The interest of 18Fluoro-deoxyglucose (FDG) positron emission tomography (PET) imaging in the management of patients with multiple myeloma (MM) for the workup at diagnosis and for therapeutic evaluation has recently been demonstrated. FDG-PET is a powerful imaging tool for bone lesions detection at initial diagnosis with high sensitivity and specificity values. The independent pejorative prognostic value on progression-free survival (PFS) and overall survival (OS) of baseline PET-derived parameters (presence of extra-medullary disease (EMD), number of focal bone lesions (FLs), and maximum standardized uptake values [SUV max ]) has been reported in several large independent prospective studies. During therapeutic evaluation, FDG-PET is considered as the reference imaging technique, because it can be performed much earlier than MRI which lacks specificity. Persistence of significant FDG uptake after treatment, notably before maintenance therapy, is an independent pejorative prognostic factor, especially for patients with a complete biological response. So FDG-PET and medullary flow cytometry are complementary tools for detection of minimal residual disease before maintenance therapy. However, the definition of PET metabolic complete response should be standardized. In patients with smoldering multiple myeloma, the presence of at least one hyper-metabolic lytic lesions on FDG-PET may be considered as a criterion for initiating therapy. FDG-PET is also indicated for initial staging of a solitary plasmacytoma so as to not disregard other bone or extra-medullary localizations. Development of nuclear medicine offer new perspectives for MM imaging. Recent PET tracers are willing to overcome limitations of FDG. (11)C-Methionine, which uptake reflects the increased protein synthesis of malignant cells seems to correlate well with bone marrow infiltration. Lipid tracers, such as Choline or acetate, and some peptide tracers, such as (68) Ga-Pentixafor, that targets CXCR4 (chemokine receptor-4, which is often expressed with high density by myeloma cells), are other promising PET ligands. 18F-fludarabine and immuno-PET targeting CD138 and CD38 also showed promising results in preclinical models.

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Serum markers and bone marrow examination are commonly used for monitoring therapy response in multiple myeloma (MM), but this fails to identify minimal residual disease (MRD), which frequently persists after therapy even in complete response patients, and extra-medullary disease escape. Positron emission tomography with computed tomography using 18F-deoxyglucose (FDG-PET/CT) is the reference imaging technique for therapeutic assessment and MRD detection in MM. To date, all large prospective cohort studies of transplant-eligible newly diagnosed MM patients have shown a strong and independent pejorative prognostic impact of not obtaining complete metabolic response by FDG-PET/CT after therapy, especially before maintenance. The FDG-PET/CT and MRD (evaluated by flow cytometry or next-generation sequencing at 10−5 and 10−6 levels, respectively) results are complementary for MRD detection outside and inside the bone marrow. For patients with at least a complete response, to reach double negativity (FDG-PET/CT and MRD) is a predictive surrogate for patient outcome. Homogenization of FDG-PET/CT interpretation after therapy, especially clarification of complete metabolic response definition, is currently underway. FDG-PET/CT does not allow MRD to be evaluated when it is negative at initial workup of symptomatic MM. New PET tracers such as CXCR4 ligands have shown high diagnostic value and could replace FDG in this setting. New sensitive functional magnetic resonance imaging (MRI) techniques such as diffusion-weighted MRI appear to be complementary to FDG-PET/CT for imaging MRD detection. The goal of this review is to examine the feasibility of functional imaging, especially FDG-PET/CT, for therapeutic assessment and MRD detection in MM.

Multiple myeloma (MM) is always preceded by an initial monoclonal gammopathy of undetermined significance (MGUS) that then develops into asymptomatic or smoldering multiple myeloma (SMM), which constitutes an intermediate clinical stage between MGUS and MM. According to a recent study, risk factors for faster MGUS to MM progression include an M protein of 1.5 g/dL or more and an abnormal free light chain ratio in patients with non-IgM MGUS. Therefore, the International Myeloma Working Group (IMWG) decided to recommend whole-body computed tomography (WBCT) for patients with high-risk MGUS in order to exclude early bone destruction. Studies evaluating magnetic resonance imaging (MRI) in SMM found an optimal cutoff of two or more focal lesions to be of prognostic significance for fast progression into symptomatic disease and considered this biomarker as a myeloma-defining event (MDE) needing to start therapy with the aim to avoid progression to harmful bone lesions. Moreover, studies assessing positron emission tomography (PET) with computed tomography (CT) using 18F-deoxyglucose (FDG) (FDG-PET/CT) in SMM showed that presence of focal bone lesion without underlying osteolysis is associated with a rapid progression to symptomatic MM. Latest IMWG guidelines recommended to perform WBCT (either CT alone or as part of an FDG-PET/CT protocol) as the first imaging technique at suspected SMM and, if these images are negative or inconclusive, to perform whole-body MRI. The goal of this paper is to clarify the role of different imaging modalities in MGUS and SMM workups.

In the context of multiple myeloma, patient diagnosis and treatment planning involve the medical analysis of full-body Positron Emission Tomography (PET) images. There has been a growing interest in linking quantitative measurements extracted from PET images (radiomics) with statistical methods for survival analysis. Following very recent advances, we propose an end-to-end deep learning model that learns relevant features and predicts survival given the image of a lesion. We show the importance of dealing with the variable scale of the lesions, and propose to this end an attention strategy deployed both on the spatial and channels dimensions, which improves the model performance and interpretability. We show results for the progression-free survival prediction of multiple myeloma (MM) patients on a clinical dataset coming from two prospective studies. We also discuss the difficulties of adapting deep learning for survival analysis given the complexity of the task, the small lesion sizes, and PET low SNR (signal to noise ratio).