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

Abstract

Objective: 

Different SSTR2 antagonists have been developed. This study aims to evaluate the impact of different peptides and chelators on the diagnostic performance of SSTR2 antagonists in well-differentiated NETs.

Methods: 

In this prospective study, 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; 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. Biodistribution in normal organs, lesion detection ability, and tumor uptakes were compared within each arm.

Results: 

A total of 40 participants (age, 49.5 ± 13.4, 21 men), 20 in each arm, were recruited in the study. In arm A, ⁶⁸Ga-DOTA-LM3 showed lower background. However, the lesion detection ability (overall lesion detected, 445 vs 548; P = .005) and the lesion uptake (overall lesions SUVmax, 19.8 ± 17.2 vs 35.3 ± 28.8; P < .001) was significantly lower than those of ⁶⁸Ga-NODAGA-LM3. In arm B, both ⁶⁸Ga-NODAGA-LM3 and ⁶⁸Ga-NODAGA-JR11 showed similar biodistribution and lesion uptake (SUVmax, 28.5 ± 23.8 vs 25.0 ± 20.0; P < .001) despite minor differences. The lesion detection ability was the same between these 2 tracers (overall lesion detected, 503 vs 503).

Conclusions: 

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.

Introduction

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 (ClinicalTrials.gov 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, https://links.lww.com/JP9/A20. 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.

Results

Participants

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
Primary
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
	68Ga-NODAGA-LM3	68Ga-DOTA-LM3		68Ga-NODAGA-LM3	68Ga-NODAGA-JR11
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
	68Ga-NODAGA-LM3	68Ga-DOTA-LM3		68Ga-NODAGA-LM3	68Ga-NODAGA-JR11
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, K_i67 = 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, https://links.lww.com/JP9/A21.

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.

Discussion

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]

Conclusion

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
	68Ga-NODAGA-LM3	68Ga-DOTA-LM3		68Ga-NODAGA-LM3	68Ga-NODAGA-JR11
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.

Acknowledgments

None.

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.