An artificial intelligence method using FDG PET to predict treatment outcome in diffuse large B cell lymphoma patients.

ABSTRACT: Convolutional neural networks (CNNs) may improve response prediction in diffuse large B-cell lymphoma (DLBCL). The aim of this study was to investigate the feasibility of a CNN using maximum intensity projection (MIP) images from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) baseline scans to predict the probability of time-to-progression (TTP) within 2 years and compare it with the International Prognostic Index (IPI), i.e. a clinically used score. 296 DLBCL ¹⁸F-FDG PET/CT baseline scans collected from a prospective clinical trial (HOVON-84) were analysed. Cross-validation was performed using coronal and sagittal MIPs. An external dataset (340 DLBCL patients) was used to validate the model. Association between the probabilities, metab

SUBMITTER: Ferrandez MC 

PROVIDER: S-EPMC10423266 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature