Abstract Book

S1158

ESTRO 37

Conclusion The obtained results show that Machine Learning coupled with radiomics has a great potential in distinguishing, before the treatment, responders with volume reduction from responders without volume reduction to radiosurgery. EP-2106 Simulation of oxygen and hypoxic PET-tracer distributions in 3D vascular architectures of tumours I. Paredes Cisneros 1,2,3,4 , I. Espinoza 4 , D. Nolte 5 , C. Bertoglio 5,6 , C. Karger 1,3 , A. Gago-Arias 4 1 German Cancer Research Center DKFZ, Medical Physics in Radiation Oncology, Heidelberg, Germany 2 Heidelberg University, Medical Faculty Heidelberg, Heidelberg, Germany 3 Heidelberg Institute for Radiation Oncology HIRO, National Center for Radiation Research in Oncology NCRO, Heidelberg, Germany 4 Pontificia Universidad Católica de Chile, Institute of Physics, Santiago, Chile 5 University of Chile, Center for Mathematical Modeling, Santiago, Chile 6 University of Groningen, Johann Bernoulli Institute, Groningen, The Netherlands Purpose or Objective Tumour response to ionizing radiation exhibits a strong dependence on oxygenation due to the increased radioresistance of hypoxic cells. Hypoxic PET-tracers are used to assess tumour oxygenation prior and during radiotherapy treatments in a non-invasive way. Previous studies have simulated the oxygen distribution and the resulting voxel-based time-activity curves (TAC) of the tracer in tissue sections. However, no simulations on realistic three-dimensional (3D) vascular architectures have been studied yet. This study aims to develop a tool to simulate the voxel-based oxygen and FMISO-PET dynamics for realistic 3D vascular architectures. Material and Methods Oxygen and FMISO-PET dynamic distributions were calculated by solving reaction-diffusion equations, using a 3D finite element method. 3D vascular architectures and the corresponding meshes were constructed using the open source meshing software Gmsh . In this framework, vessels were modeled as cylinders and constitute the source of oxygen (partial oxygen pressure (pO2) of 40 mmHg). For the simulation of dynamics of the FMISO distribution, an arterial input function was used. The underlying partial differential equations were solved using the open source computing platform FEniCS . The vessel geometry consisted of three perpendicular intersecting vessels, of radius 10 µm and 300 µm long. First, the stationary oxygen distribution was simulated. This distribution was then used as input to solve the time-dependent 3D FMISO distribution. The impact on FMISO dynamics of maximum oxygen consumption (gmax) and capillary permeability to FMISO (L T ) was investigated. TAC were calculated from the total FMISO concentration in the volume and the input function. Results Figure 1 shows pO2 and bound FMISO concentration (Cb) for the given geometry. For distances larger than approximately 180 µm from vessels, cells were considered necrotic and no binding of FMISO occured.

Figure 1. pO2 and Cb distributions at 90 minutes post injection. For the gmax values considered, very small TAC variations were observed. However, increased gmax consistently caused steeper oxygen profiles and binding of FMISO closer to the vessels. Figure 2 shows the input function and TAC calculated with different L T values . Larger overall activity was observed in TAC for increased L T values.

values (corrected by F-

Figure 2. TAC for different L T

18 radioactive decay) and input function. Conclusion

A framework to study the spatiotemporal behaviour of TAC in 3D vascular architectures has been developed. This allows relating macroscopically measured TAC with microscopic vascular parameters, such as vascular fraction, hypoxic fraction, vessel permeability and binding parameters. First results in simple geometries exhibit consistent results. More complex and realistic vascular architectures may be obtained using the software VascuSynth, which generates vascular trees based on random seeds and physical laws. The application of the developed method may easily be extended to these vasculatures. EP-2107 CT-based Radiomics Features Predict Brain Metastasis in Small Cell Lung Cancer Q. Wen 1 , Z. Jian 2 , W. Linlin 3 , M. Xue 3 , Y. Yong 2 , S. Xindong 3 , Y. Jinming 3 1 Shandong Cancer Hospital affiliated to Shandong University, Radiation Oncology, Jinan, China 2 Shandong Cancer Hospital Affiliated to Shandong University- Shandong University, Radiation Physics, Jinan, China 3 Shandong Cancer Hospital Affiliated to Shandong University- Shandong University, Radiation Oncology, Jinan, China Purpose or Objective In this study, we retrospectively evaluated the value of pre-treatment computed tomography (CT)-based radiomics features in prediction brain metastasis (BM) for small cell lung cancer (SCLC) patients. Material and Methods Totally, 129 patients were enrolled in this study. Clinical and pathological features were obtained from medical