Shortened acquisition protocols for the quantitative assessment of the 2-tissue-compartment model using dynamic PET/CT18F-FDG studies

Journal of Nuclear Medicine

Volumn 52, Issue 3, 2011, Pages 379-385

  Strauss, Ludwig G ^a   Pan, Leyun ^a   Cheng, Caixia ^a   Haberkorn, Uwe ^a,b   Dimitrakopoulou Strauss, Antonia ^a  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

18F FDG; Compartment modeling; PET; Quantification

Indexed keywords

FLUORODEOXYGLUCOSE F 18;

ARTICLE; COMPARTMENT MODEL; COMPUTER ASSISTED EMISSION TOMOGRAPHY; COMPUTER PROGRAM; CORRELATION COEFFICIENT; IMAGE PROCESSING; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIOACTIVITY; SUPPORT VECTOR MACHINE; TWO TISSUE COMPARTMENT MODEL;

ALGORITHMS; COMPUTER SIMULATION; FLUORODEOXYGLUCOSE F18; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, BIOLOGICAL; NEOPLASMS; POSITRON-EMISSION TOMOGRAPHY; RADIOPHARMACEUTICALS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TIME FACTORS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 79952799351     PISSN: 01615505     EISSN: None     Source Type: Journal    
DOI: 10.2967/jnumed.110.079798     Document Type: Article

References (19)

1. Dimitrakopoulou-Strauss A, Strauss L. Quantitative studies using positron emission tomography (PET) for the diagnosis and therapy planning of oncological patients. Hell J Nucl Med. 2006;9:10-21.
2. Strauss LG, Conti PS. The applications of PET in clinical oncology. J Nucl Med. 1991;32:623-648.
3. Strauss LG, Klippel S, Pan L, Schönleben K, Haberkorn U, Dimitrakopoulou-Strauss A. Assessment of quantitative FDG PET data in primary colorectal tumours: which parameters are important with respect to tumour detection? Eur J Nucl Med Mol Imaging. 2007;34:868-877.
4. Vriens D, Visser EP, de Geus-Oei LF, Oyen WJG. Methodological considerations in quantification of oncological FDG PET studies. Eur J Nucl Med Mol Imaging. 2010;37:1408-1425.
5. Dimitrakopoulou-Strauss A, Hoffmann M, Bergner R, et al. Prediction of short-term survival in patients with advanced nonsmall cell lung cancer following chemotherapy based on 2-deoxy-2-[F-18]fluoro-D-glucose-positron emission tomography: a feasibility study. Mol Imaging Biol. 2007;9:308-317. (Pubitemid 47304781)
6. 18F-FDG PET kinetics in patients with metastatic colorectal carcinoma receiving FOLFOX chemotherapy. J Nucl Med. 2004;45:1480-1487.
7. Visser EP, Kienhorst LBE, de Geus-Oei LF, Oyen WJG. Shortened dynamic FDG-PET protocol to determine the glucose metabolic rate in non-small cell lung carcinoma. IEEE Nucl Sci Symp Conf Rec. 2008;4455-4458.
8. Pan L, Cheng C, Dimitrakopoulou-Strauss A, Haberkorn U, Strauss LG. Machine learning based kinetic modeling: a robust and reproducible solution for PET data analysis [abstract]. J Nucl Med. 2009;50(suppl 2):1427P.
9. Ohtake T, Kosaka N, Watanabe T, et al. Noninvasive method to obtain input function for measuring glucose utilization of thoracic and abdominal organs. J Nucl Med. 1991;32:1432-1438.
10. 18F-FDG PET studies for the differentiation of malignant and benign bone lesions. J Nucl Med. 2002;43:510-518.
11. 18F-FDG studies in patients with primary and recurrent soft-tissue sarcomas: impact on diagnosis and correlation with grading. J Nucl Med. 2001;42:713-720.
12. Dimitrakopoulou-Strauss A, Strauss LG, Egerer G, et al. Prediction of chemotherapy outcome in patients with metastatic soft tissue sarcomas based on dynamic FDG PET (dPET) and a multiparameter analysis. Eur J Nucl Med Mol Imaging. 2010;37:1481-1489.
13. 18F-FDG PET on the early prediction of therapy outcome in patients with high-risk soft-tissue sarcomas after neoadjuvant chemotherapy: a feasibility study. J Nucl Med. 2010;51:551-558.
14. 18F-FDG-PET for the evaluation of soft tissue sarcomas: relation to differential diagnosis, tumor grading and prediction of prognosis. Hell J Nucl Med. 2009;12:223-228.
15. 18F-FDG-PET in patients with malignant primary brain tumor. Nucl Med Commun. 2009;30:602-609.
16. Chen P-H, Lin C-J, Schölkopf B. A tutorial on n-support vector machines. Appl Stoch Models Bus Ind. 2005;21:111-136.
17. Ozer S, Langer DL, Liu X, et al. Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med Phys. 2010;37:1873-1883.
18. Sattlecker M, Bessant C, Smith J, Stone N. Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics. Analyst. 2010;135:895-901.
19. Jayasurya K, Fung G, Yu S, et al. Comparison of bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med Phys. 2010;37:1401-1407.