled data for the diagnostic value, and the Mantel-Haenszel method (fixed effects model) was used for the predictive value.

Table 6: Chi-square test was used to assess heterogeneity among the included studies and Spearman correlation was used to assess the threshold effect among the studies.

Study
Parameter
Sensitivity
Specificity
Spearman correlation

p
I²
Heterogeneity

p
I²
Heterogeneity
Coefficient
P

Diagnostic value
SUVmax
13.00
0.0722
46.1%
Moderate
40.34
0.0000
82.6%
High
0.405
0.320

SUVmean
1.08
0.8970
0.00%
Low
16.81
0.0021
76.2%
High
-0.100
0.873

Predictive value
SUVmax
2.59
0.2733
22.90%
Low
2.11
0.3488
5.10%
Low
0.500
0.667

: Chi-square, I²: inconsistency (I-sequence); p: p value.

3.4. Diagnostic Performance and Heterogeneity Analysis

The pooled sensitivity, specificity, DOR, and SROC for SUVmax and SUVmean in diagnosing a primary tumor and lymph node metastasis are shown in Figures 3 and 4. This meta-analysis revealed a pooled sensitivity of 0.85 (0.79-0.89), specificity of 0.93 (0.90-0.96), and DOR of 48.35 (18.95-123.33) for SUVmax and a sensitivity of 0.86 (0.80-0.91), specificity of 0.92 (0.88-0.94), and DOR of 40.49 (14.16-115.77) for SUVmean for RGD PET/CT in patients with suspected tumors. Based on the SROC value of these parameters, we observed the AUC and Q of SUVmax to be greater than those of SUVmean (AUC=0.9262 SE=0.0216, Q=0.8606; AUC=0.9312 SE=0.9312, Q=0.9312). We then compared the diagnostic abilities of these four parameters, namely, SUVmax, SUVmean, T/N, and visual analysis, via regression analysis. The relative diagnostic odds ratio (RDOR) determined by meta-regression analysis was 0.79 (0.38-1.61), and there was no significant difference among the diagnostic abilities of the four parameters (P=0.4814).

Figure 3: Forest plot of the sensitivity, specificity, and diagnostic OR (DOR) of RGD PET/CT for parameters (A1, A2, A3: sensitivity, specificity, and DOR for SUVmax; B1, B2, B3: sensitivity, specificity, and DOR for SUVmean) for the diagnosis of suspected carcinoma. Circle: likelihood ratios of individual studies. Diamond: pooled likelihood ratios of all five enrolled studies. Subscript 1: the set of data for the diagnosis of carcinoma in situ. Subscript 2: the set of data for the diagnosis of metastasis.

(a)

(b)

Figure 4: Summary receiver operating characteristic (SROC) curves of RGD PET/CT for parameters ((a): SROC curves for SUVmax; (b): SROC curves for SUVmean) for the diagnosis of suspected carcinoma. Circle: likelihood ratios of individual studies. The middle blue lines are the SROC curves, and the adjacent two lines are 95% confidence intervals. AUC: area under the ROC curve. SE: standard error. Q: Q index.

3.5. Subgroup Analysis of Diagnostic Performance

To obtain further insight, we analyzed heterogeneity with regard to lesion location, RGD radioligand, and tumor type for SUVmax and for lesion location and RGD radioligand for SUVmean. However, no factors were found to clearly affect heterogeneity (Table 7).

Table 7: Subgroup analysis for SUVmax and SUVmean for the diagnostic value of RGD PET/CT.

SUVmax
SUVmean

Factors
Primary or metastatic lesions
RGD radioligands
Tumor types
RGD radioligands
Primary or metastatic lesions

P-value
0.1612
0.1214
0.8209
0.2018
0.1882

3.6. Predictive Performance

The pooled sensitivity, specificity, and DOR of SUVmax in the prediction of tumors were 0.80, 0.74, and 15.76 (4.33-57.32), respectively (Figure 5). The AUC and Q of SUVmax were 0.8682 and 0.7988, respectively.

(a)

(b)

(c)

(d)

Figure 5: Forest plot of sensitivity, specificity, and DOR and the SROC curve of SUVmax at baseline of RGD PET for the prediction of short-term outcomes after treatment. Circle: likelihood ratios of individual studies. Diamond: pooled likelihood ratios of all three enrolled studies. The middle blue line is the SROC curve, and the adjacent two lines are 95% confidence intervals. AUC: area under the ROC curve. Circle: likelihood ratios of individual studies. Diamond: pooled likelihood ratios of all enrolled studies. The middle blue lines are the SROC curves, and the adjacent two lines are 95% confidence intervals. AUC: area under the ROC curve. SE: standard error. Q: Q index.

