ESTRO 2020 Abstract book

S878 ESTRO 2020

of 574 patients with LAPC was included in this study. Radiation Therapy (RT) doses ranged from 24.0-61.2 Gy delivered in fractions of 1.25-8 Gy. GEM doses ranged from 0.2-3.6 g/m 2 , administered at individual doses of 40-600 mg/m 2 once or twice a week intravenously over 30 or 60 minutes, during the course of RT. Total tumour response was described using the Poisson model where the total biological effective dose (BED) of RCT was calculated as the sum of the biological effective dose of RT (BEDrt) and the equivalent biological effective dose of CH (BEDch). Considering only the cases irradiated with 50.4 Gy in 1.8 Gy fractions (BEDrt of 54.6 Gy), the variation in tumour response with the dose of GEM was assessed and modelled. Other dose prescriptions were used to assess the robustness of the models with BEDrt. Tumour response to RT was described by the Linear- Quadratic-Time model using the parameters: g of 3.1, a/β of 6.4, Tpot of 16 days and Tk of 21 days. Three models describing tumour response to CH were evaluated: 1) constant model: tumour response to CH was constant independent of the delivered dose of GEM; 2) linear model: response increased linearly with the dose of CH; 3) Hill model: common model used to describe cell survival to CH having as parameters: Emax (maximum effect), ED50 (dose causing a 50% maximum inhibition effect) and H (is the Hill exponent describing the slope of the curve). Model parameters were determined using a non-linear least square function. Results For the constant model and a BEDrt of 54.6 Gy resulted in an equivalent BEDch, of 64.7 Gy. This value did not change significantly for other BEDrt values. Overall tumour response to RCT increased with the dose of GEM up to a maximum of 50% for the total dose of 2.4 g/m 2 (400mg/m 2 /week). Thus, with the linear model an increase in BEDch of 5.1 Gy/g/m 2 was determined to which a baseline value of 57.4 Gy needs to be added. The estimated parameters of the Hill model are Emax = 0.2%, ED50 = 7.6x10 -5 g/m 2 and H = -0.48. Within the range of GEM doses up to 2.4 g/m 2 , both the linear model and the single Hill model showed a better association with clinical outcome (sum square error ~80) than the constant model (sum square error 149). Conclusion The linear model is the simplest approach to describe the variation of tumour response with the dose of GEM during RCT. The single Hill model, requiring the determination of three parameters, has shown to also be a good descriptor of the impact of RCT on tumour response. However, both these models fail considerably for high dose values of GEM as those used in CH as monotherapy. PO-1535 Machine Learning and Oversampling techniques to predict urinary toxicity after prostate cancer RT E. Mylona 1 , F. Filias 2 , M. Ibrahim 3 , S. Supiot 4 , N. Magne 5 , G. Crehange 6 , M. Hatt 3 , O. Acosta 1 , R. De Crevoisier 1 1 Université de Rennes 1, LTSI, Rennes CEDEX, France ; 2 University of Patras, Physics Departement, Patras, Greece ; 3 University of Brest, LaTIM, Brest, France ; 4 Centre Georges François Leclerc, Department of Radiation Oncology, Dijon, France ; 5 Lucien Neuwirth Cancer Institute, Radiotherapy Department, St Priest en Jarez, France ; 6 Institut de Cancérologie de l'Ouest, Medical Physics Department, Saint Herblain, France Purpose or Objective Machine learning methods have the potential to improve the prediction capabilities of radiation-induced toxicity models. However, there is currently no consensus on the best performing algorithms, particularly in the presence of low number of toxicity events. Oversampling techniques have been developed to tackle the lack of informative data. The goal of this work was to thoroughly compare several machine-learning strategies combined with

were collected (clinical, histological, and therapeutic variables [including dosimetric data], among others). The end points of analysis (development of acute esophagitis, cough, dyspnea or pneumonitis) were estimated and scored using Common Terminology Criteria for Adverse Events version 4.0. For the analysis, patients were grouped according grades (grade <2 vs grade ≥2). The cases from both groups were balanced by random elimination of samples from the majority group. The following forward selection techniques were applied for exploratory purposes: 1) Minimum Redundance-Maximum Relevance (mRMR); 2) Relief; 3) Random Forest (RF); and 4) Information Gain (IG). In addition, three subsetting methods were used for that purpose: 1) Correlated-based Feature Selection; 2) Boruta; and 3) Chi – squared filtering (ChiSq). A voting method (VM) which considers the features selected by two or more corresponding selection methods was applied. The characteristics selected in each case have been used to train the following classifiers: 1) Support vector machine (SVM); 2) Artificial Neural Network (ANN); 3) Linear Regression (LR); and 4) Naïve Bayes (NB). All selection of characteristics-classifiers combinations have been trained by 5-fold cross validation (80% of samples for training and 20% for testing) and their output were evaluated by means of the area under the ROC curve (AUC) for each RT-induced toxicity prediction. Furthermore, the same predictive models for toxicity have been trained using the common features used and recommended by 2 experienced radiation oncologists. Results For the training dataset, the rates of acute grade ≥2 esophagitis, cough, dyspnea or pneumonitis were 34% (N=204), 31% (N=123), 19% (N=115), and 28% (N=166), respectively. The models that showed a higher AUC for predicting acute esophagitis, cough, dyspnea or pneumonitis were VM+LR (AUC 0.88), RF+LR (AUC 0.86), Boruta +LR (AUC 0.79), and ChiSq +LR (AUC 0.90), respectively. The number of features used for each model was 58, 38, 16, and 31 respectively. When using the features (N=27) used and recommended by 2 experienced radiation oncologists the AUC for predicting acute esophagitis, cough, dyspnea or pneumonitis were 0.79, 0.64, 0.69, and 0.56, respectively. Conclusion Feature selection methods improve accuracy in toxicity prediction. This tool could help to enrich lung cancer guidelines and may be useful for guiding RT intensity in an individualized therapy. PO-1534 Tumour response modelling to radiochemotherapy with gemcitabine for pancreatic cancer B. Costa Ferreira 1,2,3 , J. Dias 3,4 , P. Mavroidis 5,6 , H. Rocha 3,4 , A. Gomes 1 1 Porto Polytecnic, School of Health, Porto, Portugal ; 2 Aveiro University, I3N Physics Department, Aveiro, Portugal ; 3 Coimbra University, Institute for Systems Engineering and Computers at Coimbra, Coimbra, Portugal ; 4 Coimbra University, CeBER and FEUC, Coimbra, Portugal ; 5 University of North Carolina, Department of Radiation Oncology, Chapel Hill, USA ; 6 Karolinska Institutet and Stockholm University, Division of Medical Radiation Physics, Stockholm, Sweden Purpose or Objective To assess tumour response as a function of the dose of gemcitabine (GEM) in concomitant radiochemotherapy (RCT) of locally advanced pancreatic cancer (LAPC). Three models describing this tumour response to chemotherapy (CH) were tested: the constant, the linear, and the Hill model. Material and Methods Clinical data was retrieved from the literature where details on patients, therapy and response to treatment were provided. The response to concomitant RCT with GEM