"Jožef Stefan" Institute
Reactor physics department (F8)
Jamova cesta 39
Telephone: +386 1 588 5450
Fax: +386 1 588 5377
Department is located at
Reactor Centre Podgorica.
Head Assistant Prof. Dr. Luka Snoj.
Figure 1: Series of first two images show the cellular proliferation, imaged with FLT PET/CT before the radiotherapy and after radiotherapy. Third image has radiation field overlayed over the cellular proliferation image. Series of images clearly show how the cellular proliferation disappears in the irradiated bone marrow.
Medical Physics GroupMedical Physics is an applied branch of physics concerned with the application of the concepts and methods of physics to the diagnosis and treatment of disease. Medical Physics research in our group, strengthened with the research of our collaborators at the Department of Medical Physics at University of Wisconsin – Madison and our clinical colleagues at the University of Wisconsin Carbone Cancer Center, is focused into image-guided cancer* therapy.
Within this general area, our research is grouped into five focused areas:
LIU, G., JERAJ, Robert, VANDERHOEK, M., PERLMAN, S., KOLESAR, Jill M., HARRISON, M.R., SIMONČIČ, Urban, EICKHOFF, J.C., CARMICHAEL, L., CHAO, B., MARNOCHA, R., IVY, P., WILDING, G. Pharmacodynamic Study Using FLT PET/CT in Patients with Renal Cell Cancer and Other Solid Malignancies Treated with Sunitinib Malate. Clin Cancer Res, 17(24), 2011, 7 str., doi: 10.1158/1078-0432.CCR-11-1677. [COBISS.SI-ID 25278759]
Beside head of the Medical Physics Group Dr. Robert Jeraj, members of this group are Dr. Urban Simončič and Damijan Valentinuzzi.
Dr. Urban Simončič working area: Kinetic analysis of PET images – method optimization and clinical applicationsPositron emission tomography (PET) is already well established imaging technique in modern oncology, but there is still an unresolved problem of PET image quantification. Quantitative measures can be extracted from PET data by kinetic analysis methods or uptake normalizations. Kinetic analysis is superior to the uptake normalization based PET quantification methods in the richness and the specificity of the results. However, kinetic analysis acceptance in clinical settings is still limited due to the complexity of data acquisition and its sensitivity to the imaging noise. In order to address this issue, our work is focused into improved kinetic analysis methodology comprising a robust PET imaging protocol involving kinetic analysis that is schematically presented below.
Figure 2: The schematic representation of the robust PET imaging protocol involving kinetic analysis. An overall output of the robust PET imaging protocol involving kinetic comprises the best possible kinetic parameter estimates and a precise characterization of their uncertainties. To obtain these estimates, concurrent optimization of the kinetic analysis method, image acquisition method and image reconstruction method is necessary. The optimization procedure is iterative, with subsequent improvement of simulation accuracy using the clinical data.
The developed methodology is then utilized in clinical settings. There are two main types of clinical applications for molecular imaging and kinetic analysis: 1) using the imaging data for individual’s treatment assessment and guidance, 2) using the imaging data in clinical studies with the aim of discovering population behavior during the treatment.
Figure 3: Here is an example of FLT PET/CT treatment response assessment for the patients treated with sunitinib malate. Upper row shows the treatment response quantification with the optimized kinetic analysis, while the lower image shows response quantification with a standardized uptake value. The example shows that optimized kinetic analysis produces considerably different values for treatment response than the SUV. Superiority of optimized kinetic analysis (in term of better accuracy) can be tested with simulations.
Preliminary results assure the improvement in individual’s treatment assessment accuracy and the feasibility of more aggressive treatment interventions. However, estimation accuracy for population observables in clinical studies could be limited with the clinical data heterogeneity and lowering the individual’s treatment response uncertainty does not necessarily affect the population response uncertainty.
Currenty, we are investigating a multiscale computer model, which combines experimental data from clinical studies on a larger population of patients with data, specific for each patient, which we obtained by using advanced imaging techniques (PET/CT), capable of determining the heterogenity within the tumor (proliferation, hypoxia, metabolism). We believe that the lack of knowledge of each individual tumor's heterogenity is an important reasons, why some treatments do not end up as we expected.
Axitinib (Pfizer) is an example of an anti-angiogenic targeted drug, which is still undergoing the clinical trials, and which we would like to include in our simulation. The drug inhibitsthe so-called VEGF TKI receptors ("vascular endothelial growth factor - tyrosine kinase receptors"), which are responsible for stimulating the growth of new tumor blood vessels. We hope that by using our model, we will be able to determine the optimal dosage for each patient, predict clinical outcome and potential interactions when using Axitinb in combination therapies, for example in combination with radio- or chemotherapy. Finally, we would also like to test different hypotheses as to why sooner or later most tumors become resistive to anti-angiogenic drugs, which is currently one of the main problems.
Figure 4 (above): Simulation of the number of tumor cells during anti-angiogenic therapy (blue) and during one-week drug holiday (red).
(below left): - real FLT PET image of cellular proliferation in the tumor after one-week drug holiday.
(below right): - simulated FLT PET image of cellular proliferation in the tumor after one-week drug holiday.
08-Nov-2012 by email@example.com