3.7. Publication Bias

Due to the small number of studies that investigated other parameters, only studies involving SUVmax and SUVmean were tested for publication bias. Deek’s funnel plots indicated no significant publication bias (P=0.474 for SUVmax and P=0.603 for SUVmean). We did not analyze publication bias for predictive studies because the number of included studies was relatively small.

4. Discussion

This is the first meta-analysis to evaluate the diagnostic performance of RGD PET/CT in the detection of underlying malignancy and to assess the ability of RGD PET/CT to predict the short-term effects of treatment. The tumor diagnostic ability for SUVmax was slightly superior to that of SUVmean, though there was no significant difference. There was considerable heterogeneity in SUVmax and SUVmean in the literature, and we have not yet reached a consensus regarding the factors influencing the diagnostic value of RGD PET/CT because of the limited number of studies included in the meta-analysis. Overall, the different receptor binding affinities and receptor-binding kinetics of different radiotracers may be an important factor. The inclusion of different tumor types in this meta-analysis may be another important reason for the observed inter-tumor heterogeneity; αvβ3 expression level and tumor angiogenesis vary among tumor types. Meta-analysis of the use of the same RGD PET for the diagnosis of the same type of tumor is more persuasive [36].

As the receptor binding affinity and tumor retention of RGD radioligands improve, multiple types of RGD peptides are being used in clinical studies; ¹⁸F-Galacto-RGD and ¹⁸F-Fluciclatide were the first two such compounds. These two compounds have similar distribution characteristics, including heterogeneous tumor uptake in monitoring sensitivity for primary and metastatic foci. The sensitivity for evaluating primary lesions ranges from 83% to 100%, though the sensitivity of ¹⁸F-Galacto-RGD is only 33-54% for metastatic lymph nodes (LNs) and 46-78% for distant metastases [9–11, 13, 14, 16, 37]. Although the sensitivity of ¹⁸F-Fluciclatide was relatively better, reaching 88-94% for all lesions and 71-88% for metastases [15, 16], the moderate level of sensitivity for metastases is insufficient for tumor staging. As a result, to enhance the performance of integrin imaging, multimeric RGD peptides with increased receptor binding affinity and tumor retention have been used in clinical studies [38, 39]. The results showed that ¹⁸F-Alfatide PET/CT and ¹⁸F-FPPRGD2 PET/CT are able to clearly identify all primary lesions (100% sensitivity) [18, 19]; however, fewer primary lesions (83.8%) can be distinguished with ⁶⁸Ga-NOTA-PRGD2 PET/CT [17]. We comprehensively compared the diagnostic ability of ¹⁸F-Alfatide II RGD PET/CT and ⁶⁸Ga-NOTA-PRGD2 PET/CT using data from studies by Fei Kang and Kun Zheng. Although the detection rates for primary lesions and metastatic LNs were similar (76.92% versus 75% for primary lesions and metastatic LNs, respectively, in the study by Fei Kang; 80.88% versus 77.14% in the study by Kun Zheng), the specificity for metastatic LNs was slightly inferior to that for primary lesions (92.31% versus 100% in the study by Fei Kang; 82.61% versus 97.58% in the study by Kun Zheng, P>0.05) [33, 34]. ¹⁸F-Alfatide II PET/CT has been performed for diagnosing bone metastases [20] and brain metastases [21], and among different types of metastases, ¹⁸F-Alfatide II PET/CT demonstrates excellent diagnostic sensitivity for osteolytic metastases (100%), mixed bone metastases (100%), and mixed bone metastases (98%) but moderate sensitivity for osteoblastic metastases (70%). Regarding brain metastases, all 20 lesions from patients were identified by ¹⁸F-Alfatide II PET/CT. Yue Zhou et al. demonstrated a relatively higher sensitivity of ¹⁸F-Alfatide PET/CT for detecting NSCLC (90.0%) and squamous cell carcinoma (SCC) (100%) than adenocarcinoma (AC) (83.3%). This may be due to the low affinity of ¹⁸F-Alfatide for AC [26]. The much easier radiosynthetic procedure for ¹⁸F-Alfatide and ¹⁸F-Alfatide II compared to others would facilitate large-scale clinical trials [22]. Nonetheless, the diagnostic ability of RGD PET/CT still requires larger sample-size clinical trials for validation.

