ESTRO 37 Abstract book

S1157

ESTRO 37

Physics Unit, Villejuif, France 6 Institut Universitaire du Cancer de Toulouse Oncopole, Nuclear Medicine Department, Toulouse, France 7 Hôpital Privé d'Antony, Nuclear Medicine Department, Antony, France 8 Institut Universitaire du Cancer de Toulouse Oncopole, Radiotherapy Department, Toulouse, France 9 INSERM U1214, Toulouse Neuro Imaging Center- Toulouse University, Toulouse, France 10 Gustave Roussy, Nuclear Medicine Department, Villejuif, France 11 INSERM, U1015, Villejuif, France 12 Gustave Roussy, Radiotherapy Department, Villejuif, France Purpose or Objective The role of quantitative PET imaging for predicting treatment outcome has been widely proven in the literature. A better knowledge of physical and physiological processes combined with more computing power lead to large improvements in acquisition/ reconstruction methods allowing a better precision of images. Despite a better image quality, they introduced strong biases in image quantization. Several accreditation protocols have been developed for standardized uptake value quantization, but are possibly not adapted to texture analysis. The aim of this study was to develop and validate a post-processing method permitting a standardization of retrospective PET images before radiomic analysis. Material and Methods A homogeneous Jaszczak and a Triple Line phantom (3 FDG-filled tubes, 1mm diameter) were acquired on 2 devices (PET1, PET2). 22 volumes of interest (VOI) were drawn in the Jaszczak on 9 acquisition/reconstruction protocols (PET1: N=8, PET2: N=1). An in-home Python code was developed for spatial resampling of images to a new voxel size of 2x2x2mm 3 . Spatial resolution was calculated on Triple Line and the set with the worse spatial resolution was chosen as reference. A gaussian filtering was applied to all other images to reach the reference spatial resolution (Fig.1). A multicenter clinical validation of the method was performed retrospectively in 4 institutions totalizing 137 patients, divided in 5 protocols on 3 PET devices: PET1.1, PET1.2, PET2.1, PET2.2, PET3 with respectively 29, 24, 24, 32, 28 patients. In each patient, we segmented a homogeneous hepatic VOI. Process of regridding, filtering and extraction of 39 radiomic features was applied both on phantom and patient data. A robust (non-variable) feature was defined by a non-significant difference between couples of devices according to Wilcoxon’s tests (p(feature PETa , feature PETb )>0.05).

iteration number and manufacturer’s post-filter width (p>0.05). 21 features were not robust (p<0.05). After standardization, range of SNR values was reduced (before: 9.2-27.7; after: 20.7-33.7). Filtering was highly efficient to harmonize 3 protocols on phantom data (p>0.05, 36 features) where the only varying parameter was manufacturer’s post-filter width. For more complex variations (PSF +/-), no benefit was observed. On original patient data, histogram features were robust (p>0.05). Differences in textural feature values were highly variable depending on PET scanner. Conclusion For the first time we proposed and validated on phantom data a simple method of harmonization. This method was more efficient to compensate for a difference induced by a variation of manufacturer post-filter width than a more complex parameter. For patient acquisitions, a combination of features calculated on original data and on filtered data appears necessary for a full set of robust features. EP-2101 Radiomic CT Features for Evaluation of PD- L1, CD8+TILs and Foxp3+TILs Expression in Stage I NSCLC Q. Wen 1 , W. Linlin 1 , Z. Jian 1 , B. Tong 1 , Y. Yong 1 , S. Xindong 1 , Y. Jinming 1 1 Shandong Cancer Hospital Affiliated to Shandong University- Shandong University, Radiation Oncology, Jinan, China Purpose or Objective Radiomic can quantify tumor phenotypic characteristics non-invasively and apply features algorithms to computed tomography (CT) images. In this study, we investigated the association between CT-based radiomic features and programmed death-ligand 1 (PD-L1), CD8+ tumor- infiltrating lymphocytes (TILs) and forkhead box protein 3+ (FOXP3+) TILs expression in patients with surgically resected stage I non-small cell lung cancer (NSCLC). Material and Methods A total of 96 patients with surgically resected NSCLC were included in the institutional review board-approved retrospective study and performed immunohistochemistry of PD-L1, CD8+TILs and FOXP3+ TILs. Clinical and demographic factors were obtained from medical records. 127 radiomic features coming from 5 different feature categories (tumor shape, intensity histogram, gray-level co-occurrence matrix, run length matrix, wavelet texture) were extracted from segmented volumes of each tumor. 48 out of 127 were considered as independent features and were performed in this analysis. Results In our univariate analysis, PD-L1 expression was significantly correlated with male sex (P = 0.003), squamous carcinoma (P < 0.001), never smoking statys (P = 0.015). And 8 radiomic features was detected a statistically significant difference between positive PD-L1 expression group and negative PD-L1 expression group in univariate analysis. A multiple logistic regression model illustrated that adding radiomic feature to clinical factors might improve the predictive value, due to the AUC increasing from 0.628 to 0.714 (P < 0.001). In addition, there was no radiomic features and clinical variables had significant correlated with CD8+ TILs and FOXP3+ TILs Radiomic features based on computed tomography of NSCLC could provide useful information regarding tumor phenotype, and the model was made up of radiomic features and clinical data could be predict the expression of PD-L1 non-invasively. expression. Conclusion

Results Spatial resolution of reference dataset evaluated on phantom acquisitions was 7.9mm. Before standardization, only histogram features were robust while varying PSF,