ESTRO 2020 Abstract Book

S831 ESTRO 2020

processing methods. These features were mainly describing geometric aspects of the tumour rather than relationship/combination of grey levels. Out of the reproducible features, Spearman’s rank correlation coefficient identified 73 features robust to various pre- processing techniques. Figure1 shows an overview of the robustness of the texture features investigated in this work.

Conclusion The performed analysis focused on investigating the usability and usefulness of the proposed phantom, and it showed that Radiomik can be an useful tool to study Radiomic Feature reproducibility and repeatability. [1] Mackin D. et al. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol 2015. [2] Dinapoli N. et al, Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features. Proc of the 37th Annual International Conference of the IEEE (2015). PO-1537 Robustness analysis of standardized radiomic features extracted from T2-weighted MR images C. Piazzese 1 , P. Whybra 1 , C. Bernori 1 , B. Omar 1 , E. Spezi 1 1 Cardiff University, School of Engineering, Cardiff, United Kingdom Purpose or Objective Many studies have demonstrated the potential value of radiomics features as biomarkers to derive prognostic/predictive information. However, imaging features are strongly related to the modality used to computed them. While CT or PET are measured in absolute units with physical meaning, magnetic resonance (MR) images are expressed in arbitrary units and require several pre-processing steps to perform quantitative analyses. In this work, we assessed the robustness of MR-based standardized radiomics features when various pre- processing methods (normalization, quantization and T2-weighted MR images and radiotherapy volumes of 47 patients with soft-tissue sarcomas 1,2 were collected. Four methods were used to normalize the images: original grey levels, same maximum or same mean for all images and grey levels dynamics limited to µ±3σ. Grey levels range was then quantized to 8, 16, 32 and 64 bins and the follow interpolation schemes were tested: original resolution, trilinear isotopic resampling to 0.5, 0.8 and 1 mm. Each MR image was pre-processed with 64 combinations of normalization, quantization and interpolation. Radiomic features, in compliance with the IBSI protocol 3 , were automatically extracted using SPAARC 4 (SPAARC Pipeline for Automated Analysis and Radiomics Computing), a software developed in-house. Features reproducibility was assessed with intraclass correlation coefficient (ICC>0.90). Reproducible features were further investigated with Spearman’s rank correlation coefficient to assess patient ranking consistency. Results Out of the 302 standardized radiomic features computed, 121 showed to be reproducible when using various pre- interpolation) are used. Material and Methods

Figure1.Robustness of standardized radiomic features computed from T2-weighted MR images of patients with proven soft-tissue sarcomas. Conclusion We evaluated the robustness of different MR-based standardized radiomics features when various pre- processing schemes are used. Our results showed less than 100 features are both robust and reproducible. Further work is needed to investigate more the individual impact of these pre-process techniques on the discovered features.

References : 1.

Vallières M et al.A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.The Cancer Imaging Archive, 2015 Clark K et al.The Cancer Imaging Archive(TCIA):Maintaining and Operating a Public Information Repository.Journal of Digital Imag, 2013 Whybra P et al.Assessing radiomic feature robustness to interpolation in 18F-FDG PET imaging.Sci Rep, 2019 https://arxiv.org/abs/1612.07003v7

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PO-1538 Dose-surface-maps patterns of risk of urinary toxicity for prostate patients treated with HDRB boost

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PO-1539 Predictive modelling of late fibrosis in breast cancer radiotherapy M. Lizondo 1 , J. Fuentes-Raspall 2 , N. Jornet 3 , A. Latorre- Musoll 3 , P. Delgado-Tapia 3 , P. Carrasco 3 , J. Pérez-Alija 3 , P. Gallego 3 , P. Simón 3 , A. Ruiz-Martínez 3 , M. Adrià 3 , I. Valverde-Pascual 3 , M. Barceló 3 , N. Garcia 3 , M. Ribas 3 1 Institut de Recerca Hospital de la Santa Creu i Sant Pau, Servei de Radiofísica i Radioprotecció, Barcelona, Spain ; 2 Hospital de la Santa Creu i Sant Pau, Servei d'Oncologia Radioteràpica, Barcelona, Spain ; 3 Hospital de la Santa Creu i Sant Pau, Servei de Radiofísica i Radioprotecció, Barcelona, Spain Purpose or Objective Fibrosis is one of the late complications associated with radiotherapy treatment in breast cancer. In this work, different variables are analysed to develop a predictive model of radioinduced fibrosis with a view to further personalizing the treatment.