ESTRO 2020 Abstract book

S873 ESTRO 2020

receiving 5 Gy (V5), 20 Gy (V20) ,absolute lung volume spared from a 5 Gy dose (VS5) and PTV/Lung volume ratio (%) were analyzed. The normal tissue complication probability (NTCP) for radiation pneumonitis was calculated with the Lyman–Kutcher–Burman (LKB) model and the Poisson-linear-quadratic (LQ) model. And clinical factors, including age, gender, tumor site, chronic obstructive pulmonary disease (COPD), forced expiratory volume in 1 second (FEV1) of pre-radiotherapy were analyzed. Radiation pneumonitis was diagnosed by radiologist on computed tomography. Results Thirty patients (41%) developed Grade ≧ 2 radiation pneumonitis. These patients were given 3 dimensional conformal radiotherapy (3D-CRT). They were prescribed 60Gy in 30 fractions (2Gy/fr/day) as the standard RT dose fractionation regimen in the definitive management of stage III LA-NSCLC disease. The MLD (RP Grade 1 : 12.5 vs Grade 2 : 14.6 Gy, p<0.001), V5 (30.4 vs 38.7%, p=0.001), V20 (22.7 vs 28.7%, p<0.001), PTV/Lung volume ratio(14 vs 20.3%, p=0.003), NTCP LQ model(2.7 vs 5.7%, p<0.001),and NTCP LKB model(6.3 vs 14.8%, p<0.001) were low at Grade 1 radiation pneumonitis. There were no significant differences in VS5 (RP 2325 vs 1978 cc, p=0.07). A receiver operating characteristics (ROC) curve analysis confirmed 10% and 31.7% as the best cut-off value of NTCP LKB model (odds ratios : 9.05; 95% confidence interval, 2.0 to 40.4; P=0.004) and V20 (OR : 9.05; 95% CI, 2.5 to 81.9; P=0.003) respectively for Grade ≧ 2 radiation pneumonitis (area under the curve ; AUC=0.817; AUC=0.835). Sensitivity of the NTCP and V20 were 76.7%, 80% respectively. Specificity of the NTCP and V20 were 65.1%, 62.8% respectively. Conclusion NTCP LKB model and V20 were the best predictive factors of symptomatic radiation pneumonitis after CRT for LA- NSCLC. Multivariate models that also include clinical variables were slightly more predictable. In order to discuss the role of predictive factors, additional validation should be performed by using cut-off value of NTCP and V20 prospectively. PO-1524 FET-PET radiomics predicting outcome after re-irradiation in recurrent glioblastoma M. Carles 1,2 , M.M. Starke 3 , M. Mix 2,4 , H. Urbach 5 , T. Schimek-Jasch 3 , F. Eckert 6,7 , M. Niyazi 8,9 , D. Baltas 1,2 , A.L. Grosu 2,3 , I. Popp 2,3 1 University Medical Center Freiburg, Department of Radiation Oncology- Division of Medical Physics, Freiburg im Breisgau, Germany ; 2 German Cancer Consortium DKTK- German Cancer Research Center DKFZ, Partner Site Freiburg, Freiburg, Germany ; 3 University Medical Center Freiburg, Department of Radiation Oncology, Freiburg im Breisgau, Germany ; 4 University Medical Center Freiburg, Department of Nuclear Medicine, Freiburg im Breisgau, Germany ; 5 University Medical Center Freiburg, Department of Neuroradiology, Freiburg im Breisgau, Germany ; 6 University Hospital Tübingen, Department of Radiation Oncology, Tübingen, Germany ; 7 German Cancer Consortium DKTK- German Cancer Research Center DKFZ, Partner Site Tübingen, Tübingen, Germany ; 8 University Hospital- LMU Munich, Department of Radiation Oncology, Munich, Germany ; 9 Cerman Cancer Consortium DKTK- German Cancer Research Center DKFZ, Partner Site Munich, Munich, Germany Purpose or Objective Radiotherapy for primary and recurrent glioblastoma (GBM) is conventionally planned on anatomical magnetic resonance imaging (MRI), where the target volume is defined as the area of tumor-related gadolinium enhancement on a T1-weighted sequence (Gd-T1-MRI). Recent studies have indicated that O-(2-[ 18 F]fluoroethyl)- L-tyrosine (FET) positron emission tomography (PET) is

more specific than MRI and equally sensitive for tumor visualization. However, in recurrent GBM there is yet no clear evidence whether FET-PET radiomics can provide information on outcome prediction. The aim of this study was analyze the use of FET-PET radiomic image features in predicting time to tumor progression (TTP) and recurrence location for patients with recurrent GBM. Material and Methods 32 patients with histologically confirmed recurrent GBM and scheduled for re-irradiation were prospectively recruited. Both Gd-T1-MRI and FET-PET were performed before re-irradiation. PET target volumes were defined semi-automatically with a threshold of 1.8 times the standardized-uptake-value (SUV) of the background (2 volumes manually defined in cerebrum and cerebellum), while MRI volumes were contoured by experienced radiation oncologists. MRI and PET images were rigidly co- registered and 135 FET-PET image features (IF) were derived from both MRI- and PET-tumor volumes (V MRI , V PET ). Results Although V PET and V MRI volumes were comparable in size (Wilcoxon Rank Test), there was little agreement in the localization: Dice-Similarity-Coefficient=0.3±0.2 and Predictive-Positive-Value=0.4±0.3. All SUV parameters (SUV max , SUV peak , SUV mean , SUV min ) showed significant differences between V FET and V MRI when computed on PET. Small Zone Low Gray Level Emphasis (SZLGE) showed statistically significant predictive value for TTP. Additionally, two radiomics signatures could accurately predict TTP (p=0.001) and OS (p=0.03). SZLGE and the TTP radiomics signature predicted local versus distant recurrence location with areas-under-the-curve of 0.63 and 0.66, respectively. Conclusion Our findings suggest that FET-PET provides complementary information with respect to MRI and could contribute to the outcome assessment of patients with recurrent GBM scheduled for re-irradiation. PO-1525 Immunohistochemistry and radiomics features for survival prediction in small cell lung cancer E. Gkika 1 , M. Benndorf 2 , B. Oerther 2 , F. Mohammad 1 , S. Beitinger 1 , S. Adebahr 1 , M. Carles 1 , T. Schimek-Jasch 1 , C. Zamboglou 1 , C. Waller 3 , A. Grosu 1 , G.K.J. Kayser 4 1 Uniklinik Freiburg, Radiation Oncology, Freiburg, Germany ; 2 Uniklinik Freiburg, Radiology, Freiburg, Germany ; 3 Uniklinik Freiburg, Department of Hematology and Oncology, Freiburg, Germany ; 4 Uniklinik Freiburg, Pathology, Freiburg, Germany Purpose or Objective To evaluate the role of different immunohistochemical and radiomic features on the treatment outcome in patients with small cell lung cancer (SCLC). Material and Methods Consecutive patients with histologically proven SCLC (limited n=48 and extensive disease n=53) treated with radiotherapy at our department were included in the analysis. The expression of different immunohistochemical markers from the initial tissue biopsy such as CD56, CD44, chromogranin, synaptophysin, TTF-1, GLUT-1, Hif-1 alpha, PD-1 und PD-L1 and MIB-1/KI-67 as well as the LDH und NSE from the initial blood sample and CT radiomic texture features from a homogenous subgroup (n=31) of patients were correlated with the overall survival (OS) and progression free survival (PFS). Furthermore for CD44, Hif- 1 alpha and GLUT-1 H-Scores were generated for further analysis. For radiomic analysis a total of 72 a total of 72 texture features was obtained for each tumor (all features from the implemented classes gray level co-occurrence matrix, gray level run length matrix, first order histogram features and shape features were derived). Results