Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers.
Multiple myeloma (MM)
diffusion-weighted whole-body imaging with background body signal suppression (DWIBS)
magnetic resonance imaging (MRI)
modified Dixon chemical-shift imaging (mDIXON)
tumor burden
Journal
Quantitative imaging in medicine and surgery
ISSN: 2223-4292
Titre abrégé: Quant Imaging Med Surg
Pays: China
ID NLM: 101577942
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
15
12
2020
accepted:
01
04
2021
entrez:
3
8
2021
pubmed:
4
8
2021
medline:
4
8
2021
Statut:
ppublish
Résumé
To assess the quantification of tumor burden in multiple myeloma (MM) patients using whole-body magnetic resonance imaging (MRI) and to identify the correlation between MRI parameters and prognostic biomarkers. We retrospectively analyzed 95 newly diagnosed MM patients treated at our hospital from June 2018 to March 2020. All patients underwent whole-body MRI examination, including diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging (mDIXON), and short TI inversion recovery (STIR) sequences. The MRI presentation was used to determine MM infiltration patterns and calculate apparent diffusion coefficient (ADC) and a fat fraction (FF). The one-way ANOVA and Kruskal-Wallis test were used to compare the differences of these values between DS, ISS, and R-ISS stages in different MM infiltration patterns. Spearman correlation test was used for correlation analysis of ADC and FF against prognostic biomarkers, and two independent sample The MRI presentation was classified into normal pattern (36 patients; 37.9%), diffuse (27 patients; 28.4%), and focal (32 patients; 33.7%) infiltration patterns. Statistically significant ADC and FF differences between different DS, ISS, and R-ISS stages were observed in normal/diffuse infiltration patterns but not in focal infiltration patterns. The ADC and FF of the normal/diffuse infiltration pattern showed correlations with hemoglobin, β2-microglobulin, bone marrow plasma cells, flow cytometry of bone marrow cells, and serum monoclonal protein. In contrast, ADC in focal infiltration patterns was negatively correlated with β2-microglobulin and C-reactive protein. The FF of patients with a normal/diffuse infiltration pattern was higher in the low free light-chain ratio group than that in the high free light-chain ratio group (P=0.023). Our observations indicate that quantitative whole-body functional MRI examination may serve as an effective complement to imaging diagnosis based on morphology and provide further information on the tumor burden of patients with MM.
Sections du résumé
BACKGROUND
BACKGROUND
To assess the quantification of tumor burden in multiple myeloma (MM) patients using whole-body magnetic resonance imaging (MRI) and to identify the correlation between MRI parameters and prognostic biomarkers.
METHODS
METHODS
We retrospectively analyzed 95 newly diagnosed MM patients treated at our hospital from June 2018 to March 2020. All patients underwent whole-body MRI examination, including diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging (mDIXON), and short TI inversion recovery (STIR) sequences. The MRI presentation was used to determine MM infiltration patterns and calculate apparent diffusion coefficient (ADC) and a fat fraction (FF). The one-way ANOVA and Kruskal-Wallis test were used to compare the differences of these values between DS, ISS, and R-ISS stages in different MM infiltration patterns. Spearman correlation test was used for correlation analysis of ADC and FF against prognostic biomarkers, and two independent sample
RESULTS
RESULTS
The MRI presentation was classified into normal pattern (36 patients; 37.9%), diffuse (27 patients; 28.4%), and focal (32 patients; 33.7%) infiltration patterns. Statistically significant ADC and FF differences between different DS, ISS, and R-ISS stages were observed in normal/diffuse infiltration patterns but not in focal infiltration patterns. The ADC and FF of the normal/diffuse infiltration pattern showed correlations with hemoglobin, β2-microglobulin, bone marrow plasma cells, flow cytometry of bone marrow cells, and serum monoclonal protein. In contrast, ADC in focal infiltration patterns was negatively correlated with β2-microglobulin and C-reactive protein. The FF of patients with a normal/diffuse infiltration pattern was higher in the low free light-chain ratio group than that in the high free light-chain ratio group (P=0.023).
