Impact of antagonist peptides and chelators on the diagnostic performance of PET/CT using gallium-68–labeled somatostatin receptor antagonists : Journal of Pancreatology

Secondary Logo

Journal Logo

Original Article

Impact of antagonist peptides and chelators on the diagnostic performance of PET/CT using gallium-68–labeled somatostatin receptor antagonists

Xing, Haiquna; Zhu, Wenjiaa; Cheng, Yuejuanb; Yang, Qiaoa; Jia, Ruc; Zhao, Hongd; Bai, Chunmeib; Huo, Lia,*; Wu, Wenminge,*

Author Information
Journal of Pancreatology 6(1):p 28-33, March 2023. | DOI: 10.1097/JP9.0000000000000101



Neuroendocrine tumors (NETs) are featured by the overexpression of somatostatin receptors (SSTRs), which can be specifically targeted by somatostatin analogs. Since the approval of 68Ga-DOTATATE and 177Lu-DOTATATE by Food and Drug Administration, SSTR agonists have been considered the mainstay of SSAs. They have high affinities to SSTR2s and are internalized into tumor cells after interaction, thus considered as ideal analogs for SSTR2 targeting.[1]

On the other hand, somatostatin receptor antagonists are characterized by a low internalization rate and high tumor affinity.[2–5] They might have the potential to surpass agonists in terms of diagnostic and therapeutic efficacy. Recently, 4 gallium-68–labeled antagonists, 68Ga-NODAGA-JR11, 68Ga-DOTA-JR11, 68Ga-NODAGA-LM3, and 68Ga-DOTA-LM3, have been systematically compared with agonists in the imaging of well-differentiated NETs.[6–8] The results demonstrated lower background in normal organs and higher target-to-background ratios with antagonists. Overall, antagonists had better lesion detection abilities compared with agonists. Looking into the data, however, remarkable differences were noted between these antagonists. For example, both DOTA chelated antagonists, 68Ga-DOTA-JR11 and 68Ga-DOTA-LM3, showed extremely low uptake in SSTR2-positive organs including pituitary, adrenal glands, and spleen, which were different from NODAGA chelated antagonists. The tumor uptake also varied along with different antagonists. In previous studies, 68Ga-NODAGA-LM3 demonstrated significantly higher tumor uptake compared to 68Ga-DOTATATE, while 68Ga-DOTA-JR11 had significantly lower tumor accumulation. Nicolas et al reported comparable tumor uptake between 68Ga-NODAGA-JR11 and 68Ga-DOTATOC.[6]

It is essential to understand the differences and principles of these antagonists in assisting tracer optimization in clinical trials. For now, there have been no study comparing these antagonists in the same group of participants. Therefore, we designed this study to evaluate the impact of different peptides and chelators on the diagnostic performance of SSTR2 antagonists in well-differentiated NETs. 68Ga-NODAGA-JR11, 68Ga-NODAGA-LM3, and 68Ga-DOTA-LM3 were assessed in this study, excluding 68Ga-DOTA-JR11 for its low tumor uptake and poor detection of bone metastases.[7]

Materials and methods

Study design

This was a prospective single center study conducted from October 2020 to April 2021 ( identifier: NCT04491851), which was approved by the institutional review board of Peking Union Medical College Hospital. Participants with well-differentiated NETs were consecutively recruited in the study and all signed a written informed consent before study participation. The inclusion and exclusion criteria could be found in Additional Table 1, Participants were equally randomized into 2 arms: arm A, participants would undergo a whole-body 68Ga-NODAGA-LM3 PET/CT scan on the first day and 68Ga-DOTA-LM3 PET/CT scan on the second day to explore the impact of different chelators on antagonist imaging, arm B, participants would undergo a whole-body 68Ga-NODAGA-LM3 PET/CT scan on the first day and 68Ga-NODAGA-JR11 PET/CT scan on the second day to explore the impact of different peptides on antagonist imaging.

