biostudies-literatureFerrandez MCHanarth FondsKWF Kankerbestrijding140https://www.ebi.ac.uk/biostudies/studies/S-EPMC12662970biostudies-literatureUnknown15(1)<h4>Background</h4>The aim of this study was to develop 3D convolutional neural networks (CNN) for the prediction of 2 years' time to progression using PET/CT baseline scans from diffuse large B-cell lymphoma (DLBCL) patients. The predictive performance of the 3D CNNs was compared to that of the International Prognostic Index (IPI) and a previously developed 2D CNN model using maximum intensity projections (MIP-CNN).<h4>Results</h4>1132 DLBCL patients were included from 7 independent clinical trials. Two 3D CNN models were developed using a training dataset of 636 patient scans merged from two trials, one CNN model trained on lesion-only PET (L-PET3D-CNN) and the second model trained on both lesion-only and whole body PET scans (LW-PET3D-CNN). The 3D models were cross-validated and performEJNMMI researchPredicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans.PMC12662970#VU-2018-11648Hanarth FondsMikhaeel NGGyorke TCzibor SEertink JJZijlstra JMFerrandez MCZwezerijnen GJCHanoun CLugtenburg PJCeriani LZucca EHeymans MWWiegers SEKurch LHuttmann AChamuleau MEDBarrington SFBoellaard RDuhrsen UGolla SSVfalsePredicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans.<h4>Background</h4>The aim of this study was to develop 3D convolutional neural networks (CNN) for the prediction of 2 years' time to progression using PET/CT baseline scans from diffuse large B-cell lymphoma (DLBCL) patients. The predictive performance of the 3D CNNs was compared to that of the International Prognostic Index (IPI) and a previously developed 2D CNN model using maximum intensity projections (MIP-CNN).<h4>Results</h4>1132 DLBCL patients were included from 7 independent clinical trials. Two 3D CNN models were developed using a training dataset of 636 patient scans merged from two trials, one CNN model trained on lesion-only PET (L-PET3D-CNN) and the second model trained on both lesion-only and whole body PET scans (LW-PET3D-CNN). The 3D models were cross-validated and perform2025-01-01T00:00:00Z2025 Nov2026-06-05T21:35:31.101Z2026-05-22T03:12:32.872ZS-EPMC126629704131340510.1186/s13550-025-01336-1