Total diffusion volume in MRI vs. total lesion glycolysis in PET/CT for tumor volume evaluation of multiple myeloma



This study compared the tumor burden and prognostic impact of total diffusion volume (tDV) and total lesion glycolysis (TLG) in the same patients with newly diagnosed multiple myeloma (NDMM) simultaneously. We also examined the relationship between these imaging tumor volumes (TVs) and plasma cell (PC) TV in bone marrow (BM) specimens.


We retrospectively reviewed the data of 63 patients with newly diagnosed multiple myeloma (NDMM) from April 2016 to March 2018. tDV was calculated from whole-body diffusion-weighted imaging and TLG was calculated from the average standard uptake value and the metabolic tumor volume, respectively. Cellularity of BM hematopoietic tissue and the percentage of BM PCs were used as a reference of PC volume in the BM.


The Spearman correlation coefficient between tDV and TLG was moderate (ɤs = 0.588, p < 0.001) when PET false-negative patients were excluded. There were positive correlations between the BM plasma cell volume (BMPCV) and the imaging TVs (ɤs = 0.505, vs. tDV; and 0.464, vs. TLG). Patients with high tDV and high TLG, as determined by the receiver operating characteristic curve, had worse survival; moreover, patients with both high tDV and high TLG showed the worst prognosis (median progression-free and overall survival: 13.2 and 28.9 months, respectively).


Although tDV and TLG each reflected the total TV, in several cases, tDV and TLG were discrepant due to the biological features of each MM. It is important to use both modalities for complementary assessment of total tumor burden and biological characteristics in MM.

Key Points

Total diffusion volume (tDV) and total lesion glycolysis (TLG) reflect the total tumor volume and have prognostic value in patients with multiple myeloma (MM).

tDV and TLG could assess MM from different biological perspectives and should be considered for each patient individually.


Multiple myeloma (MM) is caused by the proliferation of monoclonal malignant plasma cells (PCs) in the bone marrow (BM). Assessment of BMPCs is important for its diagnosis and for response evaluation in patients with MM [1, 2]. To evaluate the extent of myeloma lesions, whole-body magnetic response imaging (WB-MRI) and positron-emission tomography/computed tomography (PET/CT) with fluorine-18 fluorodeoxyglucose (18F-FDG) have emerged as novel modalities. Whole-body diffusion-weighted imaging (WB-DWI) of MRI provides additional information over morphologic imaging and the apparent diffusion coefficient (ADC) shows different values, depending on the proportion or the presence of water, cell density, fat, and malignant tumor cells in the BM [3, 4]. These imaging modalities can identify extramedullary disease (EMD) as well as medullary disease, and both have been incorporated in the consensus guidelines for use in both clinical trials and clinical practice [5,6,7,8,9]. Pretreatment imaging characteristics also have prognostic impact in patients with newly diagnosed MM (NDMM). In MRI, the BM infiltration pattern and the number of focal lesions (FLs) > 7 [10,11,12], and in PET/CT, higher maximum standard uptake value (SUVmax), FLs > 3, and the presence of EMD [13, 14] are reported to predict a worse prognosis.

Recently, quantitative imaging assessment of the tumor burden has become available. Several previous studies have shown that metabolic tumor volume (MTV) and total lesion glycolysis (TLG), calculated from18F-FDG PET/CT, and total diffusion volume (tDV), calculated from WB-DWI, could reflect the potential quantitative tumor volume (TV) as well as predict prognosis [15,16,17,18]. MTV and TLG reflects the size and expansion of a tumor lesion with high glucose metabolism [19]. Although assessing the total TV during patient follow-up might predict treatment response and prognosis [17, 18], statements regarding assessment of treatment response, such as the Italian Myeloma criteria for PET USe (IMPeTUs) [20] for FDG PET/CT and the Myeloma Response Assessment and Diagnosis System (MY-RADS) [8] for MRI, still refrain from mentioning the total imaging TV. To date, there have been only few studies on imaging TV and, to the best of our knowledge, there have been no reports evaluating both tDV and TLG simultaneously in patients with MM.

This study aimed to compare the tumor burden and prognostic impact of both tDV and TLG in the same patients with NDMM simultaneously, and to examine the relationship between these imaging TVs and PC tumor volume in BM specimens.


Study design and patients

We retrospectively identified 69 consecutive patients with symptomatic NDMM diagnosed from January 2016 to December 2018 at our institute. MM diagnosis and treatment responses were assessed using the International Myeloma Working Group criteria [1, 2]. Data from 6 patients (MRI n = 3, PET/CT n = 2, best supportive case due to end-stage gastric cancer n = 1) were unavailable and excluded from the analysis. Ultimately, 63 patients were included in our analysis (Fig. 1). The observation period ended on March 2020. All patients underwent pretreatment PET/CT assessment concurrently with WB-DWI within a time interval of 1 week. Interpretation of each modality conformed to the relevant consensus statement [5, 6]. All participants provided written informed consent for the use of PET/CT, MRI, and clinical data. The study was conducted according to the Declaration of Helsinki and was approved by the review board of our center.

Fig. 1

Participant flowchart

Assessment of bone marrow

BMPC percentage was assessed by manual counts of BM-biopsy slides immunohistochemically stained for CD138 [21]. BM examinations were performed blindly from unilateral posterior iliac crest by each clinician at the time of diagnosis. High-risk cytogenetic abnormalities (CAs) were defined either as del(17p), t(4;14), or t(14;16) by interphase fluorescence in situ hybridization analysis using PCs purified by CD138-coated magnetic beads (Miltenyi Biotec). In this study, we defined the “BMPC volume (BMPCV)” by calculating the percentage of cellularity and the percentage of CD138-positive PCs in BM-biopsy specimens (e.g., if cellularity was 60% and CD138-positive PCs were 80% in the BM, the BMPCV was 48).

Acquisition and analysis of PET/CT

PET/CT imaging details have been described previously [18]. High-risk PET/CT findings were defined as the presence of more than 3 FLs, SUVmax > 4.2, or EMD [14]. MTV was calculated as the volume of myeloma lesions with SUV ≥ 2.5 on PET/CT. The TLG was defined from the sum of product of the SUVmean and total MTV. A computer-aided analysis of the PET/CT images for the MTV and TLG was performed using Metavol software (Hokkaido University) [22]. In the present study, results of non-FDG-avid patients who showed positive findings on WB-DWI but lacked whole-body FDG accumulation on PET/CT were reported as PET false-negative.

Acquisition and analysis of MR images

WB-DWI was performed on a 1.5-Tesla unit (Magnetom Vision; Siemens Healthcare). WB-DWI was performed with the following parameters: acquisition type, 2D; repetition time, 5400 ms; echo time, 74 ms, inversion time, 180 ms; slice thickness, 5 mm; b-values, 0 and 900 s/mm2. The ADC was calculated voxel by voxel for each image slice in b = 0 and 900 images. The calculation of tDV by WB-DWI was quantitatively performed using newly developed medical imaging software (BD score; PixSpace) [17] by a board-certified radiologist (Y.M., with 13 years of experience) as follows: regions-of-interest (ROI) including areas of abnormal signal high intensity of ADC, which were considered to contain myeloma lesions in the BM or extramedullary tissue, were automatically obtained with a b-value of 900 s/mm2. Areas of physiologically high signal intensity were subsequently removed manually. Threshold values for distinguishing disease from background (≤ 99%) were manually adjusted to cover all the lesions in the bone.

Statistical analysis

Continuous variables were summarized as medians and interquartile ranges (IQR). Categorical variables were described using frequencies and percentages. Patient survival rates were estimated using the Kaplan–Meier method and were compared using the log-rank test. The correlations between various imaging and pathological TV were evaluated by Spearman’s rank correlation coefficients as data was not normally distributed. The correlation coefficient (ɤs) was interpreted as weak (0.30 to 0.50), moderate (0.50 to 0.70), and strong (0.70 to 0.90) based on previous literature [23]. All statistical analyses were performed using EZR software package (Saitama Medical Center, Jichi Medical University) [24]. A two-sided p < 0.05 was considered as statistically significant.


