Highlights
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VCFs on WBLDCT were associated with worse PFS and OS in univariate analysis.
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No imaging bone markers independently predicted outcomes in multivariate models.
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WBLDCT is suitable for the diagnosis of MBD, but with limited prognostic value.
Keywords: Multiple myeloma, Whole-body low-dose computed tomography, Bone, Fracture, Overall survival, Progression-free survival
Abstract
Background
Multiple myeloma (MM) frequently presents with myeloma bone disease (MBD), manifesting as osteolytic lesions and skeletal-related events (SREs), significantly impairing quality of life and increasing morbidity. Whole-body low-dose computed tomography (WBLDCT) has become the standard for assessing bone involvement at diagnosis, but its prognostic significance remains unclear. The aim of this study was to evaluate the burden of MBD in newly diagnosed MM patients using WBLDCT and examined associations between imaging characteristics and survival outcomes.
Methods
In this retrospective, single center, analysis of 119 MM patients, WBLDCT was performed at diagnosis prior to treatment initiation. Imaging findings, including vertebral compression fractures (VCFs), lesion number, cortical destruction, and appendicular skeleton medullary cavity (ASMC) patterns, were recorded. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan-Meier curves and Cox regression models.
Results
VCFs were significantly associated with inferior PFS (18.1 vs. 33.6 months; p = 0.013) and OS (51.5 months vs. not reached; p = 0.023) in univariate analyses. However, in multivariable models, no imaging parameter, including VCFs, retained independent prognostic significance. Other imaging variables (lesion count, ASMC subtype, cortical destruction) were not predictive of outcomes.
Conclusions
While VCFs identified on WBLDCT correlate with poor outcomes in univariate analysis, they do not serve as independent prognostic markers when adjusting for established clinical factors. These findings suggest that in the era of novel anti-myeloma therapeutics, the bone disease burden at diagnosis may not impact prognosis significantly.
1. Introduction
Multiple myeloma (MM) is a neoplasm of plasma cells marked by the aberrant proliferation of clonal plasma cells in the bone marrow, often associated with significant bone involvement. Myeloma bone disease may present as osteolytic lesions and/or skeletal-related events (SREs), affecting nearly 80 % of patients at diagnosis and significantly impacting quality of life and increasing morbidity [1,2]. The precise assessment of myeloma bone disease is crucial for diagnosing symptomatic MM according to the International Myeloma Working Group (IMWG) criteria, [3] as well as for determining its clinical implications, in terms of planning therapeutic approaches and patient prognosis.
Whole-body low-dose computed tomography (WBLDCT) has become and remains the standard for evaluating myeloma bone disease both in patients with symptomatic disease and smoldering myeloma, [[4], [5], [6]] particularly due to its high sensitivity in identifying cortical and trabecular bone destruction as compared to traditional skeletal surveys [[7], [8], [9], [10], [11]]. Interestingly, deep learning models based on WBLDCT imaging characteristics may even predict for the presence of high-risk cytogenetic abnormalities [12]. Although the presence of SREs at the time of MM diagnosis has been associated with increased morbidity and dismal patient outcomes, [13,14] data on the role of the myeloma bone disease burden in survival outcomes are scarce in the current literature.
In this context, this study evaluated the bone disease burden in patients with newly diagnosed MM using WBLDCT and we explored possible correlations between imaging characteristics and survival outcomes.
2. Methods
2.1. Study design and patient selection
This was a retrospective observational study conducted at a single tertiary referral center. Consecutive patients newly diagnosed with MM who underwent baseline WBLDCT at diagnosis, before the administration of any anti-myeloma treatment were enrolled. All patients were diagnosed and treated in accordance with the IMWG guidelines and standard clinical protocols. Patient and disease characteristics were abstracted from patient charts. The study was conducted in accordance with the Declaration of Helsinki. All patients provided informed consent for undergoing WBLDCT and providing data for further studies (approval by the Institutional Review Board of Alexandra Hospital 003/9.4.12). High-risk cytogenetics were defined by the presence of del(17p), t(4;14), or t(14;16).
2.2. Imaging analysis
All WBLDCT scans were reviewed independently by two experienced radiologists with expertise in MM imaging. More specifically, each patient was evaluated for the presence of any osteolytic lesions, the total number and the largest diameter of the lesions, the presence of cortical destruction, the presence of fractures and vertebral compound fractures (VCFs), the presence of appendicular skeleton medullary cavities (ASMC) classified into fatty, nodular, diffuse or mixed, their density in Hounsfield units and their localization. Each skeletal site (skull, cervical spine, thoracic spine, lumbar spine, sacrum, left and right innominates, left and right ribs, left and right clavicles, left and right scapulae, sternum, left and right femurs, left and right humerus) was assessed separately (Supplemental Fig. 1).
2.3. Statistical analysis
Data were expressed as frequencies, mean with SD or median with interquartile range (IQR), as appropriate depending on data type. For categorical variables, associations between groups were investigated using Fisher’s exact test, since the chi-square test could not reliably be used due to low expected cell counts (i.e., expected frequency < 5 in some categories). For continuous or ordinal variables that were not normally distributed, the Kruskal–Wallis rank-sum test was used, a non-parametric alternative to ANOVA, to evaluate differences across groups. All tests were two-sided and p values < 0.05 were considered significant. To investigate the different projected survival outcomes for overall and disease-free survival, survival curves were estimated using the Kaplan-Meier method, and differences between groups were compared using the log-rank test to obtain a p-value. To assess whether our VCF predictor provided additional prognostic value beyond the MM Revised Stage (RISS), two Cox proportional hazards models were fitted. One model included only RISS, the other model included both RISS and VCF predictors. The models were compared using a likelihood ratio test (LRT), to evaluate whether the model with the added predictor significantly improves the overall fit. Statistical analysis was performed using R version 4.1.2 (2021–11-01).
3. Results
3.1. Patient and treatment characteristics
119 consecutive patients (57.5 % females) diagnosed with NDMM from November 1st, 2012 until August 31st, 2017 were included, with a median age of 67 years (range 37–81). Patients were assessed per ISS and R-ISS stage as follows: 31 (26.3 %) and 20 (16.9 %) stage 1, 30 (25.4 %) and 71 (60.2 %) stage 2, 57 (48.3 %) and 27 (22.9) stage 3, respectively. As per R2-ISS they were distributed as follows: 48 (40.7 %) low risk, 24 (20.3 %) low-intermediate risk, 33 (28.0 %) intermediate-high risk and 13 (11.0 %) high risk. 73 patients (61.3 %) had performance status 0–1, whereas 19 (16.1 %) had at least one high-risk cytogenetic abnormality (Table 1). The patients received induction treatment as follows: 80 (67 %) based on proteasome inhibitors, 14 (12 %) based on immunomodulatory drugs, 22 (19 %) based on both a PI and an IMiD and 3 (2 %) based on anti-CD38 monoclonal antibodies. 31 (26.3 %) of the patients underwent autologous stem-cell transplantation (Table 2). All patients with myeloma bone disease at diagnosis (n = 84) received bisphosphonates, as per clinical practice.
Table 1.
Baseline patient characteristics.
| Variable | All patients (n = 119) |
|---|---|
| Age (years) | 67 (37–81) |
| Females (n, %) | 56 (57.5) |
| Hb (g/dL) | 10.8 (7.5–16.0) |
| Cr (mg/dL) | 1.1 (0.5–16.5) |
| Ca (mg/dL) | 9.8 (8.9–13.1) |
| B2 microglobulin (mg/dL) | 5.7 (2.1–40.2) |
| LDH (U/L) | 195 (107–649) |
| Alb (g/dL) | 4.1 (2.2–5.3) |
| Urea (mg/dL) | 50 (20–256) |
| Alk (IU/L) | 68 (23–141) |
| IgG (g/dL) | 327.5 (0.6–9010) |
| IgA (g/dL) | 17.15 (23–5460) |
| IgM (g/dL) | 25.5 (16–163) |
| M−peak (g/dL) | 2.7 (0.1–7.0) |
| κFLC (mg/L) | 59.0 (1.7–28500.0) |
| λFLC (mg/L) | 10.3 (0.1–13400.0) |
| BM infiltration (%) | 75 (20–100) |
| Heavy chain (n, %) IgG IgA IgD |
45 (54.8) 43 (36.4) 1 (0.8) |
| Light chain only (n, %) | 19 (16.1) |
| ISS (n, %) 1 2 3 |
31 (26.3) 30 (25.4) 57 (48.3) |
| R-ISS (n, %) 1 2 3 |
20 (16.9) 71 (60.2) 27 (22.9) |
| R2-ISS category (n, %) Low Low-intermediate Intermediate-High High |
48 (40.7) 24 (20.3) 33 (28.0) 13 (11.0) |
| PS (n, %) 0 1 2 3 4 |
30 (25.4) 43 (36.4) 26 (22.0) 15 (12.7) 1 (0.8) |
| High-risk cytogenetics (n, %) | 19 (16.1) |
| MRI exam (n, %) | 96 (81.4) |
| MRI pattern (n, %) focal normal variegated diffuse |
36 (30.5) 16 (23.6) 3 (2.5) 41 (34.7) |
Table 2.
