Multiple Myeloma Prognosis From PET Images: Deep Survival Losses and Contrastive Pretraining

Objective: Diagnosis and follow-up of multiple myeloma (MM) patients involve analyzing full body positron emission tomography (PET) images. Toward 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 (PFS) of MM patients from PET lesion images. Methods: we study three aspects of such deep learning approach: 1) Loss Function: we review existing and propose new losses for survival analysis based on contrastive triplet learning; 2) Pretraining: we conceive two pretraining strategies to cope with the relatively small datasets, based on patch classification and triplet loss embedding; and 3) 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 pretraining methods. Conclusion and Significance: we propose for the first time an end-to-end deep learning approach, M2P2, to predict the PFS of MM patients from PET lesion images. We introduce two contrastive learning approaches, never used before for survival analysis.