Predicting 2-year time to progression in diffuse large B cell lymphoma using 3D CNNs on whole-body PET/CT scans.

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

SUBMITTER: Ferrandez MC 

PROVIDER: S-EPMC12662970 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature