Image texture analysis (radiomics) uses radiographic images to quantify characteristics that may identify tumour heterogeneity and associated patient outcomes. Using fluoro-deoxy-glucose positron emission tomography/computed tomography (FDG-PET/CT)-derived data, including quantitative metrics, image texture analysis and other clinical risk factors, we aimed to develop a prognostic model that predicts survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL) from GOYA (NCT01287741). Image texture features and clinical risk factors were combined into a random forest model and compared with the international prognostic index (IPI) for DLBCL based on progression-free survival (PFS) and overall survival (OS) predictions. Baseline FDG-PET scans were available for 1263 patients, 832 patients of these were cell-of-origin (COO)-evaluable. Patients were stratified by IPI or radiomics features plus clinical risk factors into low-, intermediate- and high-risk groups. The random forest model with COO subgroups identified a clearer high-risk population (45% 2-year PFS [95% confidence interval (CI) 40%-52%]; 65% 2-year OS [95% CI 59%-71%]) than the IPI (58% 2-year PFS [95% CI 50%-67%]; 69% 2-year OS [95% CI 62%-77%]). This study confirms that standard clinical risk factors can be combined with PET-derived image texture features to provide an improved prognostic model predicting survival in untreated DLBCL.

1st Faculty of Medicine Charles University General Hospital Prague Czech Republic

BC Cancer Center for Lymphoid Cancer and the University of British Columbia Vancouver British Columbia Canada

Department of Clinical and Biological Sciences University of Turin Turin Italy

Department of Hematology Aalborg University Hospital Aalborg Denmark

Department of Radiology and Medical Imaging University of Virginia Charlottesville Virginia USA

Department of Radiology University of Washington Seattle Washington USA

F Hoffmann La Roche Ltd Basel Switzerland

Genentech Inc South San Francisco California USA

Hematology Department of Translational and Precision Medicine Sapienza University Rome Italy

Medical Physics Division Santa Croce e Carle Hospital Cuneo Italy

Multidisciplinary Oncology Outpatient Clinic Candiolo Cancer Institute Candiolo Italy

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