Synthesis and radiolabeling

NODAGA-LM3 and DOTA-LM3 were supplied by CS Bio Co. (Menlo Park, CA). NODAGA-JR11 was supplied by WuXi AppTec (Shanghai, China). The tracers were radiolabeled manually in a hot cell. Briefly, 5 mL 0.05 mol/L hydrochloric acid was used to elute 68GaCl3 from a 68Ge/68Ga generator (ITM, Germany). Based on an empirically non-decay corrected yields of approximately 65%, 230 to 300 MBq 68GaCl3 was added into a reaction vial containing 40 μg precursor. Sodium acetate buffer were added to bring the pH of the final reaction mixture to 4.0, followed by incubation at 100°C for 10 minutes to allow for radionuclide incorporation. After cooling to room temperature, the reaction mixture was diluted with 5 mL water and then further purified by a preconditioned Oasis HLB light cartridge. The loaded cartridge was washed with normal saline to remove free radionuclide first. Thereafter, the product was eluted off the cartridge with 0.5 mL 75% ethanol solution followed by 5 mL normal saline. Finally, the radiotracer was filtered through a 0.22 μm filter (Millipore, Merck, Germany) into a sterile product vial. The final product was composed of 150 to 200 MBq radiopharmaceutical, approximately 0.38 mL ethanol, and approximately 40 μg total peptide mass with a >95% radiochemical purity.

PET/CT imaging

The participants in arm A received an injection dose of 185 ± 30 MBq 68Ga-NODAGA-LM3 on the first day and 174 ± 35 MBq 68Ga-DOTA-LM3 on the second day. In arm B, the participants received an injection dose of 170 ± 21 MBq 68Ga-NODAGA-LM3 on the first day and 167 ± 24 MBq 68Ga-NODAGA-JR11 on the second day. The radiotracers were administered to all subjects by quick bolus injection.

The PET/CT study was carried out on a time-of-flight PET/CT scanner (Polestar m660, SinoUnion Healthcare Inc., Beijing, China). A low-dose CT scan was obtained at 60-minute post-injection and followed by a static whole body PET scan. Images were reconstructed using an ordered subsets expectation maximization algorithm (2 iterations, 10 subsets, 192 × 192 matrix) and corrected for CT-based attenuation, dead time, random events, and scatter.

PET/CT image analysis

All images were anonymized and independently reviewed on MIM software (MIM Software Inc., Cleveland, OH) by 2 experienced nuclear medicine physicians (WZ and QY) who were masked to the medical history of the participants. Any disagreement would be discussed with another senior expert (LH) till they reached agreements.

The uptake in normal organs were measured in the following organs: pituitary, parotid, thyroid, lung, blood pool, normal liver and spleen parenchyma, pancreas (uncinate process), adrenal, stomach, small intestine, kidney, and bone marrow. Regions of interest were drawn over these organs to exclude focal lesions, and the maximum standardized uptake value (SUVmax) normalized to participants’ body weight was recorded, which was defined as the point of maximal radiotracer uptake within the delineated volume (g/mL). For bilateral organs such as parotids, thyroids, lungs, and kidneys, the average SUVmax were calculated. Since the uptake of the right adrenal gland could be influenced by the adjacent liver uptake, only left adrenal gland was measured.

Any focal accumulations that could not explained by physiologic uptake were interpreted as focal lesions. All lesions were further categorized according to their sites, including primary sites, liver, bone, lymph node, and others. The diagnostic efficacy was measured by counting the number of lesions detected in each lesion site and all sites combined as well. The lesion uptake was measured as SUVmax as well as tumor-to-background ratio using blood pool, kidney, and liver as reference tissues.

Statistical analysis

Data were expressed as mean ± standard deviation (SD). The differences of biodistribution in normal organs, lesion SUVmax, and target-to-background ratios between antagonists were evaluated using paired t test (SPSS, version 26, IBM Cor., USA). The nonparametric Wilcoxon matched-pair signed-rank test was used to compare number of identified lesions. The Pearson Chi-square test and students’ t test were used to compare the participant profiles between the 2 arms. P value <.05 was considered to indicate statistical significance.



