• Journal of radiological science and technology
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• v.29 no.4
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• pp.249-255
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• 2006

Purpose: The diagnostic utility of fluorine-18 2-deoxy-D-glucose positron emission tomograhpy ($^{18}F-FDG $PET) for the non-invasive differentiation of focal lung lesions originated from cancer or inflammation disease by combined visual image interpretation and semi-quantitative uptake value analysis has been documented. In general, Standardized Uptake Value(SUV) is used to diagnose lung disease. But SUV does not contain dynamic information of lung tissue for the glucose. Therefore, this study was undertaken to hypothesis that analysis of dynamic kinetics of focal lung lesions base on $^{18}F-FDG$ PET may more accurately determine the lung disease. So we compared Time Activity Curve(TAC), Standardized Uptake Value-Dynamic Curve(SUV-DC) graph pattern with Glucose Metabolic Rate(MRGlu) from Patlak analysis. Methods: With lung disease, 17 patients were examined. They were injected with $^{18}F-FDG$ over 30-s into peripheral vein while acquisition of the serial transaxial tomographic images were started. For acquisition protocol, we used twelve 10-s, four 30-s, sixteen 60-s, five 300-s and one 900-s frame for 60 mins. Its images were analyzed by visual interpretation TAC, SUV-DC and a kinetic analysis(Patlak analysis). The latter was based on region of interest(ROIs) which were drawn with the lung disease shape. Each optimized patterns were compared with itself. Results: In TAC patterns, it hard to observe cancer type with inflammation disease in early pool blood area but over the time cancer type slope more remarkably increased than inflammation disease. SUV-DC was similar to TAC pattern. In the result of Patlak analysis, In time activity curve of aorta, even though inflammation disease showed higher blood activity than cancer, at first as time went by, blood activity of inflammation disease became the lowest. However, in time activity curve of tissue, cancer had the highest uptake and inflammation disease was in the middle. Conclusion: Through the examination, TAC and SUV-DC could approached the results that lung cancer type and inflammation disease type has it's own difference shape patterns. Also, it has outstanding differentiation between cancer type and inflammation in Patlak and MRGlu analysis. Through these analysis methods, it will helpful to separation lung disease.

• Journal of the Korean Physical Society
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• v.80
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• pp.247-256
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• 2022

Our objective was to investigate radiomics signatures and prediction models defined by four segmentation methods in using 2-[¹⁸F]fluoro-2-deoxy-d-glucose positron emission tomography (¹⁸F-FDG PET) imaging of lung metastases of soft-tissue sarcomas (STSs). For this purpose, three fixed threshold methods using the standardized uptake value (SUV) and gradient-based edge detection (ED) were used for tumor delineation on the PET images of STSs. The Dice coefficients (DCs) of the segmentation methods were compared. The least absolute shrinkage and selection operator (LASSO) regression and Spearman's rank, and Friedman's ANOVA test were used for selection and validation of radiomics features. The developed radiomics models were assessed using ROC (receiver operating characteristics) curve and confusion matrices. According to the results, the DC values showed the biggest difference between SUV40% and other segmentation methods (DC: 0.55 and 0.59). Grey-level run-length matrix_run-length nonuniformity (GLRLM_RLNU) was a common radiomics signature extracted by all segmentation methods. The multivariable logistic regression of ED showed the highest area under the ROC (receiver operating characteristic) curve (AUC), sensitivity, specificity, and accuracy (AUC: 0.88, sensitivity: 0.85, specificity: 0.74, accuracy: 0.81). In our research, the ED method was able to derive a significant model of radiomics. GLRLM_RLNU which was selected from all segmented methods as a meaningful feature was considered the obvious radiomics feature associated with the heterogeneity and the aggressiveness. Our results have apparently showed that radiomics signatures have the potential to uncover tumor characteristics.