First line treatment characteristics and outcomes.
| Variable | All patients (n = 119) |
|---|---|
| First-line treatment (n, %) VCD VMP RAD KMP VTD RD Anti-CD38-based Other* |
52 (44.1) 19 (16.1) 9 (7.6) 9 (7.6) 7 (5.9) 5 (4.2) 2 (1.7) 15 (12.7) |
| Follow-up (years, median – range) | 4.2 (0.1–7.3) |
| First response, at least PR (n, %) | 91 (76.3) |
| Time to first response (months, median – range) | 0.9 (0.1–5.4) |
| Best responses (n, %) CR or better VGPR PR |
11 (9.3) 39 (33.1) 40 (33.9) |
| Time to best response (months, median – range) | 1.9 (0.1–39.0) |
| PD (n, %) | 79 (67.0) |
| Deaths (n, %) | 63 (54.2) |
| HDM-ASCT (n, %) | 31 (26.3) |
*Other therapy: VTP (1), VRD (3), IXA (3), CTD (4), Elotuzumab (4).
3.2. Bone disease burden evaluated by WBLDCT
Regarding osteolysis assessment, the median (range) number of osteolyses in each bone group were as follows: 2 (0–96) for the spine (cervical spine, thoracic spine, lumbar spine, sacrum), 0 (0–30) for the skull, 0 (0–41) for the shoulder (left and right clavicles, left and right scapulae), 0 (0–28) for the appendices (left and right femurs, left and right humerus) and 0 (0–42) for the ribs (right and left ribs). Regarding the ASMC assessment in the bilateral femurs and humerus, 47 patients (39.5 %) showed fatty ASMCs, 39 patients (32.8 %) had diffuse ASMCs, 30 patients (25.2 %) had nodular ASMCs, whereas 38 patients (31.9 %) had mixed ASMCs subtypes. The detailed evaluation of bone disease burden in terms of number of lesions, maximum size of detected lesions, and presence of cortical destruction in each examined bone is presented in Supplemental Tables 1–3.
3.3. Survival outcomes and the impact of bone disease burden
During a median follow-up of 4.2 years (range 0.1–5.4), 79 patients (67.0 %) showed disease progression and 63 (54.2 %) died. The median time to disease progression was 2.61 years (95 % CI: 2.05, 4.51). The median progression-free survival (PFS) was 2.19 years (95 % CI: 1.61–3.31) and median overall survival (OS) was 6.24 years (95 % CI: 4.06-not reached).
The 119 patients were stratified according to the presence (n = 52) or absence (n = 67) of VCFs. When applying the presence or not of VCFs as a single predictor for PFS and OS, the results of Kaplan-Meier survival estimation and Cox proportional hazards regression both showed significantly worse survival if at least one VCF was present. More specifically, the median PFS was 33.6 months (95 %CI: 26.2–65.0) for patients without VCFs compared to 18.1 months (95 %CI: 10.8–29.4) for patients with at least one VCF (HR 1.68, 95 %CI: 1.11 – 2.53, P = 0.013, Fig. 1). Similarly, the median OS was prolonged in patients without VCFs (median not reached, 95 %CI: 69.1-not reached) compared to those with at least one VCF (median 51.5, 95 %CI: 30.8–94.5) (HR 1.76, 95 %CI: 1.1–2.9, P = 0.023, Fig. 2). However, among all the examined imaging variables, no factor or combination factors had independent significance for patient prognosis in the multivariable analysis. Survival analysis for both PFS and OS was performed according to the presence of fracture, cortical destruction, lesion quantity, lesion size, hyperdensities, and combinations thereof (Supplemental Table 4). The curve comparisons did not yield a significant p-value with the log-rank test.
