• The Korean Journal of Nuclear Medicine
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• v.34 no.4
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• pp.294-302
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• 2000

Purpose: To investigate the mechanisms related to F-18-FDG uptake by tumors, F-18-FDG accumulation was compared with glucose transporter-1 (Glut-1) expression and hexokinase activity in various human cancer cell lines. Materials and Methods: Human colon cancer (SNU-C2A, SNU-C4, SNU-C5), hepatocellular carcinoma (SNU-387, SNU-423, SNU-449), lung cancer (NCI-H522, NCI-H358, NCI-H1299), uterine cervical cancer (HeLa, HeLa 229, HeLa S3) and brain tumor (A172, Hs 683) cell lines were used. After 24 hr incubation of $5{\times}10^5$ cells, 37 kBq F-18-FDG was added and the uptake by cells at 10 min was measured using a gamma counter. Hexokinase activity was measured by continuous spectrophotometric rate determination. To measure mitochondrial hexokinase activity, mitochondrial fraction was separated by a high speed centrifuge. Immunohistochemical staining of Glut-1 was performed, and graded as 0, 1, 2, or 3 according to expression. Results: There was difference among F-18-FDG uptake, total and mitochondrial hexokinase activity, and Glut-1 expression with different cancer cell lines. The correlations of F-18-FDG with total hexokinase and mitochondrial hexokinase activity were low (r=0.27 and 0.26, respectively). Glut-1 expression showed a good correlation with F-18-FDG uptake (p=0.81, p=0.0015). Previously, we reported no correlation of F-18-FDG uptake with hexokinase activity in colon cancer cell lines. Thus, when colon cancer cells were excluded, F-18-FDG uptake showed higher correlation with total hexokinase and mitochondrial hexokinase activity (r=0.81, p=0.0027 and r=0.81, p=0.0049, respectively). Conclusion: Both Glut-1 expression and hexokinase activity were contributing factors related to F-18-FDG accumulation in human cancer cell lines. The relative contribution of Glut-1 expression and hexokinase activity, however, was different among different cancer cell types.

• The Journal of Korean Society for Radiation Therapy
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• v.30 no.1_2
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• pp.97-105
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• 2018

Purpose : Proton Therapy using Bragg-peak, because it has distinct characteristics in providing maximum dosage for tumor and minimal dosage for normal tissue, a medical imaging system that can quantify changes in patient position or treatment area is of paramount importance to the treatment of protons. The purpose of this research is to evaluate the usefulness of the algorithm by comparing the image matching through the set-up and in-house code through the existing dips program by producing a Matlab-based in-house registration code to determine the error value between dips and DRR to evaluate the accuracy of the existing treatment. Materials and Methods : Thirteen patients with brain tumors and head and neck cancer who received proton therapy were included in this study and used the DIPS Program System (Version 2.4.3, IBA, Belgium) for image comparison and the Eclipse Proton Planning System (Version 13.7, Varian, USA) for patient treatment planning. For Validation of the Registration method, a test image was artificially rotated and moved to match the existing image, and the initial set up image of DIPS program of existing set up process was image-matched with plan DRR, and the error value was obtained, and the usefulness of the algorithm was evaluated. Results : When the test image was moved 0.5, 1, and 10 cm in the left and right directions, the average error was 0.018 cm. When the test image was rotated counterclockwise by 1 and $10^{\circ}$, the error was $0.0011^{\circ}$. When the initial images of four patients were imaged, the mean error was 0.056, 0.044, and 0.053 cm in the order of x, y, and z, and 0.190 and $0.206^{\circ}$ in the order of rotation and pitch. When the final images of 13 patients were imaged, the mean differences were 0.062, 0.085, and 0.074 cm in the order of x, y, and z, and 0.120 cm as the vector value. Rotation and pitch were 0.171 and $0.174^{\circ}$, respectively. Conclusion : The Matlab-based In-house Registration code produced through this study showed accurate Image matching based on Intensity as well as the simple image as well as anatomical structure. Also, the Set-up error through the DIPS program of the existing treatment method showed a very slight difference, confirming the accuracy of the proton therapy. Future development of additional programs and future Intensity-based Matlab In-house code research will be necessary for future clinical applications.