ESTRO 37 Abstract book

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ESTRO 37

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.

brain metastasis groups in univariate analysis (CI=0.642, p=0.031). A multiple logistic regression model illustrated that adding radiomics features to a clinical model resulted in a significant improvement of predicting power, the AUC increased from 0.682 to 0.711 (P=0.023). Conclusion This study shown that radiomics features could capture useful information about tumor phenotype, and the model we discovered can be used to predict predictive brain metastasis through pre-treatment CT in SCLC patients. In our future work we focus on inner and external validations for this model. EP-2108 Robustness study on radiomic features in [18F]-FDG PET/CT and [18F]-FDG PET/MR D. Vuong 1 , M. Bogowicz 1 , M. Huellner 2 , P. Veit-Haibach 2 , N. Andratschke 1 , J. Unkelbach 1 , M. Guckenberger 1 , S. Tanadini-Lang 1 1 University Hospital Zurich, Radiation Oncology, Zürich, Switzerland 2 University Hospital Zurich, Nuclear Medicine, Zürich, Switzerland Purpose or Objective Radiomics is a promising tool for identification of new prognostic biomarkers. However, image reconstruction settings and test-retest variability may influence the absolute values of radiomic features. Unstable radiomic features cannot be used as reliable biomarkers. PET/MR is becoming more available and often replaces PET/CT. The aim of this study was to quantify to what extend [18F]-FDG PET/CT radiomics models can be transferred to PET/MR. Material and Methods Nine patients with non-small cell lung cancer underwent first an [18F]-FDG PET/MR scan (SIGNA PET/MR, GE Healthcare, Waukesha) followed by an [18F]-FDG PET/CT scan (Discovery 690, GE Healthcare) with a delay time of 38 min ± 5 min. All patients had one single FDG injection for both scans. The primary tumors were segmented independently on the PET dataset from PET/CT and PET/MR with two semi-automated methods (gradient- based and threshold-based). The resolution of the scans was 2.73x2.73x3.27 mm for the PET/CT and 2.34x2.34x2.78 mm for the PET/MR. Resampling was performed to 3.27 mm. In total, 1355 radiomic features were calculated, i.e. shape (n=18), intensity (n=17), texture (n=136) and wavelets (n=1184). The intra-class correlation coefficient (ICC(3,2)) was calculated to compare the radiomic features in both imaging modalities. An ICC larger than 0.9 was considered stable among both types of PET scans. An average linkage hierarchical clustering was performed to identify classes of stable and uncorrelated features with a cut-off value of 0.7. Results

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 records and database. The phenotype of the primary tumor was quantified on pre-treatment CT scans by 435 radiomics features, which was extracted from segmented volumes of each tumor. 62 of these, which were considered stable and independent features, were included in this analysis. Univariate and multivariate analysis was performed to evaluate radiomics performance using the concordance index (CI) Results High level of NSE （ OR=1.945 ， P=0.014, differentiation （ OR=1.654 ， p<0.001 ） and smoking status (OR=1.452 ， p=0.032 ） were independent clinical and pathological factors for brain metastasis prediction. Statistically significant differences were found in 21 radiomics features between brain metastasis and non-