Fig. 1.
Kaplan-Meier curve for PFS according to the presence/absence of VCFs at MM diagnosis.
Fig. 2.
Kaplan-Meier curve for OS according to the presence/absence of VCFs at MM diagnosis.
Regarding VCF presence, it was investigated against RISS to determine whether it is an independent predictive factor for survival. Both the RISS and the ISS staging were predictive for PFS (RISS HR = 2.1, 95 %CI: 1.48–2.91, p < 0.0001; ISS HR = 1.91, 95 %CI: 1.45–2.51, p < 0.001) and OS (RISS HR = 2.73, 95 %CI: 1.80–4.14, p < 0.001; ISS HR = 2.24, 95 %CI: 1.56–3.17, p < 0.001) in the univariate analysis. A model was constructed for RISS survival and another model for RISS and VCF survival. Regarding the PFS, the likelihood ratio test comparing the two models yielded a chi-square statistic of 1.72 with 1 degree of freedom (p = 0.190). Regarding the OS, the likelihood ratio test comparing the two models yielded a chi-square statistic of 0.68 with 1 degree of freedom (p = 0.41). This indicates that adding VCF to a model already containing RISS did not significantly improve model fitness neither for PFS nor for OS, suggesting that VCF did not function as an independent predictor for survival models in our cohort.
4. Discussion
In this study, we evaluated WBLDCT scans to evaluate comprehensively the bone disease burden at diagnosis in a group of newly diagnosed patients with MM and we examined how it affected their outcomes in terms of PFS and OS. Among all the examined bone-related indices, the presence of VCFs at diagnosis was associated to significantly inferior survival outcomes. However, none of the imaging parameters, including VCFs, retained their statistical significance in the multivariable analysis. These results suggest that the extent of myeloma bone disease at diagnosis does not affect patient prognosis, especially in the modern era of anti-myeloma treatments. Our findings are in line with previous research showing that bone disease may not always be a suitable predictor for PFS and OS, [15] despite the fact that SREs remain a major cause of morbidity and functional impairment for patients with MM.
SREs, and especially VCFs, are well known to increase MM disease burden and MM-related morbidity. Vertebral instability secondary to marked myeloma bone disease in the spine has been associated with increased myeloma cell load, frailty indices and increased activity of inflammatory cytokines [[16], [17], [18]]. As anticipated, the status of bone health in patients with MM is correlated with the performance status, as well. Interestingly, it has been also linked to treatment adherence [19]. All these constitute confounding factors that altogether may contribute to poorer patient outcomes.
There are a number of reasons why there seems to be a dissociation between imaging findings and survival outcomes when evaluated in the context of multifactorial analysis.
First, treatment-related variables, including the use of triplet or quadruplet induction regimens along with upfront autologous transplantation, can overcome the negative prognostic impact of myeloma bone disease and associated SREs present at diagnosis. Novel anti-myeloma agents have shown a favorable effect on markers of bone metabolism, whereas bone targeted therapies including bisphosphonates and denosumab prevent SREs [[20], [21], [22], [23], [24], [25], [26]]. Second, the presence of high-risk cytogenetics and underlying disease biology may play a more decisive role in determining long-term outcomes, independent of skeletal findings on imaging at diagnosis. This concept has been previously proposed by Rasche et al who showed that distinct bone lesions in MM may represent spatial heterogeneity rather than a direct sign of aggressive myeloma disease [27]. Disease relapse after remission may initiate from dormant myeloma cells with a resistant phenotype in the endosteal niche [28], which is not necessarily associated with the extent of bone disease at diagnosis according to WBLDCT.
Furthermore, the lack of any prognostic role for the lesion number or ASMC patterns supports the view that lesion burden on imaging may reflect local bone marrow involvement in distinct myeloma foci rather than universal disease kinetics. Another interesting observation in our patient cohort was the large variability of ASMC subtypes, although none of the examined patterns had any prognostic value. While diffuse ASMCs in WBLDCT might be thought to reflect diffuse marrow infiltration as evident by magnetic resonance imaging (MRI) [29], the lack of prognostic impact may be due to possible overlap with other disease or host-related factors. We should also acknowledge the retrospective nature of our data and the relative small patient numbers in the subgroups that may have led to underpowered analysis unable to reveal marginally significant associations. In addition to the above, a longitudinal evaluation of bone health with imaging studies and the impact of WBLDCT-defined bone disease parameters on the risk for subsequent SREs may be important to derive prognostic information and should be addressed in future studies.
