ESTRO 2020 Abstract Book

S824 ESTRO 2020

Results The median OS calculated from diagnosis was 21 months for patients without metastases and 13 months for patients with metastases. Higher LDH (p<0.001) and NSE (p<0.001) correlated with worse OS. The expression of synaptophysin correlated with a better OS (HR 0.529 95% CI 0.299-0.937, p=0.029). The expression of TTF-1(HR 0.282, 95% CI: 0.116- 0.684, p=0.005) and a lower GLUT-1 H-score (median = 50, HR: 0.524, 95% CI: 0.273-1.003, p=0.05) correlated with a better PFS. Chromogranin A correlated with the presence of cerebral metastases (OR 0.268, 95% CI: 0.087-0.826, p=0.02). Our radiomics analysis did not reveal a single texture feature that was highly correlated with overall survival or progression free survival. Correlation coefficients ranged between -0.35 and 0.45 for overall survival and between -0.27 and 0.49 for progression free survival. Conclusion In this analysis the expression of synaptophysin correlated with a better OS. This marker might be considered for decision making such as treatment intensification in synaptophysin-negative patients. No radiomic features correlated with any of the markers or the outcome of the treatment. PO-1526 a radiomics signature to predict response of chemoradiotherapy in esophagus squamous cell carcinoma B. Li 1 , Q. Cao 2 , D. An 3 1 Shandong Cancer Hospital and Institute, Department of radiation oncology, Jinan, China ; 2 Southeast University, Laboratory of Image Science and Technology, Nanjing, China ; 3 Shandong University, Cheeloo college of medicine, JInan, China Purpose or Objective To investigate the potential image markers for early prediction of treatment response on thoracic esophagus squamous cell carcinoma (ESCC) treated with concurrent chemoradiotherapy (CCRT). Material and Methods 159 thoracic ESCC patients enrolled from two institutions were divided into training and validation sets. A total of 944 radiomics features were extracted from pretreatment 18 F-FDG PET image sequences. We first performed the inter-observer test (two delineate methods) and limma on 10 pairs of patients (responders vs. nonresponders) to identify repeatably differentially expressed features (DEFs). Then, the least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to construct a treatment response related radiomics signature. At last, the performance of this signature was assessed in both sets with receiver operating characteristic (ROC) curves and Kaplan-Meier (KM) analysis. Results After the inter-observer test, 691 features under two delineate methods were left ( p <0.05, ICC >0.9). 61 of them from limma were regarded as DEFs and entered the LASSO model. The 7-feature radiomics signature was significantly associated with treatment response ( p <0.001 in the training set and p =0.026 in the validation set) and achieved an area under curve (AUC) value of 0.844 and 0.835 respectively. Delong test of two ROCs showed no significant difference ( p =0.918). The cut-off value of radiomics signature could successfully divide patients into high-risk and low-risk groups in both sets. Conclusion That study indicated that the proposed radiomics signature could be a useful image marker to predict the therapeutic response of thoracic ESCC patients treated with CCRT.

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).