Overall, ¹⁸F-FDG PET/CT cannot completely replace invasive staging methods because of its relatively low specificity and high uptake by inflammatory LNs [40]. Compared with the 30.21% positive predictive value of ¹⁸F-FDG PET/CT, ⁶⁸Ga-NOTA-PRGD2 PET/CT had a value of 90%[34]. FDG, as an analog of glucose, is transported into cells by glucose transporters (GLUTs) and phosphorylated by hexokinase. Similar to malignant cells, inflammatory cells exhibit increased expression of GLUT and increased affinity of GLUT toward deoxyglucose, leading to high uptake of FDG but not necessarily an increase in integrin αvβ3 [34, 41, 42]. ⁶⁸Ga-NOTA-PRGD2 PET/CT may be complementary to ¹⁸F-FDG PET/CT because of its lower sensitivity but higher specificity in the diagnosis of tumors. However, in the study by Song Gao et al., uptake by benign lesions in nine patients was heterogeneous [8]. Jiang Wu et al. also found lower specificity for ¹⁸F-Alfatide II PET/CT (54.5% for SUVmax, 63.6% for SUVmean) than ¹⁸F-FDG (81.8% for SUVmax, 81.8% for SUVmean) in differentiating between breast cancer and benign breast lesions [43]. Thus, it remains difficult to clearly distinguish inflammatory pseudotumors from malignant lesions. Angiogenesis is an early pathological change in chronic inflammation, which may be the reason for the observed low specificity [8, 44], and neovascularization may vary depending on different stages of inflammation. Although we demonstrated an excellent specificity for tumor diagnosis in our meta-analysis, we cannot rule out the possibility of a lower specificity when using RGD PET/CT, especially in the early pathological stage of chronic inflammation.

In our meta-analysis, SUVmax from RGD PET/CT before CCRT may be able to predict short-term outcomes of treatment. In a study by Xiaohui Luan et al. addressing the ability of ¹⁸F-alfatide PET/CT to identify responders, the AUC of T/N in the lung (AUC=0.944) at baseline was higher than that of SUVmax, SUVpeak, T/N in the blood, and T/N in the muscle (AUC=0.815, 0.864, 0.889, and 0.901, respectively). In another study, SUVmax (AUC=0.737) before treatment and SUVmax (AUC=0.846), T/N (AUC=0.785) during treatment were also able to predict short-term outcomes, and SUVmax during treatment had superior predictive value compared to the volumetric parameters (AUC=0.786) of MRI. As a vital process in the growth and progression of tumors, reversal of angiogenesis may occur earlier than tumor cell death; however, FDG PET cannot detect it earlier. These findings may explain why RGDPET/CT can predict tumor treatment response [28, 45, 46], though neither Nadia Withofs et al. nor Xiaohui Luan et al. were able to identify responders using ¹⁸F-FDG PET/CT [27, 35, 46].

In mouse experiments, tumors with medium and high uptake of ⁹⁹m-Tc-3P-RGD2 SPECT/CT before treatment responded well, with a greater degree of tumor response compared to tumors with low uptake levels [47]. In a pilot study with only four patients treated with bevacizumab-containing drugs (one patient with disease progression, one with a partial clinical response, and two with complete response), the patients exhibited different degrees of SUVmean decreases using ¹⁸F-FPPRGD2 PET/CT (1.6%, 7.9%, 25.2%, and 25.0%, respectively) [48]. In a study by Andrei Iagaru, patients with a 59.8% decrease in ¹⁸F-FPPRGD2 uptake had no recurrent GBM, whereas patients with a 4.8% decrease had recurrent disease [19]. RGD PET/CT may have potential for early prediction of the response to antiangiogenesis therapy, though these preliminary findings should be confirmed in larger studies.

There are limitations in this meta-analysis. First, expression of αvβ3 varies in different types of tumors, which may affect the capability of RGD PET diagnostic and prediction. Second, the receptor binding affinity and receptor-binding kinetics of different radiotracers vary, which may also affect results. These limitations are all due to the limited number of studies. Therefore, more research with large sample sizes is urgently needed.

