Multiple Myeloma Prognosis From PET Images: Deep Survival Losses and Contrastive Pre-Training

Objective: Diagnosis and follow-up of multiple myeloma (MM) patients involve analysing full-body Positron Emission Tomography (PET) images. Towards assisting the analysis, there has been an increased interest in machine learning methods linking PET radiomics with survival analysis. Despite deep-learning's success in other fields, its adaptation to survival faces several challenges. Our goal is to design a deep-learning approach to predict the progression-free survival of MM patients from PET lesion images. Methods: We study three aspects of such deep-learning approach: i) the loss function: we review existing and propose new losses for survival analysis based on contrastive triplet learning, ii) Pre-training: We conceive two pre-training strategies to cope with the relatively small datasets, based on patch classification and triplet loss embedding. iii) the architecture: we study the contribution of spatial and channel attention modules. Results: Our approach is validated on data from two prospective clinical studies, improving the c-index over baseline methods, notably thanks to the channel attention module and the introduced pre-training methods. Conclusion and significance: We propose for the first time an end-to-end deep-learning approach, M2P2, to predict the progression-free survival of MM patients from PET lesion images. We introduce two contrastive learning approaches, never used before for survival analysis.