Forty participants (age, 49.5 ± 13.4, 21 men) were prospectively recruited in this study and randomly assigned to arm A (age, 52.3 ± 12.6, 11 men) and arm B (age, 46.8 ± 13.9, 10 men). There was no exclusion. All participants completed the study. The demographic and clinical characteristics are summarized in Table 1.

Table 1 - Demographic and clinical characteristics of participants
Arm A (N = 20) Arm B (N = 20) P value
Age 52.3 ± 12.6 46.8 ± 13.9 .194
Gender .752
 Male 11 10
 Female 9 10
Grade .832
 G1 4 4
 G2 15 14
 G3 1 2
 Pancreas 9 12
 Small intestine 1 3
 Rectus 6 2
 Stomach 2 0
 Ovary 1 0
 Thymus 0 1
 Unknown 1 2

Biodistribution in normal organs

In arm A, the background in normal organs were higher with 68Ga-NODAGA-LM3 compared to 68Ga-DOTA-LM3 except for thyroids, lung, and blood pool (Fig. 1, Table 2). Note the markedly higher uptake of 68Ga-NODAGA-LM3 compared to 68Ga-DOTA-LM3 in SSTR2-positive organs, such as pituitary (6.6 ± 2.5 vs 2.0 ± 0.6; P < .001), spleen (12.3 ± 4.9 vs 4.0 ± 1.5; P < .001), and adrenal glands (8.6 ± 3.2 vs 2.6 ± 1.1; P < .001).

Table 2 - SUVmax of normal organs in arms A and B
Organs Arm A P value Arm B P value
Pituitary 6.6 ± 2.5 2.0 ± 0.6 <.001 9.4 ± 3.6 8.1 ± 3.5 .001
Parotid 2.6 ± 1.2 1.7 ± 0.3 .002 2.7 ± 1.1 2.3 ± 1.0 .003
Thyroid* 2.0 ± 0.8 2.0 ± 0.6 .831 2.1 ± 0.8 2.0 ± 0.7 .293
Lung 0.8 ± 0.4 0.9 ± 0.4 .040 1.0 ± 0.3 1.0 ± 0.2 .631
Blood pool 1.4 ± 0.6 2.2 ± 0.5 <.001 1.6 ± 0.6 1.9 ± 0.5 .116
Liver 5.7 ± 2.5 4.0 ± 2.0 <.001 5.9 ± 2.2 4.8 ± 1.8 .010
Spleen† 12.3 ± 4.9 4.0 ± 1.5 <.001 17.6 ± 8.8 13.3 ± 6.4 <.001
Pancreas‡ 3.2 ± 1.7 1.8 ± 0.7 .002 4.8 ± 4.3 4.2 ± 3.2 .163
Adrenal 8.6 ± 3.2 2.6 ± 1.1 <.001 11.0 ± 5.6 9.3 ± 4.8 .008
Stomach 3.6 ± 2.2 2.0 ± 0.9 .001 3.4 ± 2.4 3.5 ± 1.8 .880
Small intestine 3.3 ± 1.1 1.9 ± 0.5 <.001 4.0 ± 1.3 3.6 ± 1.0 .052
Kidney 14.2 ± 4.2 7.0 ± 1.8 <.001 18.8 ± 4.5 15.9 ± 3.5 <.001
Bone marrow 1.6 ± 0.6 1.5 ± 0.5 .536 1.7 ± 0.4 1.8 ± 0.5 .562
*Two patients in arm A had thyroidectomy due to thyroid cancer. The thyroid uptake in arm A was evaluated in the remaining 18 patients.
†Two patients in arm A and 3 patients in arm B had distal pancreatectomy and splenectomy due to primary tumor in pancreatic tail. The spleen uptake was evaluated in the remaining 18 patients in arm A and 17 patients in arm B.
‡One patient in arm A and 1 patient in arm B had whipple surgery. Another 5 patients in arm A and 3 patients in arm B had lesions in pancreatic head or uncinate process. The pancreas uptake was evaluated in the remaining 14 patients in arm A and 16 patients in arm B.

Figure 1.:
The comparison of normal organ uptake in arms A and B. SUVmax = maximum standardized uptake value.

In arm B, 68Ga-NODAGA-LM3 showed either higher (pituitary, parotid, liver, spleen, adrenal, and kidney) or comparable (thyroid, lung, blood pool, pancreas, stomach, small intestine, and bone marrow) background compared to 68Ga-NODAGA-JR11 (Fig. 1, Table 2). Nevertheless, the biodistribution patterns were similar between these 2 tracers, as both tracers showed high uptake in SSTR2-positive organs.

Lesion detection

All 40 participants showed positive lesions in at least 1 PET/CT scan. All participants were included in the analysis. The number of lesions detected is summarized in Table 3. The participant-based analysis of arm A is shown in Figure 2. Four representative participants demonstrating better (A/E), even (B/F), and worse (C/G) lesion detection with 68Ga-NODAGA-LM3 compared to 68Ga-DOTA-LM3 as well as comparable lesion detection (D/H) between 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 are shown in Figure 3.

Table 3 - Number of lesions detected in both arms
Lesion sites Arm A P value Arm B P value
Primary 12 12 >.99 16 16 >.99
Liver 318 265 .058 307 307 >.99
Bone 110 70 .043 102 102 >.99
Lymph node 48 46 .317 27 27 >.99
Other 60 52 .317 51 51 >.99
Total 548 445 .005 503 503 >.99

Figure 2.:
Participant-based comparison of lesion detection between 68Ga-NODAGA-LM3 and 68Ga-DOTA-LM3. The number of participants with more lesions detected with 68Ga-NODAGA-LM3 or 68Ga-DOTATATE are shown on the right and left, respectively. The number of participants demonstrating comparable results are shown in the middle. Other metastatic sites included 3 participants, 1 with a solitary adrenal lesion, 1 with myocardium lesions, and 1 with peritoneal lesions.
Figure 3.:
Whole-body anterior maximum-intensity projections of 4 representative participants demonstrating better (A/E), even (B/F), and worse (C/G) lesion detection with 68Ga-NODAGA-LM3 compared to 68Ga-DOTA-LM3 shown on the left as well as comparable lesion detection (D/H) between 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 shown on the right. The first participant (A/E, age, 60 years, woman) had rectal NET (grade 2, Ki67 = 5%) with multiple liver metastases and (previously unknown) bone metastases. The primary lesion was hidden behind the urinary bladder. More liver lesions were detected using 68Ga-NODAGA-LM3 with higher uptake. There were also 2 bone lesions in the left humerus and left ilium missed on 68Ga-DOTA-LM3 scan. The second participant (B/F, age, 38 y, woman) had mid-gut NET (grade 2, Ki67 = 10%) with a solitary nodal lesion and multiple liver metastases. The third participant (C/G, age, 38 y, man) had stomach NET (grade 3, Ki67 = 25%, primary tumor resected) and liver metastases. A small liver lesion on the left lobe was missed on 68Ga-NODAGA-LM3 scan. The fourth participants (D/H, age, 27 y, man) had pancreatic NET with multiple hepatic, nodal, and bone metastases. NET = neuroendocrine tumor.

In arm A, there were 60% (12/20), 75% (15/20), 40% (8/20), 50% (10/20), and 15% (3/20) of participants showing positive primary tumor, liver metastases, bone metastases, lymph node metastases, and lesions in other sites, respectively. Fifty percent (10/20) of these participants had more lesion detected on 68Ga-NODAGA-LM3 scan, while only 1 participant had better lesion detection with 68Ga-DOTA-LM3. Overall, a total of 103 lesions were missed on 68Ga-DOTA-LM3 scan compared to 68Ga-NODAGA-LM3 (445 vs 548; P = .005).

In arm B, 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 detected the same number of lesions in all 20 patients.

