Search

Purpose: 68 Ga-DOTA-TOC uptake in the pancreas uncinate process is often found due to physiologic expression of somatostatin receptors (SSTR). We investigated the association of physiologic 68 Ga-DOTA-TOC uptake in the pancreas uncinate process with blood glucose level. Methods: 68 Ga-DOTA-TOC PET scans acquired from 44 patients (male:female = 20:24, age = 50.8 ± 14.8y [mean ± SD]) were retrospectively analyzed. The blood glucose level (BGL) was examined before 68 Ga-DOTA-TOC injection. Patients diagnosed with diabetes mellitus and patients with BGL over 200 mg/dl were excluded. 68 Ga-DOTA-TOC uptake was measured by the maximum standardized uptake values (SUVmax). Additionally, SSTR-positive volume (SV) in the pancreas uncinate process was measured with two different thresholds: by SUV cutoff of 2.5 (SV2.5) and 40% of SUVmax (SV40%). These measurements on 68 Ga-DOTA-TOC PET were correlated with BGL. Results: The mean of SUVmax of the pancreas uncinate process was 6.51 ± 2.04. SV2.5 was 17.81 ± 7.14 cm3, and SV40% was 18.20 ± 8.83 cm3. A significant negative correlation was found between SUVmax of the pancreas uncinate process and BGL (r = -0.37, p = 0.01). The ratio between SUVmax of the pancreas uncinate process and SUVmean of the pancreas body also showed a significance negative correlation with BGL (r = -0.40, p = 0.01). SV2.5 (r = 0.27, p = 0.07) and SV40% (r = -0.151, p = 0.32) were not significantly correlated with BGL. Conclusion: Physiologic 68 Ga-DOTA-TOC uptake in the pancreas uncinate process was negatively correlated with BGL. Our results suggested that glycemia could affect physiologic uptake of 68 Ga-DOTA-TOC. [ABSTRACT FROM AUTHOR]

Purpose: Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers. Methods: Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method. Results: The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PET and a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively. Conclusion: We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers. [ABSTRACT FROM AUTHOR]

Introduction: Technetium thyroid uptake (TcTU) measured by single-photon emission CT/CT (SPECT/CT) is an important diagnostic tool for the differential diagnosis of Graves' disease and destructive thyroiditis. Artificial intelligence (AI) may reduce CT-induced radiation exposure by substituting the role of CT in attenuation correction (AC) and thyroid segmentation, thus realising CT-free SPECT. This study aims to compare the diagnostic accuracy for the differential diagnosis of thyrotoxicosis between CT-free SPECT and SPECT/CT., Methods and Analysis: The AI-based CT-free SPECT is a single-blind, multicentre, prospective, non-inferiority, clinical trial with a paired design conducted in the Republic of Korea. Eligible participants are adult (≥19 years old) thyrotoxicosis patients without a previous history of hyperthyroidism or hypothyroidism. Approximately 160 subjects will be screened for quantitative thyroid SPECT/CT using Tc-99m pertechnetate. CT-free thyroid SPECT will be realised using only SPECT data by the trained convolutional neural networks. TcTU will be calculated by SPECT/CT and CT-free SPECT in each subject. The primary endpoint is the accuracy of diagnosing Graves' disease using TcTU. The trial will continue until 152 completed datasets have been enrolled to assess whether the 95% (two-sided) lower confidence limit of the accuracy difference (CT-free SPECT accuracy-SPECT/CT accuracy) for Graves' disease is greater than -0.1. The secondary endpoints include the accuracy of diagnosing destructive thyroiditis and predicting the need for antithyroid drug prescription within 1 month of the SPECT/CT., Ethics and Dissemination: The study protocol has been approved by the institutional review board of Seoul National University Bundang Hospital (IRB No. B-2304-824-301), Konkuk University Medical Center (IRB No. 2023-05-022-006) and Chonnam National University Hospital (IRB No. CNUH-2023-108). Findings will be disseminated as reports, presentations and peer-reviewed journal articles., Trial Registration Number: KCT0008387, Clinical Research Information Service of the Republic of Korea (CRIS)., Competing Interests: Competing interests: WWL and KK hold a patent for the CT-free thyroid SPECT technology. The title of the invention is ‘Artificial intelligence algorithm enabling CT-free quantitative thyroid single-photon emission computed tomography (SPECT)’ (PCT/KR2023/007541). Other than this, there are no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)