Objective To compare imaging quality and semi-quantitative parameters of 18F-FDG whole-body images obtained with domestic NeuWise and Philips Ingenuity TF PET/CT scanners.Methods Thirty-four patients who underwent 18F-FDG whole-body scanning using NeuWise and Philips Ingenuity TF PET/CT systems respectively on the same day were enrolled.The imaging quality and semi-quantitative parameters of 2 kind images,also the mean standard uptake value(SUVmean)of normal tissue,the maximum standard uptake value(SUVmax),peak standard uptake value(SUVpeak),SUVmean of lesions,total lesion glycolysis(TLG)and metabolic tumor volume(MTV)were compared.Results No significant difference of imaging quality nor semi-quantitative parameters of lesions(all P＞0.05),while significant differences of SUVmean of aortic arch,liver,lumbar vertebra and spinal cord were found between 2 kind images(all P＜0.05).Strong correlations of SUVmax,SUVmean,MTV and TLG of lesions(r,=0.734-0.890,P＜0.001),and high correlation of SUVpeak(rs=0.919,P＜0.001)were found between 2 kind images.The consistency of SUVmax,SUVpeak,SUVmean,TLG and MTV at the lesion site between 2 kind images were very good to extremely good(ICC=0.891-0.986,all P＜0.001),and the differences of all above semi-quantitative parameters were within 95％confidence interval.Conclusion Imaging quality of 18F-FDG whole-body images obtained with domestic NeuWise and Philips Ingenuity TF PET/CT scanners could meet the requirements of clinical diagnosis and treatment,and semi-quantitative parameters obtained based on both images had good consistencies.

Objective To compare imaging quality and semi-quantitative parameters of 18F-FDG whole-body images obtained with domestic NeuWise and Philips Ingenuity TF PET/CT scanners.Methods Thirty-four patients who underwent 18F-FDG whole-body scanning using NeuWise and Philips Ingenuity TF PET/CT systems respectively on the same day were enrolled.The imaging quality and semi-quantitative parameters of 2 kind images,also the mean standard uptake value(SUVmean)of normal tissue,the maximum standard uptake value(SUVmax),peak standard uptake value(SUVpeak),SUVmean of lesions,total lesion glycolysis(TLG)and metabolic tumor volume(MTV)were compared.Results No significant difference of imaging quality nor semi-quantitative parameters of lesions(all P＞0.05),while significant differences of SUVmean of aortic arch,liver,lumbar vertebra and spinal cord were found between 2 kind images(all P＜0.05).Strong correlations of SUVmax,SUVmean,MTV and TLG of lesions(r,=0.734-0.890,P＜0.001),and high correlation of SUVpeak(rs=0.919,P＜0.001)were found between 2 kind images.The consistency of SUVmax,SUVpeak,SUVmean,TLG and MTV at the lesion site between 2 kind images were very good to extremely good(ICC=0.891-0.986,all P＜0.001),and the differences of all above semi-quantitative parameters were within 95％confidence interval.Conclusion Imaging quality of 18F-FDG whole-body images obtained with domestic NeuWise and Philips Ingenuity TF PET/CT scanners could meet the requirements of clinical diagnosis and treatment,and semi-quantitative parameters obtained based on both images had good consistencies.

Objective:To explore the value of salivary gland imaging based on deep learning and Delta radiomics in assessing salivary gland injury after 131I treatment in post-thyroidectomy thyroid cancer patients. Methods:A retrospective analysis on 223 patients (46 males, 177 females, age(47.7±14.0) years ) with papillary thyroid cancer, who underwent total thyroidectomy and 131I treatment in Affiliated Hospital of Guilin Medical University between December 2019 and January 2022, was conducted. All patients underwent salivary gland 99Tc mO 4- imaging before and after 131I therapy. The patients were categorized according to salivary gland function based on 99Tc mO 4- imaging results (normal salivary gland vs salivary gland injury), and divided into training and test sets in a ratio of 7∶3. A ResNet-34 neural network model was trained using images at the time of maximum salivary gland radioactivity and those based on background radioactivity counts for structured image feature data. The Delta radiomics approach was then used to subtract the image feature values of the two periods, followed by feature selection through t-test, correlation analysis, and the least absolute shrinkage and selection operator( LASSO) algorithm, to develop logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) predictive models. The diagnostic performance of 3 models for salivary gland function on the test set was compared with that of the manual interpretation. The AUCs of the 3 models on the test set were compared (Delong test). Results:Among the 67 cases of the test set, the diagnostic accuracy of 3 physicians were 89.6%(60/67), 83.6%(56/67), and 82.1%(55/67) respectively, with the time required for diagnosis of 56, 74 and 55 min, respectively. The accuracies of LR, SVM, and KNN models were 91.0%(61/67), 86.6%(58/67), and 82.1%(55/67), with the required times of 12.5, 15.3 and 17.9 s, respectively. All 3 radiomics models demonstrated good classification and predictive capabilities, with AUC values for the training set of 0.972, 0.965, and 0.943, and for the test set of 0.954, 0.913, and 0.791, respectively. There were no significant differences among the AUC values for the test set ( z values: 0.72, 1.18, 1.82, all P>0.05). Conclusion:The models based on deep learning and Delta radiomics possess high predictive value in assessing salivary gland injury following 131I treatment after surgery in patients with thyroid cancer.