Tumor uptake

There was a total of 947 matched lesions identified, 444 in arm A and 503 in arm B. The SUVmax and tumor-to-background ratio of these matched lesions are compared and summarized in Table 4 and Additional Table 2,

In arm A, 68Ga-NODAGA-LM3 showed higher SUVmax and tumor-to-blood pool than 68Ga-DOTA-LM3 in all lesion locations (P < .05). The tumor-to-liver ratios were also higher with 68Ga-NODAGA-LM3 except for primary tumors. Given the higher kidney uptake, 68Ga-NODAGA-LM3 showed comparable tumor-to-kidney ratios except for liver lesions.

In arm B, higher SUVmax was noted in primary tumors, liver and bone metastases, and all lesions detected with 68Ga-NODAGA-LM3 (P < .05). The SUVmax of lymph node and other types of metastases were comparable. The differences, however, were small.


In the present study, we prospectively compared the diagnostic performance of 3 SSTR2 antagonists, 68Ga-NODAGA-JR11, 68Ga-NODAGA-LM3, and 68Ga-DOTA-LM3 in well-differentiated NETs. The results showed that the diagnostic performance of SSTR2 antagonists was sensitive to chelators. Both 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 outperformed 68Ga-DOTA-LM3 with higher lesion uptake and detection ability, of which 68Ga-NODAGA-LM3 had marginally but significantly higher lesion uptake.

Since the development of first SSTR2 antagonist by Bass et al[9] in 1996, many comparative imaging studies have been conducted between SSTR2 antagonists and agonists. While early studies used Indium-111–labeled antagonists, more recently, Gallium-68–labeled antagonists, mainly JR11 and LM3, have been assessed by different groups.[3] Krebs et al[10] used 68Ga-DOTA-JR11 as the diagnostic pair of 177Lu-DOTA-JR11 in their clinical trial (NCT02609737). In the phase I/II imaging study by Nicolas et al,[6,11]177Lu-NODAGA-JR11 was evaluated and compared to agonist 68Ga-DOTATOC (NCT02162446). Our group has previously compared 3 different antagonists, 68Ga-DOTA-JR11, 68Ga-NODAGA-LM3, and 68Ga-DOTA-LM3 with agonist 68Ga-DOTATATE (NCT04318561).[7,8,12] With more and more data generated from these trials, the next question follows: which one is the best antagonist? Fani et al[13] found that SSTR2 antagonists were sensitive to N-terminal radiometal modifications preclinically. Substitution of DOTA by the NODAGA chelator was able to increase massively its binding affinity in contrast to the 68Ga-DOTA analog, which makes 68Ga-NODAGA the preferable chelator–radiometal coupling SSTR2 antagonists. This theory has, however, not yet been proved in clinical practice. Therefore, the present study was conducted to test this hypothesis as well as the impact of different antagonist peptides.

Based on the data from arm A, chelators were found to be a key factor influencing not only the biodistribution but also the diagnostic efficacy of antagonists. 68Ga-DOTA-LM3 demonstrated remarkable lower uptake in most organs, especially SSTR2-positive organs. This phenomenon has been previously noted in another DOTA chelated antagonist 68Ga-DOTA-JR11 and was considered a major advantage of antagonist because lower background leads to higher lesion-to-background ratio.[7,10] However, the lesion uptake of 68Ga-DOTA-LM3 was only 50% to 60% of that of 68Ga-NODAGA-LM3, which leads to lower tumor-to-background ratio. It is consistent with our previous observations that 68Ga-NODAGA-LM3 has almost a 2-fold overall SUVmax compared with 68Ga-DOTA-LM3 (median SUVmax 29.1 vs 16.1) and supports the conclusion drawn by Fani.[8,13] We believe this is also the main reason that 68Ga-DOTA-LM3 missed quite some lesions detected on 68Ga-NODAGA-LM3 scan.

In arm B, although there were some minor differences in the distribution of normal organs and tumor uptake, both antagonists share similar features. Not a single lesion was missed using either antagonist. The differences of lesion uptake were small, ranging from 3% to 14%. We did not find any clinically relevant difference between these 2 antagonists. Therefore, peptides (LM3/JR11) have very limited impact on the diagnostic performance with NODAGA chelation. This is similar to agonists that different agonists, such as 68Ga-DOTATATE and 68Ga-DOTATOC, are clinically equivalent.[14] One thing to notice is that the lesion uptake of 68Ga-NODAGA-JR11 in our study (average SUVmax: 25.0) was much higher than that measured by Nicolas et al[6] (median SUVmax: 12.3–14.4). There are several possible explanations for that. The most important one, in our opinion, is that we used a time-of-flight PET/CT scanner (Polestar m660, SinoUnion Healthcare Inc.) while they used a non-time-of-flight one (Discovery STE; GE Healthcare, USA), which could greatly affect SUV quantification.

Although not assessed in the present study, we have reasons to believe that 68Ga-DOTA-JR11 would share features with 68Ga-DOTA-LM3 as both antagonists use DOTA chelation and have similar SSTR2 affinity (IC50, 29 vs 12.5 ng/mL). 68Ga-DOTA-JR11 served as a diagnostic companion of 177Lu-DOTA-JR11 in the Phase I trial of antagonist peptide receptor radionuclide therapy.[15] The comparison of 68Ga-DOTA-JR11 PET/CT with dosimetric 177Lu-DOTA-JR11 SPECT/CT revealed a significant but only modest correlation in terms of SUVs and tumor-to-background ratios.[16] Lesions with relatively low uptake of 68Ga-DOTA-JR11 (SUVpeak ≤ 10) could still have good 177Lu-DOTA-JR11 uptake (SUVpeak ratio of 8.0). It was concluded that low tumor uptake on 68Ga-DOTA-JR11 PET should not preclude participants from treatment with 177Lu-DOTA-JR11. This conclusion, however, just proved that 68Ga-DOTA-JR11 might not be an ideal diagnostic pair of 177Lu-DOTA-JR11 though they share the common chelator. The principle of PRRT, that is, we treat what we see, requires a good correlation between diagnostic and theranostic pair. The lower SSTR2 affinity of 68Ga-DOTA-JR11 compared to 177Lu-DOTA-JR11 (IC50, 29 vs 0.73 ng/mL) may limit its role as a diagnostic pair. Given the high SSTR2 affinity and good lesion detection ability, 68Ga-NODAGA-JR11 may serve as an alternative in this clinical setting. Although the 68Ga-NODAGA-JR11/177Lu-DOTA-JR11 pair has not been tested in clinical practice, similar combination 68Ga-NODAGA-LM3/177Lu-DOTA-LM3 has been proved feasible by Baum et al[17] in their study of antagonist peptide receptor radionuclide therapy.

One of the limitations of our study was lack of gold standard. Most additional lesions detected were not confirmed histologically. Besides, our study is limited by lack of reference imaging studies, such as contrast enhanced computed tomography or magnetic resonance imaging. Hence, the sensitivity cannot be calculated. Third, our sample size was small, which might be the reason of marginally more hepatic lesion using 68Ga-NODAGA-LM3 in arm A. Finally, with more and more SSTR2 antagonists emerged combining different radiometals and chelators, such as 18F-AlF-NOTA-JR11, 99mTc-labeled SSTR2 antagonists, and 64Cu-NODAGA-JR11, whether the conclusion of our study can be extrapolated to other antagonists remains to be determined.[18–21]


Our study indicates that the diagnostic performance of SSTR2 antagonists was sensitive to chelators. Both 68Ga-NODAGA-LM3 and 68Ga-NODAGA-JR11 may outperform 68Ga-DOTA-LM3 with higher lesion uptake and detection ability, of which 68Ga-NODAGA-LM3 had marginally but significantly higher lesion uptake.

Table 4 - SUVmax of matched lesions in both arms
SUVmax Arm A P value Arm B P value
Primary 36.6 ± 20.2 22.1 ± 19.4 .002 47.4 ± 36.8 41.8 ± 33.8 .011
Liver 43.1 ± 32.7 25.6 ± 18.9 <.001 34.3 ± 25.3 29.6 ± 20.8 <.001
Bone 17.2 ± 8.2 8.8 ± 4.0 <.001 14.0 ± 8.7 12.4 ± 7.9 <.001
Lymph node 25.9 ± 17.9 11.9 ± 7.4 <.001 32.2 ± 20.4 30.3 ± 19.7 .062
Other 25.0 ± 11.3 9.3 ± 4.3 <.001 17.0 ± 10.8 16.4 ± 9.5 .146
All lesion 35.3 ± 28.8 19.8 ± 17.2 <.001 28.5 ± 23.8 25.0 ± 20.0 <.001
SUVmax = maximum standardized uptake value.



Author contributions

HX and WZ participated in the study design, writing of the paper, performance of the study and data analysis. CB participated in the study design. QY participated in the performance of the study. YC, RJ, and HZ participated in the data analysis. LH and WW are co-senior authors of this paper. All authors approved the final version of the paper.

Financial support

This work was sponsored in part by the National Natural Science Foundation of China (No. 82071967); CAMS initiative for innovative medicine (No. CAMS-2018-I2M-3-001); National Key Research and Development Program of China (No. 2016YFC0901500); Center for Rare Diseases Research, Chinese Academy of Medical Sciences, Beijing, China (No. 2016ZX310174-4).

Conflicts of interest

The authors declare no conflicts of interest.

Editor note: WW is an Editorial Board member of Journal of Pancreatology. The article was subject to the journal’s standard procedures, with peer review handled independently of these Editorial Board members and their research groups.

Ethics approval

All procedures involving human participants were carried out 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. This article does not contain any experiments with animals.