5. Conclusion

The interesting but preliminary data of this meta-analysis demonstrate that RGD PET/CT may be an ideal diagnostic method for detecting underlying malignancies in patients suspected of having tumors and may be able to predict short-term treatment outcomes. It is necessary to conduct large-scale clinical trials for RGD PET/CT to further study its diagnostic ability and predictive value for short-term outcomes.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent

Informed consent does not apply as this is a review manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Acknowledgments

This article is supported by the National Natural Science Foundation of China (81472812, 81472810) and the Science and Technology Development Plans of Shandong Province (2014GSF118138) and General Program of Shandong Medicine and Technology Development Plan (2014WS0058).

References

P. Carmeliet, “Angiogenesis in health and disease,” Nature Medicine, vol. 9, no. 6, pp. 653–660, 2003.

R. K. Jain, “Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy,” Science, vol. 307, no. 5706, pp. 58–62, 2005.

S. Matsumoto, S. Batra, K. Saito et al., “Antiangiogenic agent sunitinib transiently increases tumor oxygenation and suppresses cycling hypoxia,” Cancer Research, vol. 71, no. 20, pp. 6350–6359, 2011.

N. Schmidt, V. Hess, T. Zumbrunn, C. Rothermundt, G. Bongartz, and S. Potthast, “Choi response criteria for prediction of survival in patients with metastatic renal cell carcinoma treated with anti-angiogenic therapies,” European Radiology, vol. 23, no. 3, pp. 632–639, 2013.

G. Niu and X. Chen, “Why integrin as a primary target for imaging and therapy,” Theranostics, vol. 1, pp. 30–47, 2011.

F. Danhier, A. L. Breton, and V. Préat, “RGD-based strategies to target alpha(v) beta(3) integrin in cancer therapy and diagnosis,” Molecular Pharmaceutics, vol. 9, no. 11, pp. 2961–2973, 2012.

A. J. Beer and M. Schwaiger, “PET Imaging of αvβ3 Expression in Cancer Patients,” in Molecular Imaging, vol. 680 of Methods in Molecular Biology, pp. 183–200, Humana Press, Totowa, NJ, 2011.

S. Gao, N. Liu, W. Li et al., “A Pilot Study on Imaging of Integrin αvβ3 With RGD PET/CT in Suspected Lung Cancer Patients,” International Journal of Radiation Oncology & Biology & Physics, vol. 90, no. 1, pp. S648–S649, 2014.

R. Haubner, W. A. Weber, A. J. Beer et al., “Noninvasive visualization of the activated αvβ3 integrin in cancer patients by positron emission tomography and [¹⁸F]Galacto-RGD,” PLoS Medicine, vol. 2, article e70, 2005.

A. J. Beer, R. Haubner, M. Sarbia et al., “Positron emission tomography using [¹⁸F]Galacto-RGD identifies the level of integrin αvβ₃ expression in man,” Clinical Cancer Research, vol. 12, no. 13, pp. 3942–3949, 2006.

A. J. Beer, A.-L. Grosu, J. Carlsen et al., “[18F]Galacto-RGD positron emission tomography for imaging of αvβ3 expression on the neovasculature in patients with squamous cell carcinoma of the head and neck,” Clinical Cancer Research, vol. 13, no. 22, pp. 6610–6616, 2007.

A. J. Beer, S. Lorenzen, S. Metz et al., “Comparison of integrin αvβ3 expression and glucose metabolism in primary and metastatic lesions in cancer patients: A PET study using 18F-galacto-RGD and 18F-FDG,” Journal of Nuclear Medicine, vol. 49, no. 1, pp. 22–29, 2008.

O. Schnell, B. Krebs, J. Carlsen et al., “Imaging of integrin αvβ3 expression in patients with malignant glioma by [18F] Galacto-RGD positron emission tomography,” Neuro-Oncology, vol. 11, no. 6, pp. 861–870, 2009.

A. J. Beer, M. Niemeyer, J. Carlsen et al., “Patterns of αvβ3 expression in primary and metastatic human breast cancer as shown by 18F-galacto-RGD PET,” Journal of Nuclear Medicine, vol. 49, no. 2, pp. 255–259, 2008.

L. M. Kenny, R. C. Coombes, I. Oulie et al., “Phase I trial of the positron-emitting Arg-Gly-Asp (RGD) peptide radioligand 18F-AH111585 in breast cancer patients,” Journal of Nuclear Medicine, vol. 49, no. 6, pp. 879–886, 2008.

