Kinetic Modeling And Analysis Of FDG-PET Imaging To Differentiate Malignancies From Inflammation And Brown Adipose Tissue

[¹⁸F]fluoro-2-deoxy-D-glucose?FDG? Positron Emission Tomography?PET? has long been used to measure the glucose metabolism in human and animal studies. One of the most common and successful uses of the FDG-PET technique is the malignant tumore identification?diagnosis? and staging, showing great accuracy and sensitivity. However, FDG is not a tumor-specific tracer, making false-positive rates of FDG-PET high and unavoidable. False-positivity is especially a major concern for lung cancer studies. In general, most false-positive cases are the results of inflammatory processes and the existences of Brown Adipose Tissue.Inflammation as a defensive reaction of the body will cause infiltration of various inflammatory cells at inflammatory spots such as lymphocyte, macrophage, neutrophils, and others. This kind of cell infiltration elevates the glucose metabolism by activating the performance of glucose transporter, thus causing high uptake of FDG in PET images and confusion between inflammation and malignancies. Brown Adipose Tissue?brown fat? is a kind of fat which exists only in mammals with a function of maintaining body temperature and energy metabolism, and it is the major source for non-shivering heat production. In recent years brown fat is a hot topic in obesity and diabetes researches. Fat cells of Brown Adipose Tissue contain abundant mitochondria. Through burning fat and glucose, heat is generated. Therefore, the glucose activity is quite high inside the Brown Adipose Tissue, making it easily detected during PET imaging. There are several factors that influence the activity of Brown Adipose Tissue such as the seasons in a year and food control. Through fasting and performing PET scan in freezing seasons, brown fat is activated and high FDG uptake is found in PET images and causing false-positive results.Conventional static PET imaging and dual-time point PET imaging are found to be unable to differentiate malignancies from inflammatory lesions or brown fat. Therefore, the aim of our study is to investigate analytic tools for FDG-PET to differentiate malignancies from inflammation and Brown Adipose Tissue to avoid false-positive results. We established tumor and inflammation animal models with different body locations. To activate Brown Adipose Tissue, we acquired the PET scan in winter seasone?and also under fasting condition?. We performed dynamic PET imaging and estimated kinetic parameters of different lesions using tracer compartmental modeling. Apart from analysis of different lesion in the same body location, we also analyzed on parameters of same kind of lesion in different body locations.In Chapter 2, we introduced the mouse models used in this study for tumor and inflammation in different body locations, and activation of brown fat through food control and season control. Tumor models were either heterotopic or orthotopic. The heterotopic model is built by injection of Lewis Lung Carcinoma cells subcutaneously and the orthotopic models are established through inoculation of the same tumor cells inside the lung. Inflammatory lesions are divided into 3 kinds: Heterotopic, orthotopic and spontaneously induced models, through injection of turpentine oil subcutaneously, inhalation of lipopolysaccharide?LPS?, and induction of Mouse Hepatitis Virus. We also use part of the subcutaneous tumor models for establishment of tumor-inflammation coexistence models to observe tracer kinetics in both tumor and inflammatory lesion in the same animals. Brown fat is activated through fasting and season control. After building all the animal models, dynamic PET scan was performed on all the animals. And all animals were sacrificed and tissues were extracted for frozen section and H&E staining. Histological results are used to confirm the PET imaging data.In Chapter 3, we described the PET data acquisition in details provided the results of simple visual assessment. The whole dynamic PET data acquisition lasted for 1 hour and 20 minutes. A specially designed experimental procedure and framing protocol was used. After data acquisition, visual analysis was performed on PET images. High FDG uptake was observed in both tumor and inflammatory lesions in different animal models,. Brown fat activities shown as significant FDG accumulations were detected on the back of four mice. Visual analysis, however, failed to differentiate malignancies from inflammation and Brown Adipose Tissue.In Chapter 4, we introduced all the data analysis methods investigated in this study and presented the corresponding results. Semi-quantitative parameters for static PET data and quantitative parameters for dynamic data with kinetic modeling were compared. Regions of Interests?ROIs? were drawn manually for both static and dynamic images. For static analysis, Standardized Uptake Value?SUVmax? was calculated. Statistical results showed significances 1) in differences between tumor and inflammation in the same subcutaneous location, 2) inflammations over different body locations, and 3) in differences between tumor and brown fat. However, such approach failed to differentiate inflammation in one location and tumor in another?Subcutaneous inflammation without tumor, in situ inflammation and spontaneous inflammation?. For kinetic analysis, the quantitative influx uptake parameters?Ki? were calculated using dynamic PET images and compartmental modeling. And kinetic analysis succeeded in differentiation of tumor and inflammations as well as tumor and brown fats. Moreover, dynamic PET also showed difference in time activity between inflammations and tumors and over locations. Therefore, further validation is necessary for the use of subcutaneous counterpart as the replacement of in situ inflammation in animal experiments as our results implied significant difference of inflammations over different body locations.Last chapter summarized the findings for our current work and discussed plans for future additional researches. Compared semi-quantitative SUVmax based on static PET imaging, quantitative parameters with dynamic PET imaging and compartmental modeling can differentiate malignancies from inflammation and Brown Adipose Tissue more accurately and sensitively. Therefore false-positivity can be minimized. At last, research plans and prospective were discussed for future studies.