Patient characteristics and BM status

The baseline clinical characteristics of the patients are summarized in Table 1. The median age of the 63 patients was 74.0 years (IQR: 67.0–81.0). The number of patients with high-risk CAs, International staging system (ISS) stage III, and revised-ISS (R-ISS) stage III were 16 (25.4%), 32 (50.8%), and 19 (30.2%), respectively. Twenty-seven (42.9%) and 12 (19.0%) patients showed disease progression and death, respectively, during the study period. All patients received combination chemotherapy as an initial treatment and most of these (93.7%) treatments involved proteasome inhibitor. Twenty-four patients (38.1%) received autologous stem-cell transplantation. The median PFS and OS were 35.7 months and not reached (NR), respectively (95% confidence interval [CIs]: 25.2 months–NR, and 41.6 months–NR, respectively).

Table 1 Demographic and baseline characteristics

Median BM cellularity, BMPC percentage, and BMPCV were 60.0%, 55.0%, and 24.8 (IQR: 40.0–70.0%, 33.6–90.0%, and 12.0–55.0%), respectively. Spearman’s correlation coefficient between BM cellularity and BMPCV was strong (ɤs = 0.824, p < 0.001).

Assessment of imaging variables in PET/CT and MRI

Fifty-five patients (87.3%) showed myeloma lesions on both PET/CT and WB-DWI, and 8 (12.7%) were positive on WB-DWI but negative on PET/CT (PET false-negative). None of the patients were WB-DWI-negative but PET/CT-positive. Thirty-eight patients (60.3%) had at least 1 high-risk PET/CT finding. The proportion of patients with SUVmax > 4.2, more than 3 FLs, and EMD were 34, 31, and 4 patients (60.3, 54.0, and 6.3%), respectively. The mean ADC in all patients was 0.94 × 10−3 mm2/second (IQR: 0.86–1.04).

The median tDV, MTV, and TLG for all patients were 395.7 mL, 42.4 cm3, and 134.8 g (IQR: 117.3–944.0 mL, 5.67–287.4 cm3, and 17.8–951.3 g), respectively. The optimal cutoff values that discriminated high and low tDV and TLG were 974.2 mL and 260.0 g, respectively, by receiver operating characteristic curve analysis for predicting the highest risk of disease progression (area under the curve: 0.602 and 0.620, respectively). Based on these findings, we defined “high-tDV” and “high-TLG” values as those > 974.2 mL and > 260.0 g, respectively. There was a positive weak correlation between tDV and TLG, with ɤs = 0.494 (p < 0.001). This value elevated to moderate (ɤs = 0.588, p < 0.001) when PET false-negative patients were excluded (Fig. 2). Despite the correlation between TLG and tDV, we noted cases with significant discrepancy between the 2 parameters. Figure 3 (case 1 in Fig. 2) shows a representative case with high tDV and low TLG. This 77-year-old male patient had immunoglobulin-Dλ myeloma (IgD 2850 mg/dL), complicated with bulky plasmacytoma in the right iliac bone. The tDV of this patient increased to 1881.5 mL, but FDG uptake of BM and plasmacytoma was relatively low (SUVmax: 6.23), which resulted in discrepancy between tDV and TLG. Figure 4 (case 2 in Fig. 2) shows another patient with low tDV and high TLG. This patient had scattered nodular high FDG uptake, but the BM biopsy revealed a relatively hypocellular marrow with a low percentage of CD138-positive PCs. The tDV of this patient was 197.8 mL, while the TLG elevated to 2917.4 g due to the high SUV of this tumor.

Fig. 2

Spearman’s correlation coefficients between tDV and TLG were positively moderate correlated. ɤs = 0.588, p < 0.001. The value increased when patients with PET false-negative were excluded from the analysis. PET/CT, positron emission tomography/computed tomography; tDV, total diffusion volume; TLG, total lesion glycolysis

Fig. 3

Representative case with high tDV and low TLG (not a PET false-negative case) (case 1 in Fig. 2). The patient showed high tDV but low TLG. This case was not defined as PET false-negative. Both BM and plasmacytoma biopsy showed the proliferation of CD138-positive PCs, indicating that this was MM, not solitary plasmacytoma. In such patients, tDV should preferentially be used to measure the total tumor burden. PET/CT, positron emission tomography/computed tomography; tDV, total diffusion volume; TLG, total lesion glycolysis; BM, bone marrow; PCs, plasma cells; MM, multiple myeloma

Fig. 4

Representative case with low tDV and high TLG (case 2 in Fig. 2). The patient showed low tDV but high TLG. This case had high FDG uptake lesions throughout the body. In such patients, due to the high metabolic activity of MM, TLG should preferentially be used to measure the total tumor burden. tDV, total diffusion volume; TLG, total lesion glycolysis; FDG, fluorodeoxyglucose; MM, multiple myeloma

Correlation between BMPCV and imaging variables

To evaluate whether the tDV and TLG reflected the BMPCV, we investigated the correlation of each imaging variable with BMPCV. There was a positive moderate correlation between the BMPCV and the tDV (ɤs = 0.505, p < 0.001), as well as a positive weak correlation between BMPCV and the TLG (when excluding patients with PET false-negative; ɤs = 0.464, p < 0.001) (Fig. 5). There was a negative weak correlation between the ADC value and tDV (ɤs = − 0.362, p = 0.004).

Fig. 5

Spearman’s correlation coefficient between bone marrow plasma cell volume and tDV or TLG. ɤs = 0.505 (with tDV) and ɤs = 0.464 (with TLG). Both 2 imaging tumor volumes showed positive relationship with histological TV. tDV, total diffusion volume; TLG, total lesion glycolysis; TV, tumor volume

Prediction of survival outcomes evaluated by tDV, TLG, and their combined score

The progression-free survival (PFS) was significantly shorter in patients with high tDV than in those with low tDV (median PFS; 16.8 vs. 41.6 months, p = 0.020). Patients with low tDV tended to have better overall survival (OS) than those with high tDV, but the difference was not statistically significant (Supplementary Fig. 1). The PFS and OS were significantly shorter in patients with high TLG than in those with low TLG (median PFS: 16.8 months vs. NR; median OS: 41.6 months vs. NR, p = 0.004 and 0.026, respectively) (Supplementary Fig. 2).

Next, we evaluated the prognostic impact of combining both imaging techniques for TV, given that both WB-DWI and FDG PET/CT may reflect different biological phenomena in MM. Specifically, we divided patients into the following three groups based on their levels of tDV and TLG: group I, including patients with low tDV and low TLG; group III, comprising patients with both high tDV and high TLG; and group II, including patients with either high tDV or high TLG (not both). Kaplan-Meier survival curves of PFS and OS representing the combination of tDV and TLG were shown in Supplementary Fig. 3. Patients in group III showed the worst prognosis (median PFS and OS: 13.2 and 28.9 months, respectively, p = 0.001 and 0.06, for group I vs. group III), although patients were relatively young (median age: 70, range: 52−84 years) and despite the fact that more than half of them (n = 6/11) received high-dose chemotherapy followed by autologous stem cell transplantation. This result indicated that patients with histologically large and metabolically active myeloma presented very poor prognosis.

