Tektrotyd kit was developed by Polatom company for 99mTc labeling to make an alternative tracer of somatostatin receptor scintigraphy available. Since 2005, 99mTc-EDDA/HYNIC-Tyr3-Octreotide has been used in clinical imaging and achieved high impact in management of patients with neuroendocrine tumors. Knowing the limitations and pitfalls is essential to provide ac-curate diagnosis. Therefore, the potential pitfalls associated with the use of 99mTc-EDDA/HYNIC-TOC are reviewed on the basis of own experience. Data were analyzed of 310 patients who underwent somatostatin receptor scintigraphy with 99mTc-Tektrotyd. Pitfalls during radiolabeling process or acquisition can worsen the sensitivity of SRS (somatostatin receptor scintigraphy). Recognizing physi-ological and clinical pitfalls, the diagnostic accuracy will improve.

Metabolomics strategies are widely used to examine obesity and type 2 diabetes (T2D). Patients with obesity (n = 31) or T2D (n = 26) and sex- and age-matched controls (n = 28) were recruited, and serum and tear samples were collected. The concentration of 23 amino acids and 10 biogenic amines in serum and tear samples was analyzed. Statistical analysis and Pearson correlation analysis along with network analysis were carried out. Compared to controls, changes in the level of 6 analytes in the obese group and of 10 analytes in the T2D group were statistically significant. For obesity, the energy generation, while for T2D, the involvement of NO synthesis and its relation to insulin signaling and inflammation, were characteristic. We found that BCAA and glutamine metabolism, urea cycle, and beta-oxidation make up crucial parts of the metabolic changes in T2D. According to our data, the retromer-mediated retrograde transport, the ethanolamine metabolism, and, consequently, the endocannabinoid signaling and phospholipid metabolism were characteristic of both conditions and can be relevant pathways to understanding and treating insulin resistance. By providing potential therapeutic targets and new starting points for mechanistic studies, our results emphasize the importance of complex data analysis procedures to better understand the pathomechanism of obesity and diabetes.

Background: Metabolic syndrome and its individual components lead to wide-ranging consequences, many of which affect the central nervous system. In this study, we compared the [¹⁸F]FDG regional brain metabolic pattern of participants with type 2 diabetes mellitus (T2DM) and non-DM obese individuals. Methods: In our prospective study, 51 patients with controlled T2DM (ages 50.6 ± 8.0 years) and 45 non-DM obese participants (ages 52.0 ± 9.6 years) were enrolled. Glucose levels measured before PET/CT examination (pre-PET glucose) as well as laboratory parameters assessing glucose and lipid status were determined. NeuroQ application (NeuroQ^TM 3.6, Syntermed, Philips) was used to evaluate regional brain metabolic differences. [¹⁸F]FDG PET/CT (AnyScan PC, Mediso) scans, estimating brain metabolism, were transformed to MNI152 brain map after T1 registration and used for SPM-based group comparison of brain metabolism corrected for pre-PET glucose, and correlation analysis with laboratory parameters. Results: NeuroQ analysis did not reveal significant regional metabolic defects in either group. Voxel-based group comparison revealed significantly (P_FWE<0.05) decreased metabolism in the region of the precuneus and in the right superior frontal gyrus (rSFG) in the diabetic group as compared to the obese patients. Data analysis corrected for pre-PET glucose level showed a hypometabolic difference only in the rSFG in T2DM. Voxel-based correlation analysis showed significant negative correlation of the metabolism in the following brain regions with pre-PET glucose in diabetes: precuneus, left posterior orbital gyrus, right calcarine cortex and right orbital part of inferior frontal gyrus; whilst in the obese group only the right rolandic (pericentral) operculum proved to be sensitive to pre-PET glucose level. Conclusions: To our knowledge, this is the first study to perform pre-PET glucose level corrected comparative analysis of brain metabolism in T2DM and obesity. We also examined the pre-PET glucose level dependency of regional cerebral metabolism in the two groups separately. Large-scale future studies are warranted to perform further correlation analysis with the aim of determining the effects of metabolic disturbances on brain metabolism.

Among humanized monoclonal antibodies, bevacizumab specifically binds to vascular endothelial growth factor A (VEGF-A). VEGF-A is an overexpressed biomarker in cervix carcinoma and is involved in the development and maintenance of tumor-associated neo-angiogenesis. The non-invasive positron emission tomography using radiolabeled target-specific antibodies (immuno-PET) provides the longitudinal and quantitative assessment of tumor target expression. Due to antibodies having a long-circulating time, radioactive metal ions (e.g., ⁵²Mn) with longer half-lives are the best candidates for isotope conjugation. The aim of our preclinical study was to assess the biodistribution and tumor-targeting potential of ⁵²Mn-labeled DOTAGA-bevacizumab. The VEGF-A targeting potential of the new immuno-PET ligand was assessed by using the VEGF-A expressing KB-3-1 (human cervix carcinoma) tumor-bearing CB17 SCID mouse model and in vivo PET/MRI imaging. Due to the high and specific accumulation found in the subcutaneously located experimental cervix carcinoma tumors, [⁵²Mn]Mn-DOTAGA-bevacizumab is a promising PET probe for the detection of VEGF-A positive gynecological tumors, for patient selection, and monitoring the efficacy of therapies targeting angiogenesis.

Background/Objectives: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. Methods: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients’ ^99mTc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images—75, 50, and 25% of total counts—which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. Results: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (p > 0.05). Conclusions: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time.