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

ABSTRACT: 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.

SUBMITTER: Kostakoglu L

PROVIDER: S-EPMC9175666 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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

Kostakoglu Lale L Dalmasso Federico F Berchialla Paola P Pierce Larry A LA Vitolo Umberto U Martelli Maurizio M Sehn Laurie H LH Trněný Marek M Nielsen Tina G TG Bolen Christopher R CR Sahin Deniz D Lee Calvin C El-Galaly Tarec Christoffer TC Mattiello Federico F Kinahan Paul E PE Chauvie Stephane S

EJHaem 20220324 2

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 ( ...[more]

PMID: 35846039

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Dataset Information

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

Publications

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

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