Moreover, we examined the factors that impacted PFS and OS; both high tDV and high TLG significantly were associated with shorter PFS and only high TLG affected OS on univariate analysis (high TLG: hazard ratio [HR], 2.93; 95% CI, 1.36–6.31, high tDV: HR, 2.42; 95% CI 1.12–5.24, p = 0.006 and 0.025, respectively). In addition, high TLG showed a higher HR for death than high tDV (high TLG: HR, 3.60; 95% CI, 1.08–12.0, high tDV: HR, 2.11; 95% CI 0.67–6.69, p = 0.037 and 0.20, respectively) (Table 2). Because the number of patients was too small, a multivariate analysis including other myeloma biomarkers was not performed.

Table 2 Univariate analysis of the survival predictive factors


In the present study, we showed the relationship between tDV and TLG in patients with NDMM, and found a positive correlation between the BMPCV and both tDV and TLG. In addition, both tDV and TLG could predict PFS, but only TLG retained its prediction capability for OS. Patients with both high tDV and high TLG showed a worse outcome than those without high imaging TV.

When we compared the tDV and TLG in the same patient population, there was a positive weak correlation between tDV and TLG in our entire cohort (ɤs = 0.494), and the correlation further strengthened (ɤs = 0.588) when patients with PET false-negative were excluded from the analysis. Despite this positive correlation, there were individual cases with a marked discrepancy between the two modalities (two representative cases in Figs. 3 and 4). This discrepancy could reflect the inherent characteristics of MRI and 18F-FDG PET/CT.

TLG was calculated from the myeloma lesions, based on the level of glucose accumulation within the total volume of all the ROIs [22, 25]; TLG can represent not only the imaging-based tumor burden, but also the total amount of glycolysis of the tumor. However, approximately 10% of patients with MM showed PET false-negative, despite the extensive BM infiltration of PCs. These patients showed low hexokinase-2 expression, as previously reported [26, 27]. In this study, we found 8 (12.7%) patients with PET false-negative due to low hexokinase-2 expression [27]. In such patients, it is difficult to distinguish the heavy PC infiltration from normal or hypoplastic marrow based on FDG uptake alone.

On the other hand, tDV was calculated from ROIs including areas of abnormal high signal intensity on WB-DWI. Our data showed a mild inverse correlation between ADC value and tDV. This inverse correlation between ADC value and high cellularity or malignant lesions was well-recognized in previous studies [28,29,30]. Therefore, it was assumed that the ADC value was decreased in cases with higher tDV. Conversely, in previous reports [4, 31, 32], a positive correlation was found between the ADC value and MM or malignant lymphoma (ML) infiltration in the BM. This was partially explained by a reduction in the proportion of the fat fraction, which was considered to show an extremely low ADC value, in the BM due to the myeloma or lymphoma cell expansion, and because of intra-voxel incoherent motion, observed at a relatively lower b-value (about 300 s/mm2). Taken together, tDV could evaluate the whole-body tumor burden and reflect histopathological TV.

In addition, patients could undergo WB-DWI in a shorter acquisition time without radiation, in contrast to PET/CT; this is highly important for recently diagnosed patients with MM, who tend to survive longer due to the development of novel agents. tDV would be useful even in patients with lower FDG uptake who are not suitable for FDG PET evaluation. Taken together, tDV and TLG have different characteristics for depicting TV, and could complement each other.

Our results indicated that 11 patients (17.5%) with high imaging TV in both tDV and TLG had a poor prognosis, despite being relatively young and having received autologous stem cell transplantation. Five of these patients were classified into the intermediate risk group with R-ISS stage II, while no high-risk CAs were detected by BM examination at diagnosis (data not shown). This suggests that some patients are misclassified when solely using the existing risk classification. Although BM examination is an essential method for patients with MM, the biological features of whole-body myeloma are not represented. Indeed, standard BM evaluation was previously reported to show different myeloma burden or biological characteristics to PET-guided biopsy specimens [33]. Therefore, we suggest that imaging-guided biopsies and imaging TV may be utilized in addition to the standard BM assessment to allow for further evaluation of the tumor biology and prognostic classification.