E. Mena, R. Owenius, B. Turkbey et al., “[¹⁸F]Fluciclatide in the in vivo evaluation of human melanoma and renal tumors expressing αvβ3 and αvβ5 integrins,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 41, no. 10, pp. 1879–1888, 2014.

D. Li, X. Zhao, L. Zhang et al., “68Ga-PRGD2 PET/CT in the evaluation of glioma: A prospective study,” Molecular Pharmaceutics, vol. 11, no. 11, pp. 3923–3929, 2014.

W. Wan, N. Guo, D. Pan et al., “First experience of ¹⁸F-alfatide in lung cancer patients using a new lyophilized kit for rapid radiofluorination,” Journal of Nuclear Medicine, vol. 54, no. 5, pp. 691–698, 2013.

A. Iagaru, C. Mosci, E. Mittra et al., “Glioblastoma multiforme recurrence: An exploratory study of 18F FPPRGD2 PET/CT,” Radiology, vol. 277, no. 2, pp. 497–506, 2015.

B. Mi, C. Yu, D. Pan et al., “Pilot prospective evaluation of 18F-alfatide II for detection of skeletal metastases,” Theranostics, vol. 5, no. 10, pp. 1115–1121, 2015.

C. Yu, D. Pan, B. Mi et al., “18 F-Alfatide II PET/CT in healthy human volunteers and patients with brain metastases,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 42, no. 13, pp. 2021–2028, 2015.

H. Chen, G. Niu, H. Wu, and X. Chen, “Clinical application of radiolabeled RGD peptides for PET imaging of integrin αvβ3,” Theranostics, vol. 6, no. 1, pp. 78–92, 2016.

A. Aupérin, C. Le Péchoux, E. Rolland et al., “Meta-analysis of concomitant versus sequential radiochemotherapy in locally advanced non - small-cell lung cancer,” Journal of Clinical Oncology, vol. 28, no. 13, pp. 2181–2190, 2010.

M. A. Rueger, M. Ameli, H. Li et al., “FLT PET for non-invasive monitoring of early response to gene therapy in experimental gliomas,” Molecular Imaging and Biology, vol. 13, no. 3, pp. 547–557, 2011.

M. C. Y. De Wit, H. G. De Bruin, W. Eijkenboom, P. A. E. Sillevis Smitt, and M. J. Van Den Bent, “Immediate post-radiotherapy changes in malignant glioma can mimic tumor progression,” Neurology, vol. 63, no. 3, pp. 535–537, 2004.

Y. Zhou, S. Gao, Y. Huang et al., “A pilot study of 18F-Alfatide PET/CT imaging for detecting lymph node metastases in patients with non-small cell lung cancer,” Scientific Reports, vol. 7, no. 1, 2017.

N. Withofs, P. Martinive, J. Vanderick et al., “[18 F]FPRGD 2 PET/CT imaging of integrin α v β 3 levels in patients with locally advanced rectal carcinoma,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 43, no. 4, pp. 654–662, 2016.

H. Zhang, N. Liu, S. Gao et al., “Can an 18F-ALF-NOTA-PRGD2 PET/CT scan predict treatment sensitivity to concurrent chemoradiotherapy in patients with newly diagnosed glioblastoma?” Journal of Nuclear Medicine, vol. 57, no. 4, pp. 524–529, 2016.

P. F. Whiting, A. W. S. Rutjes, M. E. Westwood et al., “Quadas-2: a revised tool for the quality assessment of diagnostic accuracy studies,” Annals of Internal Medicine, vol. 155, no. 8, pp. 529–536, 2011.

G. Wells, B. Shea, D. O'Connell et al., “The NewcastleOttawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis,” Applied Engineering in Agriculture 18(6, pp. 727–734, 2014.

J. P. T. Higgins, S. G. Thompson, J. J. Deeks, and D. G. Altman, “Measuring inconsistency in meta-analyses,” British Medical Journal, vol. 327, no. 7414, pp. 557–560, 2003.

A. Iagaru, C. Mosci, B. Shen et al., “18F-FPPRGD2 PET/CT: Pilot phase evaluation of breast cancer patients,” Radiology, vol. 273, no. 2, pp. 549–559, 2014.

F. Kang, S. Wang, F. Tian et al., “Comparing the diagnostic potential of 68Ga-Alfatide II and 18F-FDG in differentiating between non-small cell lung cancer and tuberculosis,” Journal of Nuclear Medicine, vol. 57, no. 5, pp. 672–677, 2016.

