{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ferrandez MC"],"funding":["Hanarth Fonds","KWF Kankerbestrijding"],"pagination":["140"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12662970"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Background
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).Results
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 perform"],"journal":["EJNMMI research"],"pubmed_title":["Predicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans."],"pmcid":["PMC12662970"],"funding_grant_id":["#VU-2018-11648","Hanarth Fonds"],"pubmed_authors":["Mikhaeel NG","Gyorke T","Czibor S","Eertink JJ","Zijlstra JM","Ferrandez MC","Zwezerijnen GJC","Hanoun C","Lugtenburg PJ","Ceriani L","Zucca E","Heymans MW","Wiegers SE","Kurch L","Huttmann A","Chamuleau MED","Barrington SF","Boellaard R","Duhrsen U","Golla SSV"],"additional_accession":[]},"is_claimable":false,"name":"Predicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans.","description":"Background
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).Results
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 perform","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Nov","modification":"2026-06-05T21:35:31.101Z","creation":"2026-05-22T03:12:32.872Z"},"accession":"S-EPMC12662970","cross_references":{"pubmed":["41313405"],"doi":["10.1186/s13550-025-01336-1"]}}