Imaging findings on functional imaging studies such as increased metabolic activity in 18F-FDG positron emission tomography (PET)/CT and focal lesions in diffusion-weighted MRI may provide more prognostic information [[30], [31], [32], [33], [34], [35], [36], [37], [38]]. Although whole body MRI (WBMRI) has a lower diagnostic yield for detecting osteolytic bone disease, pseudo-CT MRI sequences improve the diagnostic accuracy [39,40]. Multi-parametric WBMRI protocols may also distinguish active from inactive myeloma foci, in concordance to 18F-FDG PET/CT [41]. Moreover, normalization of FDG-avid lesions after induction treatment with or without autologous transplantation has been associated independently with PFS [30]. Interestingly, whole body evaluation of bone marrow metabolism by 18F-FDG PET/CT scans has shown prognostic value in terms of both PFS and OS in patients with newly diagnosed MM [[42], [43], [44]].
5. Conclusions
Although the presence of VCFs at WBLDCT at diagnosis of MM was correlated with survival outcomes in the univariate analysis, none of the imaging parameters retained statistical significance in the multivariable analysis. Our results suggest that the extend of the MBD burden at diagnosis may not impact OS in the era of modern anti-myeloma treatment regimens. WBLDCT remains the gold standard for the diagnosis of myeloma bone disease, due to its high sensitivity and wide availability; however, functional imaging studies (DW-MRI, PET/CT) are promising in providing more reliable prognostic data.
CRediT authorship contribution statement
Evangelos Terpos: Writing – review & editing, Supervision, Methodology, Investigation, Data curation, Conceptualization. Vassilis Koutoulidis: Writing – original draft, Methodology, Investigation, Data curation, Conceptualization. Ioannis Ntanasis-Stathopoulos: Writing – original draft, Investigation, Data curation. Stylianos Mavropoulos-Papoudas: Writing – review & editing, Investigation, Formal analysis, Data curation. Maria Douka: Writing – review & editing, Investigation, Data curation. Maria Gavriatopoulou: Writing – review & editing, Investigation, Data curation. Panagiotis Malandrakis: Writing – review & editing, Investigation, Data curation. Vasiliki Spiliopoulou: Writing – review & editing, Investigation, Data curation. Foteini Theodorakakou: Writing – review & editing, Investigation, Data curation. Despina Fotiou: Writing – review & editing, Investigation, Data curation. Magdalini Migkou: Writing – review & editing, Investigation, Data curation. Nikolaos Kanellias: Writing – review & editing, Investigation, Data curation. Evangelos Eleutherakis-Papaiakovou: Writing – review & editing, Investigation, Data curation. Efstathios Kastritis: Writing – review & editing, Investigation, Data curation. Lia-Angela Moulopoulos: Writing – review & editing, Investigation, Data curation. Meletios A Dimopoulos: Writing – review & editing, Supervision, Investigation, Data curation.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Prof. Evangelos Terpos and Dr. Ioannis Ntanasis-Stathopoulos serve as guest editors to the Journal of Bone Oncology, in the special issue Myeloma Bone Disease. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
None.
Funding
None.
Research involving Human Participants.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Ethical approval.
Not applicable for this retrospective study.
Author contributions
VK and ET contributed to the study conception and design. Material preparation, data collection and analysis were performed by INS, VK, SMP, MD, MG, PM, VS, FT, DF, MM, NK, EEP, EK, LAM, MAD, ET. SMP performed the statistical analysis. The first draft of the manuscript was written by INS and VK, and all authors reviewed and provided feedback. All authors read and approved the final version of the manuscript.
Data availability statement
The data that support the findings of this study are available on request from the corresponding author.
Footnotes
This article is part of a special issue entitled: ‘Myeloma bone disease’ published in Journal of Bone Oncology.
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jbo.2025.100720.
Appendix A. Supplementary data
The following are the Supplementary data to this article:
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