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 18 F-fluorodeoxyglucose
( 18 F-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 18 F-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, metabolic tumour
volume and Dmax bulk was assessed. Probabilities for PET scans with synthetically
removed tumors were also assessed. The CNN provided a 2-year TTP prediction with an
area under the curve (AUC) of 0.74, outperforming the IPI-based model (AUC = 0.68).
Furthermore, high probabilities (> 0.6) of the original MIPs were considerably decreased
after removing the tumours (< 0.4, generally). These findings suggest that MIP-based
CNNs are able to predict treatment outcome in DLBCL.
