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The role of somatostatin receptor (SSTR) PET/CT, using 68 Ga-based tracers or [ 64 Cu]Cu-DOTATATE ( 64 Cu-DOTATATE), in the management of patients with neuroendocrine neoplasm (NEN) is guided by appropriate use criteria (AUC). In this study, we performed systematic analyses of referral patterns and image findings of routine 64 Cu-DOTATATE PET/CT scans to support AUC development. Methods: We included all clinical routine 64 Cu-DOTATATE PET/CT scans performed between April 10, 2018 (start of clinical use), and May 2, 2022, at Copenhagen University Hospital-Rigshospitalet. We reviewed the referral text and image report of each scan and classified the indication according to clinical scenarios as listed in the AUC. Results: In total, 1,290 patients underwent 2,249 64 Cu-DOTATATE PET/CT scans. Monitoring of patients with NEN seen both on conventional imaging and on SSTR PET without clinical evidence of progression was the most common indication (defined as "may be appropriate" in the AUC) and accounted for 703 (31.3%) scans. Initial staging after NEN diagnosis ("appropriate" in the AUC) and restaging after curative-intent surgery ("may be appropriate" in the AUC) accounted for 221 (9.8%) and 241 (10.7%) scans, respectively. Selection of patients eligible for peptide receptor radionuclide therapy ("appropriate" in the AUC) and restaging after peptide receptor radionuclide therapy completion ("appropriate" in the AUC) accounted for 95 (4.2%) and 115 (5.1%) scans, respectively. The number of scans performed for indications not defined in the AUC was 371 (16.5%). Image result analysis revealed no disease in 669 scans (29.7%), stable disease in 582 (25.9%), and progression in 461 (20.5%). In 99 of the 461 (21.5%) scans, progression was detected on PET but not on CT. Conclusion: Our study provided real-life data that may contribute to support development of 64 Cu-DOTATATE/SSTR PET/CT guidelines including AUC. Some scenarios listed as "may be appropriate" in the current AUC were frequent in our data. Monitoring of patients with NEN without clinical evidence of progression was the most frequent indication for 64 Cu-DOTATATE PET/CT, in which disease progression was detected in more than one third, and a large proportion was visible by PET only. We therefore conclude that this scenario could potentially be classified as appropriate., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Objective: We present an algorithm to estimate the delay between a tissue time-activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics., Methods: Whole-body scans of 15 patients divided equally among three tracers, namely [ 15 O]H 2 O, [ 18 F]FDG and [ 64 Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times were estimated by fitting the cumulatively summed input function and tissue time-activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation: name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time-activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution., Results: Our proposed model performs better for low signal-to-noise ratio time-activity curves compared to both cross-correlation and the one-tissue compartment models for non-[ 15 O]H 2 O tracers. Testing on synthetically produced time-activity curves showed only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays., Conclusion: The algorithm is robust to noise and proves applicable on a range of tracers as tested on [ 15 O]H 2 O, [ 18 F]FDG and [ 64 Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Nielsen, Lindberg, Bordallo, Johnbeck, Law, Fischer, Andersen and Andersen.)

Frequent somatostatin receptor PET, for example, 64 Cu-DOTATATE PET, is part of the diagnostic work-up of patients with neuroendocrine neoplasms (NENs), resulting in high accumulated radiation doses. Scan-related radiation exposure should be minimized in accordance with the as-low-as-reasonably achievable principle, for example, by reducing injected radiotracer activity. Previous investigations found that reducing 64 Cu-DOTATATE activity to below 50 MBq results in inadequate image quality and lesion detection. We therefore investigated whether image quality and lesion detection of less than 50 MBq of 64 Cu-DOTATATE PET could be restored using artificial intelligence (AI). Methods: We implemented a parameter-transferred Wasserstein generative adversarial network for patients with NENs on simulated low-dose 64 Cu-DOTATATE PET images corresponding to 25% (PET 25% ), or about 48 MBq, of the injected activity of the reference full dose (PET 100% ), or about 191 MBq, to generate denoised PET images (PET AI ). We included 38 patients in the training sets for network optimization. We analyzed PET intensity correlation, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean-square error (MSE) of PET AI /PET 100% versus PET 25% /PET 100% Two readers assessed Likert scale-defined image quality (1, very poor; 2, poor; 3, moderate; 4, good; 5, excellent) and identified lesion-suspicious foci on PET AI and PET 100% in a subset of the patients with no more than 20 lesions per organ ( n = 33) to allow comparison of all foci on a 1:1 basis. Detected foci were scored (C 1 , definite lesion; C 0 , lesion-suspicious focus) and matched with PET 100% as the reference. True-positive (TP), false-positive (FP), and false-negative (FN) lesions were assessed. Results: For PET AI /PET 100% versus PET 25% /PET 100% , PET intensity correlation had a goodness-of-fit value of 0.94 versus 0.81, PSNR was 58.1 versus 53.0, SSIM was 0.908 versus 0.899, and MSE was 2.6 versus 4.7. Likert scale-defined image quality was rated good or excellent in 33 of 33 and 32 of 33 patients on PET 100% and PET AI , respectively . Total number of detected lesions was 118 on PET 100% and 115 on PET AI Only 78 PET AI lesions were TP, 40 were FN, and 37 were FP, yielding detection sensitivity (TP/(TP+FN)) and a false discovery rate (FP/(TP+FP)) of 66% (78/118) and 32% (37/115), respectively. In 62% (23/37) of cases, the FP lesion was scored C 1 , suggesting a definite lesion. Conclusion: PET AI improved visual similarity with PET 100% compared with PET 25% , and PET AI and PET 100% had similar Likert scale-defined image quality. However, lesion detection analysis performed by physicians showed high proportions of FP and FN lesions on PET AI , highlighting the need for clinical validation of AI algorithms., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: Patients with neuroendocrine neoplasms (NEN) engage in lifelong follow-up with frequent somatostatin receptor PET, e.g. [ 64 Cu]Cu-DOTATATE PET, and continued measures to reduce radiation exposures should be in pursued in accordance with the as-low-as-reasonably-achievable (ALARA) principle. We therefore aimed to determine the lowest achievable [ 64 Cu]Cu-DOTATATE dose while maintaining image quality and lesion detection rate., Procedures: We included scans from 38 patients with NEN referred to routine [ 64 Cu]Cu-DOTATATE PET/CT. Using reconstruction of under-sampled PET list-mode data, we simulated [ 64 Cu]Cu-DOTATATE activity dose-reduced PET equivalents with median [range] 142 MBq [127;157], 95 MBq [85;105], and 48 MBq [42;52], corresponding to 75% (PET 75% ), 50% (PET 50% ), and 25% (PET 25% ) of the full-dose 191 MBq [169;209] (PET 100% ). Three blinded readers independently assessed image quality (scores 1-5), lesion confidence (scores 0-2), and counted lesions grouped by organs and regions. Number of lesions, proportions of patients with diagnostic image quality (reader-median image quality ≥ 4), diagnostic lesion confidence (reader-median lesion confidence ≥ 1), and per-patient sensitivities and specificities for organ-specific disease on PET 75-25% were compared with PET 100% ., Results: The median [ 64 Cu]Cu-DOTATATE activity dose could be reduced from 191 to 142 MBq without decline in diagnostic image quality (P = 0.62), diagnostic lesion confidence (P = 1.0), or number of lesions detected in major organs or regions (P = 0.19-0.71). Sensitivity and specificity for detection of liver disease were 100% (26/26 patients) and 100% (12/12), respectively, for both PET 75% and PET 50% . Overall sensitivity for detection of NEN was 100% (26/26) for both PET 75% and PET 50% , and overall specificities were 92% (11/12) and 100% (12/12) for PET 75 and PET 50 , respectively. Following dose-blinded post hoc review, the PET 75% specificity was adjusted to 100% (12/12)., Conclusions: The [ 64 Cu]Cu-DOTATATE activity dose can be reduced from 191 MBq to at least 142 MBq without losing image quality or lesion detection ability and further reduced to 95 MBq without loss of clinically relevant information., (© 2022. World Molecular Imaging Society.)

Background: Segmentation of neuroendocrine neoplasms (NENs) in [ 64 Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [ 64 Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a deep learning U-net architecture. Dice score, sensitivity and precision were used for selection of the final model. AI segmentations were implemented in a clinical imaging viewer where a physician evaluated performance and performed manual adjustments., Results: Cross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01., Conclusion: We implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine., (© 2022. The Author(s).)