CONCLUSIONS
CONCLUSIONS
Our observations indicate that quantitative whole-body functional MRI examination may serve as an effective complement to imaging diagnosis based on morphology and provide further information on the tumor burden of patients with MM.
Identifiants
pubmed: 34341748
doi: 10.21037/qims-20-1361
pii: qims-11-08-3767
pmc: PMC8245954
doi:
Banques de données
figshare
['10.6084/m9.figshare.14204678.v2']
Types de publication
Journal Article
Langues
eng
Pagination
3767-3780Informations de copyright
2021 Quantitative Imaging in Medicine and Surgery. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-1361). The authors have no conflicts of interest to declare.
Références
J Natl Compr Canc Netw. 2016 Apr;14(4):389-400
pubmed: 27059188
Br J Radiol. 2018 Jul;91(1088):20170389
pubmed: 29393672
Blood. 2018 Jul 5;132(1):59-66
pubmed: 29784643
AJR Am J Roentgenol. 2014 Oct;203(4):854-62
pubmed: 25247952
Haematologica. 2015 Jun;100(6):818-25
pubmed: 25795721
Clin Lymphoma Myeloma Leuk. 2015 Sep;15(9):541-5
pubmed: 26119495
Eur J Radiol. 2005 Jul;55(1):56-63
pubmed: 15950101
Cancer. 1975 Sep;36(3):842-54
pubmed: 1182674
Blood. 2016 Jun 16;127(24):2955-62
pubmed: 27002115
Eur Radiol. 2018 Dec;28(12):5001-5009
pubmed: 29858641
Eur Radiol. 2018 Apr;28(4):1701-1708
pubmed: 29143105
J Clin Oncol. 2015 Sep 10;33(26):2863-9
pubmed: 26240224
Cancers (Basel). 2020 Sep 07;12(9):
pubmed: 32906608
Radiology. 2011 Dec;261(3):700-18
pubmed: 22095994
J Clin Oncol. 2005 May 20;23(15):3412-20
pubmed: 15809451
Leukemia. 2014 Dec;28(12):2402-3
pubmed: 25079172
J Clin Oncol. 2007 Mar 20;25(9):1121-8
pubmed: 17296972
PLoS One. 2017 Jul 3;12(7):e0180562
pubmed: 28672007
Diagn Interv Imaging. 2019 Sep;100(9):513-519
pubmed: 31130374
Radiology. 2017 Feb;282(2):484-493
pubmed: 27610934
Lancet Oncol. 2014 Nov;15(12):e538-48
pubmed: 25439696
Cancer Invest. 2017 Mar 16;35(3):195-201
pubmed: 28112977
Radiology. 2019 Jun;291(3):632-641
pubmed: 31012817
Blood. 2008 Jan 15;111(2):785-9
pubmed: 17942755
Haematologica. 2018 Nov;103(11):1772-1784
pubmed: 30171031
J Clin Oncol. 2015 Feb 20;33(6):657-64
pubmed: 25605835
Cancer. 2004 Dec 1;101(11):2599-604
pubmed: 15503306
J Clin Oncol. 2010 Mar 20;28(9):1606-10
pubmed: 20177023
Br J Haematol. 2017 Jan;176(2):222-233
pubmed: 27766627
Oncotarget. 2018 May 18;9(38):25254-25264
pubmed: 29861868
Br J Radiol. 2018 Sep;91(1089):20170344
pubmed: 28936896
Am J Hematol. 2012 Sep;87(9):861-4
pubmed: 22641455
Br J Haematol. 2014 Jun;165(6):777-85
pubmed: 24628463
Quant Imaging Med Surg. 2020 Aug;10(8):1614-1635
pubmed: 32742956
Lancet Oncol. 2016 Aug;17(8):e328-e346
pubmed: 27511158
Int J Cancer. 2014 Nov 15;135(10):2380-6
pubmed: 24706394
Eur Radiol. 2012 May;22(5):1114-21
pubmed: 22138735