Background: A decrease in thermogenesis is suspected to be implicated in the energy expenditure reduction during breast cancer treatment. This study aimed to investigate the impact of chemotherapy on the metabolic activity of brown adipose tissue (BAT) and the link with weight variation.Methods: This was an ancillary analysis of a multicentre trial involving 109 HER2+ breast cancer patients treated with neoadjuvant chemotherapy. A centralised review of 18 F-FDG uptake intensity (SUV max ) in specific BAT regions (cervical and supraclavicular) was conducted on two PET-CT scans for each patient (before and after the first course of chemotherapy). Results: Overall, after one course of chemotherapy a significant decrease of 4.4% in 18 F-FDG-uptake intensity was observed. It was not correlated to initial BMI, age or season. During chemotherapy, 10.1% (n = 11) of the patients lost weight (− 7.7 kg ± 3.8 kg; ie, − 9.4% ± 3.7%) and 29.4% (n = 32) gained weight (+ 5.1 kg ± 1.7 kg; ie, + 8.5% ± 2.6%). Among these subgroups, only the patients who had gained weight underwent a significant decrease (13.42%) in 18 F-FDG uptake intensity (p = 0.042). Conclusion:This study is the first to highlight in a large cohort of patients the negative impact of chemotherapy on brown adipose tissue activity. Weight gain during chemotherapy could thus potentially be explained in part by a decrease in brown adipose tissue activity.
The objective of this study was to evaluate periarticular FDG uptake scores from 18 F-FDG-PET/CT to identify polymyalgia rheumatica (PMR) within a population presenting rheumatic diseases. Methods: A French retrospective study from 2011 to 2015 was conducted. Patients who underwent 18 F-FDG-PET/CT for diagnosis or follow-up of a rheumatism or an unexplained biological inflammatory syndrome were included. Clinical data and final diagnosis were reviewed. Seventeen periarticular sites were sorted by a visual reading enabling us to calculate two scores: mean FDG visual uptake score, number of sites with significant uptake same as that or higher than liver uptake intensity and by a semi-quantitative analysis using mean maximum standardized uptake value (SUVmax). Optimal cutoffs of visual score and SUVmax to diagnose PMR were determined using receiver operating characteristics curves. Results: Among 222 18 F-FDG PET/CT selected for 215 patients, 161 18 F-FDG PET/CT were performed in patients who presented inflammatory rheumatism as a final diagnosis (of whom 57 PMR). The presence of at least three sites with significant uptake identified PMR with a sensitivity of 86% and a specificity of 85.5% (AUC 0.872, 95% CI [0.81-0.93]). The mean FDG visual score cutoff to diagnose a PMR was 0.765 with a sensitivity of 82.5% and a specificity of 75.8% (AUC 0.854; 95% CI [0.80-0.91]). The mean SUVmax cutoff to diagnose PMR was 2.168 with a sensitivity of 77.2% and a specificity of 77.6% (AUC 0.842; 95% CI [0.79-0.89]). Conclusions: This study suggests that 18 F-FDG PET/CT had good performances to identify PMR within a population presenting rheumatic diseases.
Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms.Methods: This retrospective study included 140 patients who underwent [18F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning.Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort.Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.
PurposeDifferentiating brain metastasis recurrence from radiation necrosis can be challenging during MRI follow-up after stereotactic radiotherapy. [18F]-FDG is the most available PET tracer, but standard images performed 30 to 60 minutes postinjection provide insufficient accuracy. We compared the diagnostic performance and interobserver agreement of [18F]-FDG PET with delayed images (4–5 hours postinjection) with the ones provided by standard and dual-time-point imaging.MethodsConsecutive patients referred for brain [18F]-FDG PET after inconclusive MRI were retrospectively included between 2015 and 2020 in 3 centers. Two independent nuclear medicine physicians interpreted standard (visually), delayed (visually), and dual-time-point (semiquantitatively) images, respectively. Adjudication was applied in case of discrepancy. The final diagnosis was confirmed histologically or after 6 months of MRI follow-up. Areas under the receiver operating characteristic curves were pairwise compared.ResultsForty-eight lesions from 46 patients were analyzed. Primary tumors were mostly located in the lungs (57%) and breast (23%). The median delay between radiotherapy and PET was 15.7 months. The final diagnosis was tumor recurrence in 24 of 48 lesions (50%), with histological confirmation in 19 of 48 lesions (40%). Delayed images provided a larger area under the receiver operating characteristic curve (0.88; 95% confidence interval [CI], 0.75–0.95) than both standard (0.69; 95% CI, 0.54–0.81; P = 0.0014) and dual-time-point imaging (0.77; 95% CI, 0.63–0.88; P = 0.045), respectively. Interobserver agreement was almost perfect with delayed images (κ = 0.83), whereas it was moderate with both standard (κ = 0.48) and dual-time-point images (κ = 0.61).Conclusions[18F]-FDG PET with delayed images is an accurate and reliable alternative to differentiate metastasis recurrence from radiation necrosis in case of inconclusive MRI after brain stereotactic radiotherapy.
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