A prognostic model integrating PET-derived metrics and image texture analyses with clinical risk factors from GOYA

eJHaem

article

Lale Kostakoglu¹  Federico Dalmasso²  Paola Berchialla³  Larry A. Pierce⁴  Umberto Vitolo⁵  Maurizio Martelli⁶  Laurie H. Sehn⁷  Marek Trněný⁸  Tina G. Nielsen⁹  Christopher R. Bolen^1,10  Deniz Sahin⁹  Calvin Lee^1,10  Tarec Christoffer El-Galaly⁹  Federico Mattiello⁹  Paul E. Kinahan⁴  Stephane Chauvie³ 

[1] Department of Radiology and Medical Imaging, University of Virginia;Medical Physics Division, Santa Croce e Carle Hospital;Department of Clinical and Biological Sciences, University of Turin;Department of Radiology, University of Washington;Multidisciplinary Oncology Outpatient Clinic, Candiolo Cancer Institute;Hematology, Department of Translational and Precision Medicine, Sapienza University;BC Cancer Center for Lymphoid Cancer and the University of British Columbia;1st Faculty of Medicine, Charles University General Hospital;F. Hoffmann-La Roche Ltd;Genentech, Inc.;Department of Hematology, Aalborg University Hospital

关键词: diffuse large B-cell lymphoma;    imaging;    lymphoid malignancies;    quantitative PET;    radiomics;   

DOI  :  10.1002/jha2.421

来源: Wiley

【 摘 要 】

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.

【 授权许可】

Unknown