[1]. Reubi JC, Waser B, Cescato R, et al. Internalized somatostatin receptor subtype 2 in neuroendocrine tumors of octreotide-treated patients. J Clin Endocrinol Metab. 2010;95:2343–2350.
[2]. Fani M, Nicolas GP, Wild D. Somatostatin receptor antagonists for imaging and therapy. J Nucl Med. 2017;58:61S–66S.
[3]. Ginj M, Zhang H, Waser B, et al. Radiolabeled somatostatin receptor antagonists are preferable to agonists for in vivo peptide receptor targeting of tumors. Proc Natl Acad Sci USA. 2006;103:16436–16441.
[4]. Wild D, Fani M, Behe M, et al. First clinical evidence that imaging with somatostatin receptor antagonists is feasible. J Nucl Med. 2011;52:1412–1417.
[5]. Nicolas GP, Mansi R, McDougall L, et al. Biodistribution, pharmacokinetics, and dosimetry of (177)Lu-, (90)Y-, and (111)In-Labeled somatostatin receptor antagonist OPS201 in comparison to the agonist (177)Lu-DOTATATE: the mass effect. J Nucl Med. 2017;58:1435–1441.
[6]. Nicolas GP, Schreiter N, Kaul F, et al. Sensitivity comparison of (68)Ga-OPS202 and (68)Ga-DOTATOC PET/CT in patients with gastroenteropancreatic neuroendocrine tumors: a prospective phase II imaging study. J Nucl Med. 2018;59:915–921.
[7]. Zhu W, Cheng Y, Wang X, et al. Head-to-head comparison of (68)Ga-DOTA-JR11 and (68)Ga-DOTATATE PET/CT in patients with metastatic, well-differentiated neuroendocrine tumors: a prospective study. J Nucl Med. 2020;61:897–903.
[8]. Zhu W, Jia R, Yang Q, et al. A prospective randomized, double-blind study to evaluate the diagnostic efficacy of (68)Ga-NODAGA-LM3 and (68)Ga-DOTA-LM3 in patients with well-differentiated neuroendocrine tumors: compared with (68)Ga-DOTATATE. Eur J Nucl Med Mol Imaging. 2021;49:1613–1622.
[9]. Bass RT, Buckwalter BL, Patel BP, et al. Identification and characterization of novel somatostatin antagonists. Mol Pharmacol. 1996;50:709–715.
[10]. Krebs S, Pandit-Taskar N, Reidy D, et al. Biodistribution and radiation dose estimates for (68)Ga-DOTA-JR11 in patients with metastatic neuroendocrine tumors. Eur J Nucl Med Mol Imaging. 2019;46:677–685.
[11]. Nicolas GP, Beykan S, Bouterfa H, et al. Safety, biodistribution, and radiation dosimetry of (68)Ga-OPS202 in patients with gastroenteropancreatic neuroendocrine tumors: a prospective phase I imaging study. J Nucl Med. 2018;59:909–914.
[12]. Zhu W, Cheng Y, Jia R, et al. A prospective, randomized, double-blind study to evaluate the safety, biodistribution, and dosimetry of (68)Ga-NODAGA-LM3 and (68)Ga-DOTA-LM3 in patients with well-differentiated neuroendocrine tumors. J Nucl Med. 2021;62:1398–1405.
[13]. Fani M, Braun F, Waser B, et al. Unexpected sensitivity of sst2 antagonists to N-terminal radiometal modifications. J Nucl Med. 2012;53:1481–1489.
[14]. Velikyan I, Sundin A, Sorensen J, et al. Quantitative and qualitative intrapatient comparison of 68Ga-DOTATOC and 68Ga-DOTATATE: net uptake rate for accurate quantification. J Nucl Med. 2014;55:204–210.
[15]. Reidy-Lagunes D, Pandit-Taskar N, O’Donoghue JA, et al. Phase I trial of well-differentiated neuroendocrine tumors (NETs) with radiolabeled somatostatin antagonist 177 Lu-Satoreotide Tetraxetan. Clin Cancer Res. 2019;25:6939–6947.
[16]. Krebs S, O’Donoghue JA, Biegel E, et al. Comparison of (68)Ga-DOTA-JR11 PET/CT with dosimetric (177)Lu-satoreotide tetraxetan ((177)Lu-DOTA-JR11) SPECT/CT in patients with metastatic neuroendocrine tumors undergoing peptide receptor radionuclide therapy. Eur J Nucl Med Mol Imaging. 2020;47:3047–3057.
[17]. Baum RP, Zhang J, Schuchardt C, et al. First-in-human study of novel SSTR antagonist (177)Lu-DOTA-LM3 for peptide receptor radionuclide therapy in patients with metastatic neuroendocrine neoplasms: dosimetry, safety and efficacy. J Nucl Med. 2021;62:1571–1581.
[18]. Xie Q, Liu T, Ding J, et al. Synthesis, preclinical evaluation, and a pilot clinical imaging study of [(18)F]AlF-NOTA-JR11 for neuroendocrine neoplasms compared with [(68)Ga]Ga-DOTA-TATE. Eur J Nucl Med Mol Imaging. 2021;48:3129–3140.
[19]. Gaonkar RH, Wiesmann F, Del Pozzo L, et al. SPECT imaging of SST2-expressing tumors with (99m)Tc-based somatostatin receptor antagonists: the role of tetraamine, HYNIC, and spacers. Pharmaceuticals (Basel). 2021;14:300.
[20]. Fani M, Weingaertner V, Kolenc Peitl P, et al. Selection of the first (99m)Tc-Labelled somatostatin receptor subtype 2 antagonist for clinical translation-preclinical assessment of two optimized candidates. Pharmaceuticals (Basel). 2020;14:19.
[21]. Rylova SN, Stoykow C, Del Pozzo L, et al. The somatostatin receptor 2 antagonist 64Cu-NODAGA-JR11 outperforms 64Cu-DOTA-TATE in a mouse xenograft model. PLoS One. 2018;13:e0195802.

68Ga-DOTA-LM3; 68Ga-NODAGA-JR11; 68Ga-NODAGA-LM3; Neuroendocrine tumor; Somatostatin receptor antagonist

Supplemental Digital Content

Copyright © 2022 The Chinese Medical Association, Published by Wolters Kluwer Health, Inc.