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

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 performance was independently tested on 496 patient scans merged from five external trials, using the area under the curve (AUC). Performance was compared to the IPI and MIP-CNN using DeLong test. Occlusion maps were implemented to gain insights about the models’ decision-making process. The IPI and the MIP-CNN yielded an AUC of 0.53 and 0.65 respectively on external test data. The L-PET3D-CNN and the LW-PET3D-CNN yielded a significantly higher AUC, 0.65 and 0.64 respectively, compared to the IPI. For each individual external clinical trial, the models were consistently better than IPI. The MIP-CNN and the 3D CNNs showed equivalent performance on external test data. Conclusion: The 3D CNN models remained predictive of outcome on all external test datasets, outperforming the IPI. Although these models perform similarly to the MIP-CNN, the main advantage of the 3D CNN is the use of 3D occlusion maps to better understand the decision-making process of the models.