Keywords:
Oncology, Haematologic, Musculoskeletal bone, MR, PET-CT, CT, Staging, Segmentation, Comparative studies, Haematologic diseases, Neoplasia, Cancer
Authors:
A. Amlani, O. A. Westerland, M. M. Siddique, M. Streetly, I. El-Najjar, G. Cook, V. Goh; London/UK
DOI:
10.26044/ecr2019/C-2907
Conclusion
Quantitative parameters e.g.
standardised uptake value (SUV),
apparent diffusion coefficient (ADC) and fat fraction (FF) may enable more accurate assessment of plasma cell infiltration and bone marrow composition in newly diagnosed myeloma.
The use of water-fat imaging techniques such as the two-point or multi point Dixon technique permits the calculation of fat fraction data and hence quantitative assessment using the simple equation FF = fat/(fat+water) [7].
It is postulated that,
in high grade plasma cell infiltration of bone marrow,
fat cells are replaced by plasma cells [8].
In addition,
the presence of malignant disease in the bone marrow lowers adipocytes numbers because fat cells provide the lipids necessary for tumour energy metabolism and membrane synthesis [9].
It is therefore expected that fat fraction will be lower in patients with myeloma than those without and that fat fraction values will be lower in more severe disease [8]. Previous studies have identified a correlation between symptomatic and non-symptomatic fat fraction [8] as well as pre and post therapy fat fraction in multiple myeloma [10].
As expected,
our results highlight a correlation between ADC and fat fraction values.
The lack of correlation with bone marrow percentage may be explained in part by the fact that bone aspiration only samples a focal region within the right iliac crest and that,
whilst ROIs were drawn on 3 slices,
it is difficult to guarantee that the sampled region was segmented.
Additionally,
it may be that this study was not sufficiently powered to demonstrate a correlation between plasma cell percentage and other quantitative parameters.
In summary,
our results suggest that SUV,
ADC and FF are complementary.
Our findings support the use of functional parameters in initial assessment complementing other diagnostic interventions.