Nevertheless, this study had several limitations, including its retrospective nature, small sample size, and relatively short observation period. Moreover, each modality has inherent weak points; DWI showed high signals not only in myeloma lesions but also in several normal tissues, such as normal hematopoiesis in young individuals, and FDG uptake would also occur in inflammatory and hematopoietic reactions. These factors can lead to over-/underestimation of imaging TV; for example, tDV would be overestimated when patients are male, relatively younger, or present with TLG in those with double cancers or inflammation. Additionally, we could not evaluate lesions around the skull and lesions distal to the patella.

In conclusion, when we evaluated tDV and TLG simultaneously in the same MM patients, we found that tDV and TLG are well-correlated and that both variables are useful for predicting the prognosis in patients with NDMM. Our observations indicated that tDV would be suitable for evaluating the histopathological tumor burden and total tumor burden, even in patients with low FDG uptake. TLG would be more suitable in cases with higher SUVmax, as it represents high myeloma metabolism. These findings suggested that these two parameters might reflect slightly different biological features of myeloma, in addition to reflecting the tumor burden and their complementarity. Moreover, this study contributes to understanding which modality should be used for imaging the TV for each individual case and which variable to evaluate repeatedly to assess the treatment response. In the future, larger cohorts and prospective studies are required to validate and expand our findings.



Apparent diffusion coefficient


Bone marrow


Bone marrow plasma cell


Extramedullary disease


Focal lesions


Italian Myeloma criteria for PET USe


Malignant lymphoma


Maximum standard uptake value


Metabolic tumor volume


Multiple myeloma


Myeloma Response Assessment and Diagnosis System


Newly diagnosed MM


Plasma cells




Total diffusion volume


Total lesion glycolysis


Tumor volume


Whole-body magnetic response imaging


  1. 1.

    Rajkumar SV, Dimopoulos MA, Palumbo A et al (2014) International Myeloma Working Group updated criteria for the diagnosis of multiple myeloma. Lancet Oncol 15:e538–e548

    Article  Google Scholar 

  2. 2.

    Kumar S, Paiva B, Anderson KC et al (2016) International Myeloma Working Group consensus criteria for response and minimal residual disease assessment in multiple myeloma. Lancet Oncol 17:e328–e346

    Article  Google Scholar 

  3. 3.

    Nonomura Y, Yasumoto M, Yoshimura R et al (2001) Relationship between bone marrow cellularity and apparent diffusion coefficient. J Magn Reson Imaging 13:757–760

    CAS  Article  Google Scholar 

  4. 4.

    Hillengass J, Bauerle T, Bartl R et al (2011) Diffusion-weighted imaging for non-invasive and quantitative monitoring of bone marrow infiltration in patients with monoclonal plasma cell disease: a comparative study with histology. Br J Haematol 153:721–728

    Article  Google Scholar 

  5. 5.

    Dimopoulos MA, Hillengass J, Usmani S et al (2015) Role of magnetic resonance imaging in the management of patients with multiple myeloma: a consensus statement. J Clin Oncol 33:657–664

    Article  Google Scholar 

  6. 6.

    Cavo M, Terpos E, Nanni C et al (2017) Role of (18)F-FDG PET/CT in the diagnosis and management of multiple myeloma and other plasma cell disorders: a consensus statement by the International Myeloma Working Group. Lancet Oncol 18:e206–e217

    Article  Google Scholar 

  7. 7.

    Hillengass J, Usmani S, Rajkumar SV et al (2019) International myeloma working group consensus recommendations on imaging in monoclonal plasma cell disorders. Lancet Oncol 20:e302–e312

    Article  Google Scholar 

  8. 8.

    Messiou C, Hillengass J, Delorme S et al (2019) Guidelines for acquisition, interpretation, and reporting of whole-body MRI in myeloma: Myeloma Response Assessment and Diagnosis System (MY-RADS). Radiology 291:5–13

    Article  Google Scholar 

  9. 9.

