ESTRO 2020 Abstract Book

S851 ESTRO 2020

based radiomic features did not improve performance of radiomic models in either of the investigated scenarios. Considering only the largest lesion instead of the weighted mean of all lesions resulted in AUC scores smaller by 0.16 on average.

Conclusion These preliminary results lead to define a group of robust dosomic features potentially useful for improve model prediction. Robustness evaluation of the second level features will be performed, as well as the dependency on electron density map slice thickness and other clinical/planning parameters. PO-1571 Radiomics for prediction of metastatic melanoma patient survival after immunotherapy H. Gabrys 1 , L. Basler 1 , S. Hogan 2 , M. Pavic 1 , M. Bogowicz 1 , D. Vuong 1 , S. Tanadini-Lang 1 , R. Förster 1 , M. Huellner 3 , R. Dummer 2 , M. Guckenberger 1 , M.P. Levesque 2 1 University Hospital Zurich, Department of Radiation Oncology, Zurich, Switzerland ; 2 University Hospital Zurich, Department of Dermatology, Zurich, Switzerland ; 3 University Hospital Zurich, Department of Nuclear Medicine, Zurich, Switzerland Purpose or Objective Immune checkpoint inhibition has achieved substantial improvements in survival of metastatic melanoma patients; however, reliable biomarkers for patient selection are lacking. This study aims to assess the predictive potential of PET/CT-based radiomics for modeling patient overall survival (OS) in metastatic melanoma patients receiving checkpoint inhibitors. Material and Methods A retrospective single-institution cohort of 112 patients with metastatic melanoma was the basis of this study. Patients were treated with either single-checkpoint- inhibition using anti-PD-1-antibodies or dual-checkpoint- inhibition in combination with anti-CTLA-4-antibodies. PET/CT imaging was done before the treatment (TP0) and at 3 months after the treatment initiation (TP1). All individual metastases (n=716 over all patients) were segmented on all images for calculation of radiomic features (n=172) describing shape, intensity, and texture of the metastases. To perform the analysis on a patient level, radiomic features extracted from multiple lesions were aggregated via weighted arithmetic mean, where the weight corresponded to a lesion volume. Logistic regression models were built using the data available at TP0 and delta-radiomic features calculated between TP0 and TP1. The performance metric was the area under the receiver operating characteristic curve (AUC). The generalization performance of the models was estimated with a nested cross-validation (inner loop: 10 times repeated 10-fold cross-validation, outer loop: 10 times repeated 5-fold cross-validation). Model calibration has been evaluated with calibration curves and the Brier score. The radiomic models were benchmarked against models based only on metastases volume. Results The OS at 12 and 36 months was 84% and 55%, respectively (Fig. 1). Prognostic models based on radiomic features achieved AUCs of 0.87 +/- 0.08 and 0.74 +/- 0.13 at 12 and 36 months, respectively. Volume-based models performed slightly worse with AUCs of 0.83 +/- 0.15 and 0.71 +/- 0.13, at 12 and 36 months, respectively. Nevertheless, the radiomics models were more stable and better calibrated than the volume-based models (Fig. 2). Inclusion of PET-

Figure 1. Overall survival: 84% at 12 months and 55% at 36 months. Figure 2. Probability calibration curves. Brier scores: 0.06 (radiomic model 12m), 0.10 (volumetric model 12m), 0.13 (radiomic model 36m), 0.18 (volumetric model 36m). Conclusion Radiomic features, considering all metastases in one patient from baseline imaging and follow-up imaging at 3 months, allow for accurate prediction of survival in metastatic melanoma patients at 12 and 36 months after treatment with immune checkpoint-inhibitors. PO-1572 Use of intravoxel incoherent motion (IVIM) MRI for predicting dysphagia in oropharyngeal carcinoma M. Maddalo 1 , L. Altabella 2 , L. Pegurri 1 , A. Guerini 3 , G. Peretto 3 , A. Alghisi 3 , O. Turla 3 , A. Guaineri 3 , G. Costantino 3 , L. Spiazzi 4 , L. Costa 1 , C. Mozzetti 3 , N. Pasinetti 5 , M. Buglione 3 1 Spedali Civili di Brescia, Department of Radiation Oncology, Brescia, Italy ; 2 Azienda Ospedaliero - Universitaria di Parma, Department of Medical Physics, Parma, Italy ; 3 Unversità degli Studi di Brescia - ASST Spedali Civili di Brescia, Department of Radiation Oncology, Brescia, Italy ; 4 Spedali Civili di Brescia, Department of Medical Physics, Brescia, Italy ; 5 ASST Valcamonica, Depertment of Radiation Oncology, Esine BS, Italy Purpose or Objective The IVIM technique is an advanced MR imaging method that allows to obtain informations on diffusion, perfusion and pseudo-diffusion. This work is a preliminary analysis of the data of a prospective protocol on patients affected by OPC treated with radical radio-chemotherapy that aims to identify an IVIM signal pattern predictive of 1) early response during treatment and early changes to organs at