A Microdosing Study with 99mTc-PHC-102 for the SPECT/CT Imaging of Primary and Metastatic Lesions in Renal Cell Carcinoma Patients

99mTc-PHC-102 is a 99mTc-labeled derivative of acetazolamide, a high-affinity small organic ligand of carbonic anhydrase IX (CAIX). 99mTc-PHC-102 has previously shown favorable in vivo biodistribution properties in mouse models of CAIX-positive clear cell renal cell carcinoma (ccRCC) and colorectal cancer. In this study, we aimed to explore the targeting performance of 99mTc-PHC-102 in SPECT in patients with renal cell carcinoma while also assessing the safety and tolerability of the radiotracer. Methods: We studied 5 patients with localized or metastatic ccRCC in a microdosing regimen, after the administration of a 50-μg total of CAIX ligand and 600–800 MBq of 99mTc-PHC-102. Tissue distribution and residence time in normal organs and tumors were analyzed by serial SPECT/CT scans at 3 time points (30 min, 2 h, and 6 h) after intravenous administration. Results: In the 5 patients studied, 99mTc-PHC-102 was well tolerated and no study drug–related adverse events were recorded. In the stomach, kidneys, and gallbladder, the radiotracer showed a rapid initial uptake, which cleared over time. Localization of the study drug in primary tumors of 5 patients was observed, with favorable tumor-to-background ratios. 99mTc-PHC-102 SPECT/CT allowed the identification of 4 previously unknown lung and lymph node metastases in 2 patients. Conclusion: 99mTc-PHC-102 is a promising SPECT tracer for the imaging of patients with ccRCC. This tracer has the potential to identify primary and metastatic lesions in different anatomic locations. 99mTc-PHC-102 might also serve as a companion diagnostic agent for future CAIX-targeting therapeutics.ISSN:0097-9058ISSN:0022-3123ISSN:0161-5505ISSN:2159-662XISSN:1535-566

Cold Exposure Distinctively Modulates Parathyroid and Thyroid Hormones in Cold-Acclimatized and Non-Acclimatized Humans

Cold-induced activation of thermogenesis modulates energy metabolism, but the role of humoral mediators is not completely understood. We aimed to investigate the role of parathyroid and thyroid hormones in acute and adaptive response to cold in humans. Examinations were performed before/after 15 minutes of ice-water swimming (n = 15) or 120 to 150 minutes of cold-induced nonshivering thermogenesis (NST) applied to cold-acclimatized (n = 6) or non-acclimatized (n = 11) individuals. Deep-neck brown adipose tissue (BAT) was collected from non-acclimatized patients undergoing elective neck surgery (n = 36). Seasonal variations in metabolic/hormonal parameters of ice-water swimmers were evaluated. We found that in ice-water swimmers, PTH and TSH increased and free T3, T4 decreased after a 15-minute winter swim, whereas NST-inducing cold exposure failed to regulate PTH and free T4 and lowered TSH and free T3. Ice-water swimming-induced increase in PTH correlated negatively with systemic calcium and positively with phosphorus. In non-acclimatized men, NST-inducing cold decreased PTH and TSH. Positive correlation between systemic levels of PTH and whole-body metabolic preference for lipids as well as BAT volume was found across the 2 populations. Moreover, NST-cooling protocol-induced changes in metabolic preference for lipids correlated positively with changes in PTH. Finally, variability in circulating PTH correlated positively with UCP1/UCP1, PPARGC1A, and DIO2 in BAT from neck surgery patients. Our data suggest that regulation of PTH and thyroid hormones during cold exposure in humans varies by cold acclimatization level and/or cold stimulus intensity. Possible role of PTH in NST is indicated by its positive relationships with whole-body metabolic preference for lipids, BAT volume, and UCP1 content

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy