ESTRO 2020 Abstract book

S877 ESTRO 2020

Results The incidence of lymph node metastasis (LNMs) was 58% in both cohorts. The AUCs of the clinical, radiomics and combined models were 0.79, 0.69 and 0.82 in the developmental cohort, and 0.65, 0.63 and 0.69 in the external validation cohort, with good calibration demonstrated. The ROC plots are shown in Figure 2. In comparison, the AUC of current cN-stage in development and validation cohorts was 0.60 and 0.66, respectively. For overall survival, the combined clinical and radiomics model achieved the best performance to discriminate the external validation cohort (X 2 6.08, CI = 0.60, df 1, p = 0.01).

Conclusion By embedding FAIR principles in radiomics computations, transparency of radiomics studies is augmented. The Radiomics Ontology represents a natural extension of the IBSI effort, by providing a universal language for reporting radiomics data and metadata. Finally, this ontology represents the backbone for including radiomics to distributed learning networks. PO-1532 Prediction of Lymph Node Metastases via PET Radiomics of Primary Tumour in Esophageal Adenocarcinoma Z. Shi 1 , C. Zhang 1 , P. Kalendralis 1 , P. Whybra 2 , C. Parkinson 2 , M. Berbee 1 , E. Spezi 2 , A. Roberts 3 , A. Christian 4 , T. Crosby 5 , A. Dekker 1 , L. Wee 1 , K.G. Foley 6 1 GROW School for Oncology and Developmental Biology- Maastricht University Medical Centre+, Department of Radiation Oncology MAASTRO CLINIC, Maastricht, The Netherlands ; 2 Cardiff University, School of Engineering, Cardiff, United Kingdom ; 3 University Hospital of Wales, Department of Radiology, Cardiff, United Kingdom ; 4 University Hospital of Wales, Department of Pathology, Cardiff, United Kingdom ; 5 Velindre Cancer Centre, department of clinical oncology, Cadiff, United Kingdom ; 6 Velindre Cancer Centre, department of clinical radiology, Cadiff, United Kingdom Purpose or Objective To improve clinical lymph node staging (cN-stage) in esophageal adenocarcinoma by developing and externally validating three prediction models with 1) clinical variables 2) positron emission tomography (PET) radiomics and 3) a combined clinical and radiomics model. Material and Methods Consecutive patients with fluorodeoxyglucose (FDG) avid tumours treated with neo-adjuvant therapy between 2010 and 2016 in two international centres (n=130 and n=60, respectively) were included. Four clinical variables (age, gender, clinical T-stage and tumour regression grade) and PET radiomics derived from the primary tumour were used to construct the three models. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, area under curve (AUC), discrimination and calibration were calculated for each model. The prognostic significance was also assessed.

Conclusion Accurate diagnosis of LNMs is crucial for predicting prognosis and guiding treatment decisions. Despite obtaining signal for improved prediction in the development cohort, the models using PET radiomics derived from the primary tumour were not fully replicated in an external validation cohort. PO-1533 Feature selection methods improve accuracy in radiation toxicity prediction for lung cancer F. Núñez-Benjumea 1 , S. González-García 1 , J. Moreno- Conde 1 , A. Moreno-Conde 2 , J. Cacicedo 3 , B.D. Delgado 4 , M. Borrego 4 , S. Perez Luque 4 , C.L. Parra-Calderón 1 , J.L. Lopez Guerra 5 1 Institute of Biomedicine of Seville- IBIS / Virgen del Rocío University Hospital / CSIC / University of Seville, Biomedical Informatics- Biomedical Engineering and Health Economics, Seville, Spain ; 2 Virgen Macarena University Hospital, Biomedical Informatics- Biomedical Engineering and Health Economics, Seville, Spain ; 3 Cruces University Hospital, Radiation Oncology, Barakaldo, Spain ; 4 Virgen del Rocío University Hospital, Radiation Oncology, Seville, Spain ; 5 Virgen del Rocío University Hospital, Radiation oncology, Sevilla, Spain Purpose or Objective The project named S32 aims to develop a Learning Health System to predict radiation-induced toxicity. This tool will allow physicians know in advanced the effects of radiation therapy in lung cancer patients. Material and Methods The lung cancer dataset includes clinical information of 596 patients (training dataset) treated with thoracic radiation therapy (RT) from 2013 to 05/2019 and 46 patients (validation dataset) treated from 06/2019 to 09/2019 at 2 Institutions. For each patient, 59 variables