    Tsujikawa T, Oikawa H, Tasaki T et al (2019) Whole-body bone marrow DWI correlates with age, anemia, and hematopoietic activity. Eur J Radiol 118:223–230

    Article  Google Scholar 

  10. 10.

    Lecouvet FE, Vande Berg BC, Michaux L et al (1998) Stage III multiple myeloma: clinical and prognostic value of spinal bone marrow MR imaging. Radiology 209:653–660

    CAS  Article  Google Scholar 

  11. 11.

    Walker R, Barlogie B, Haessler J et al (2007) Magnetic resonance imaging in multiple myeloma: diagnostic and clinical implications. J Clin Oncol 25:1121–1128

    Article  Google Scholar 

  12. 12.

    Moulopoulos LA, Dimopoulos MA, Kastritis E et al (2012) Diffuse pattern of bone marrow involvement on magnetic resonance imaging is associated with high risk cytogenetics and poor outcome in newly diagnosed, symptomatic patients with multiple myeloma: a single center experience on 228 patients. Am J Hematol 87:861–864

    Article  Google Scholar 

  13. 13.

    Haznedar R, Aki SZ, Akdemir OU et al (2011) Value of 18F-fluorodeoxyglucose uptake in positron emission tomography/computed tomography in predicting survival in multiple myeloma. Eur J Nucl Med Mol Imaging 38:1046–1053

    CAS  Article  Google Scholar 

  14. 14.

    Zamagni E, Patriarca F, Nanni C et al (2011) Prognostic relevance of 18-F FDG PET/CT in newly diagnosed multiple myeloma patients treated with up-front autologous transplantation. Blood 118:5989–5995

    CAS  Article  Google Scholar 

  15. 15.

    McDonald JE, Kessler MM, Gardner MW et al (2017) Assessment of total lesion glycolysis by (18)F FDG PET/CT significantly improves prognostic value of GEP and ISS in myeloma. Clin Cancer Res 23:1981–1987

    CAS  Article  Google Scholar 

  16. 16.

    Fonti R, Pellegrino S, Catalano L, Pane F, Del Vecchio S, Pace L (2020) Visual and volumetric parameters by 18F-FDG-PET/CT: a head to head comparison for the prediction of outcome in patients with multiple myeloma. Ann Hematol 99:127–135

    CAS  Article  Google Scholar 

  17. 17.

    Takasu M, Kondo S, Akiyama Y et al (2020) Assessment of early treatment response on MRI in multiple myeloma: comparative study of whole-body diffusion-weighted and lumbar spinal MRI. PLoS One 15:e0229607

    CAS  Article  Google Scholar 

  18. 18.

    Terao T, Machida Y, Tsushima T et al (2020) Pre-treatment metabolic tumour volume and total lesion glycolysis are superior to conventional positron-emission tomography/computed tomography variables for outcome prediction in patients with newly diagnosed multiple myeloma in clinical practice. Br J Haematol 191:223–230

    CAS  Article  Google Scholar 

  19. 19.

    Fonti R, Larobina M, Del Vecchio S et al (2012) Metabolic tumor volume assessed by 18F-FDG PET/CT for the prediction of outcome in patients with multiple myeloma. J Nucl Med 53:1829–1835

    CAS  Article  Google Scholar 

  20. 20.

    Nanni C, Zamagni E, Versari A et al (2016) Image interpretation criteria for FDG PET/CT in multiple myeloma: a new proposal from an Italian expert panel. IMPeTUs (Italian Myeloma criteria for PET USe). Eur J Nucl Med Mol Imaging 43:414–421

    CAS  Article  Google Scholar 

  21. 21.

    Matsue K, Matsue Y, Kumata K et al (2017) Quantification of bone marrow plasma cell infiltration in multiple myeloma: usefulness of bone marrow aspirate clot with CD138 immunohistochemistry. Hematol Oncol 35:323–328

    CAS  Article  Google Scholar 

  22. 22.

