ESTRO 2023 - Abstract Book

S1886

Digital Posters

ESTRO 2023

Our results highlight the potential benefit of mathematical models including clinical, radiological and radiomics variables for pathological features prediction in prostate cancer. These results are of considerable interest to inform the clinical decision-making process and can provide valuable information for personalizing therapy, helping identify the correct stage of the disease and guiding the clinical course.

PO-2102 CT-based radiomics for outcome prediction in oropharyngeal cancer patients treated with curative RT

S. Volpe 1 , A. Gaeta 2 , F. Colombo 1 , M. Zaffaroni 1 , M.G. Vincini 1 , M. Pepa 1 , L.J. Isaksson 1 , I. Turturici 1 , A. Casbarra 3 , G. Marvaso 1 , A.M. Ferrari 1 , G. Cammarata 2 , R. Santamaria 1 , S. Raimondi 2 , F. Botta 4 , M. Ansarin 5 , M. Cremonesi 6 , R. Orecchia 7 , D. Alterio 1 , B.A. Jereczek-Fossa 1 1 Istituto Europeo di Oncologia IRCCS, Department of Radiation Oncology, Milan, Italy; 2 Istituto Europeo di Oncologia IRCCS, Department of Experimental Oncology, Milan, Italy; 3 Istituto Europeo di Oncologia IRCCS, Department of Radiation Oncolgy, Milan, Italy; 4 Istituto Europeo di Oncologia IRCCS, Department of Medical Physics, Milan, Italy; 5 Istituto Europeo di Oncologia IRCCS, Division of Otolaryngology and Head and Neck Surgery, Milan, Italy; 6 Istituto Europeo di Oncologia IRCCS, Radiation Research Unit, Milan, Italy; 7 Istituto Europeo di Oncologia IRCCS, Scientific Directorate, Milan, Italy Purpose or Objective To assess whether CT-based radiomics could improve the prediction of overall survival (OS) and local progression free survival (LPFS) in patients with oropharyngeal cancer (OPC) treated with curative-intent IMRT. Materials and Methods Consecutive OPC patients with radiologically detectable primary tumor treated between 2005 and 2021 were included. Analyzed clinical variables included gender, age, smoking history, staging, subsite, HPV status and blood counts (e.g. baseline hemoglobin, platelets, neutrophil/lymphocyte ratio and lymphocyte/monocyte ratio). Radiomic features were extracted from manually-segmented GTVs of the primary tumor using Pyradiomics. For the analysis, univariate proportional hazard and penalized Cox regression models were applied to clinical and radiomic features, respectively; a radiomic score (RS) was obtained to stratify patients. Lasso regression was applied to select potentially significant radiomic features based on the Spearman correlation coefficient. A 10-fold cross-validation with 500 bootstrapping repetitions was performed to minimize bias and overfitting. As many as 3 and 5 features were used for OS and LPFS prediction, respectively. Three models, namely clinical, radiomic and clinical-radiomic models were built including, respectively, clinical variables only, radiomic variables only and both. For each model, C-index and respective CIs were obtained from the bootstrap estimate. The likelihood-ratio test was used to compare the performances of the models, with a p-value<0.05 indicating that radiomics led to an improvement in predictivity. Results One-hundred five patients, predominately male, were included in the analysis. Median age was 59 (IQR: 52-66) years, and stage IVA was the most represented (70%). HPV status was positive in 63, negative in 7 and missing in 35 patients. Median follow-up was 6.3 (IQR: 5.5-7.9) years. The calculation of the RS enabled to successfully stratify patients according to both OS (p<0.0001) and LPFS (0.0002) (Fig1). The model including both clinical and radiomic variables outperformed the other two, with a C-index of 0.82 [CI: 0.80-0.84] for OS and 0.86 [CI: 0.86-0.89] for LPFS.