Introduction: Follicular Lymphoma (FL) is the second most common non-Hodgkin lymphoma in adults and is heterogeneous with 20% of poor-outcome patients relapsing/progressing within 24 months (POD24) of first treatment start (Casulo et al., JCO 2015). Early identification of those POD24 patients is critical but remains elusive. We initiated a collaboration between the academic CALYM Carnot Institute and the private company Euranova aiming at developing interpretable artificial intelligence (AI) models based on Positron Emission Tomography (PET) images to predict POD24. Methods: The dataset was based on the LYSA group RELEVANCE (RE) trial (Morshhauser et al, NEJM 2018) and on real-life (RL) datasets from two french hospitals (Dijon, Toulouse). Patients with FL diagnosis confirmation on biopsy, high tumor burden criteria (GELF >0) and PET images were enrolled in this retrospective study (Table 1). The input data, including PET images, tumor segmentation masks if available, and clinical data, are hosted on CALYM’s cloud based data lake Lymphoma Data Hub. After quality controls and data preprocessing, several POD24 predictive models based on pretreatment images were developed. A deep convolutional neural network with 3D ResNet architecture pretrained on 3D medical images and enriched with clinical data was trained on PET images with a binary cross entropy loss. A machine learning (ML) model based on the XGBoost boosting algorithm was trained on tabular radiomics features after extraction of 851 radiomics features for each tumor and features aggregation at patient’s level by using only the 3 tumors with largest volumes. Models were cross-validated on the RE cohort with leave-one-out method and tested on the RL cohorts (Figure 1). Results: The ML approach achieved more promising results than the deep learning (DL) model on the RE cohort with an AUC of 0.61 for ML vs. 0.56 for DL. Interestingly, in this ML model, SUVmax and new radiomics features, such as major axis length, came out as the most predictive features. For now the external validation of the ML model on RL cohorts is slightly more limited with an AUC of 0.51. Improvement of this result is expected owing to the ongoing enrichment of the cohort with additional external data. Finally, regarding the treatment’s impact on the model’s performance, preliminary data showed better results when training on the R-CHOP cohort only. Validation is underway, and will be presented at the meeting. Conclusion: Our study demonstrates the feasibility of POD24 prediction in FL patients through a radiomics based ML approach. Such predictive models could help clinicians for early identification of poor-outcome patients and decision-making, and ultimately improve patient care.