    Hirata K, Kobayashi K, Wong KP et al (2014) A semi-automated technique determining the liver standardized uptake value reference for tumor delineation in FDG PET-CT. PLoS One 9:e105682

    Article  Google Scholar 

  23. 23.

    Dennis E, Hinkle WW, Jurs SG (2003) Applied statistics for the behavioral sciences, 5th edn. Boston’s Houghton Mifflin Harcourt, Boston

    Google Scholar 

  24. 24.

    Kanda Y (2013) Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics. Bone Marrow Transplant 48:452–458

    CAS  Article  Google Scholar 

  25. 25.

    Larson SM, Erdi Y, Akhurst T et al (1999) Tumor treatment response based on visual and quantitative changes in global tumor glycolysis using PET-FDG imaging. The visual response score and the change in total lesion glycolysis. Clin Positron Imaging 2:159–171

    Article  Google Scholar 

  26. 26.

    Rasche L, Angtuaco E, McDonald JE et al (2017) Low expression of hexokinase-2 is associated with false-negative FDG-positron emission tomography in multiple myeloma. Blood 130:30–34

    CAS  Article  Google Scholar 

  27. 27.

    Abe Y, Ikeda S, Kitadate A et al (2019) Low hexokinase-2 expression-associated false-negative (18)F-FDG PET/CT as a potential prognostic predictor in patients with multiple myeloma. Eur J Nucl Med Mol Imaging 46:1345–1350

    CAS  Article  Google Scholar 

  28. 28.

    Cao W, Liang C, Gen Y, Wang C, Zhao C, Sun L (2016) Role of diffusion-weighted imaging for detecting bone marrow infiltration in skull in children with acute lymphoblastic leukemia. Diagn Interv Radiol 22:580–586

    Article  Google Scholar 

  29. 29.

    Mori N, Ota H, Mugikura S et al (2015) Luminal-type breast cancer: correlation of apparent diffusion coefficients with the Ki-67 labeling index. Radiology 274:66–73

    Article  Google Scholar 

  30. 30.

    Onishi N, Kanao S, Kataoka M et al (2015) Apparent diffusion coefficient as a potential surrogate marker for Ki-67 index in mucinous breast carcinoma. J Magn Reson Imaging 41:610–615

    Article  Google Scholar 

  31. 31.

    Messiou C, Collins DJ, Morgan VA, Desouza NM (2011) Optimising diffusion weighted MRI for imaging metastatic and myeloma bone disease and assessing reproducibility. Eur Radiol 21:1713–1718

    CAS  Article  Google Scholar 

  32. 32.

    Albano D, Patti C, Lagalla R, Midiri M, Galia M (2017) Whole-body MRI, FDG-PET/CT, and bone marrow biopsy, for the assessment of bone marrow involvement in patients with newly diagnosed lymphoma. J Magn Reson Imaging 45:1082–1089

    Article  Google Scholar 

  33. 33.

    Rasche L, Chavan SS, Stephens OW et al (2017) Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8:268–278

    CAS  Article  Google Scholar 

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The authors would like to thank the residents of the Department of Hematology/Oncology for their medical care to the patients, and the staff of the Division of Nuclear Medicine, Department of Radiology, for their assistance in this study. We also thank Editage ( for their English language editing services.


The authors state that this work has not received any funding.

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Correspondence to Toshiki Terao.

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The scientific guarantor of this publication is Kosei Matsue, the last author.

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The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

This study includes data on patients from the previously reported study (DOI:10.1111/bjh.16633). The previous study included 185 patients reported from January 2009 to October 2019. Unlike the previous report, this present study includes additional assessments of WB-MRI and BMPCs. The reason for the observation period from 2016 to 2018 in this present study was because the protocol of MRI had not been determined before 2016.


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Terao, T., Machida, Y., Narita, K. et al. Total diffusion volume in MRI vs. total lesion glycolysis in PET/CT for tumor volume evaluation of multiple myeloma. Eur Radiol (2021).

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  • Multiple myeloma
  • PET/CT
  • DWI
  • Tumor burden