K. Zheng, N. Liang, J. Zhang et al., “68Ga-NOTA-PRGD2 PET/CT for integrin imaging in patients with lung cancer,” Journal of Nuclear Medicine, vol. 56, no. 12, pp. 1823–1827, 2015.

X. Luan, Y. Huang, S. Gao et al., “18F-alfatide PET/CT may predict short-term outcome of concurrent chemoradiotherapy in patients with advanced non-small cell lung cancer,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 43, no. 13, pp. 2336–2342, 2016.

F. Kang, Z. Wang, G. Li et al., “Inter-heterogeneity and intra-heterogeneity of αvβ3 in non-small cell lung cancer and small cell lung cancer patients as revealed by 68Ga-RGD2 PET imaging,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 44, no. 9, pp. 1520–1528, 2017.

M. Doss, H. C. Kolb, J. J. Zhang et al., “Biodistribution and radiation dosimetry of the integrin marker 18F-RGD-K5 determined from whole-body PET/CT in monkeys and humans,” Journal of Nuclear Medicine, vol. 53, no. 5, pp. 787–795, 2012.

G. Thumshirn, U. Hersel, S. L. Goodman, and H. Kessler, “Multimeric cyclic RGD peptides as potential tools for tumor targeting: Solid-phase peptide synthesis and chemoselective oxime ligation,” Chemistry - A European Journal, vol. 9, no. 12, pp. 2717–2725, 2003.

D. Boturyn, J. Coll, E. Garanger, M. Favrot, and P. Dumy, “Template assembled cyclopeptides as multimeric system for integrin targeting and endocytosis,” Journal of the American Chemical Society, vol. 126, no. 18, pp. 5730–5739, 2004.

I. Iskender, H. O. Kapicibasi, S. Z. Kadioglu et al., “Comparison of integrated positron emission tomography/computed tomography and mediastinoscopy in mediastinal staging of non-small cell lung cancer: Analysis of 212 patients,” Acta Chirurgica Belgica, vol. 112, no. 3, pp. 219–225, 2012.

S. Deppen, J. B. Putnam Jr., G. Andrade et al., “Accuracy of FDG-PET to diagnose lung cancer in a region of endemic granulomatous disease,” The Annals of Thoracic Surgery, vol. 92, no. 2, pp. 428–433, 2011.

K. Kobayashi, P. Bhargava, S. Raja et al., “Image-guided biopsy: What the interventional radiologist needs to know about PET/CT,” RadioGraphics, vol. 32, no. 5, pp. 1483–1501, 2012.

J. Wu, S. Wang, X. Zhang et al., “18F-Alfatide II PET/CT for Identification of Breast Cancer: A Preliminary Clinical Study,” Journal of Nuclear Medicine, vol. 59, no. 12, pp. 1809–1816, 2018.

L. Deban, C. Correale, S. Vetrano, A. Malesci, and S. Danese, “Multiple pathogenic roles of microvasculature in inflammatory bowel disease: A jack of all trades,” The American Journal of Pathology, vol. 172, no. 6, pp. 1457–1466, 2008.

J. R. Silber, M. S. Bobola, A. Blank, and M. C. Chamberlain, “O6-Methylguanine-DNA methyltransferase in glioma therapy: Promise and problems,” Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, vol. 1826, no. 1, pp. 71–82, 2012.

Y.-I. Kim, H.-J. Yoon, J. C. Paeng et al., “Prognostic value of 68Ga-NOTA-RGD PET/CT for predicting disease-free survival for patients with breast cancer undergoing neoadjuvant chemotherapy and surgery,” Clinical Nuclear Medicine, vol. 41, no. 8, pp. 614–620, 2016.

S. Ji, Y. Zheng, G. Shao, Y. Zhou, and S. Liu, “Integrin α(v)β3-targeted radiotracer (99m)Tc-3P-RGD2 useful for noninvasive monitoring of breast tumor response to antiangiogenic linifanib therapy but not anti-integrin α(v)β3 RGD2 therapy,” Theranostics, vol. 3, no. 11, pp. 816–830, 2013.

R. Minamimoto, A. Karam, M. Jamali et al., “Pilot prospective evaluation of 18F-FPPRGD2 PET/CT in patients with cervical and ovarian cancer,” European Journal of Nuclear Medicine and Molecular Imaging, vol. 43, no. 6, pp. 1047–1055, 2016.