OpenLB
Open Library of Bioscience
Advanced Search
Medicina (Kaunas, Lithuania)
Jan 21, 2021;57 (2):

Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?

Alberto Stefano Tagliafico, Alida Dominietto, Liliana Belgioia, Cristina Campi, Daniela Schenone, Michele Piana
Author Information
  1. Alberto Stefano Tagliafico: Department of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy.
  2. Alida Dominietto: Department of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy.
  3. Liliana Belgioia: Department of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy. ORCID
  4. Cristina Campi: Department of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy. ORCID
  5. Daniela Schenone: Department of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy.
  6. Michele Piana: Department of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy.
PMID: 33494449 DOI: 10.3390/medicina57020094

Abstract

Multiple Myeloma (MM) is the second most common type of hematological disease and, although it is rare among patients under 40 years of age, its incidence rises in elderly subjects. MM manifestations are usually identified through hyperCalcemia, Renal failure, Anaemia, and lytic Bone lesions (CRAB). In particular, the extent of the bone disease is negatively related to a decreased quality of life in patients and, in general, bone disease in MM increases both morbidity and mortality. The detection of lytic bone lesions on imaging, especially computerized tomography (CT) and Magnetic Resonance Imaging (MRI), is becoming crucial from the clinical viewpoint to separate asymptomatic from symptomatic MM patients and the detection of focal lytic lesions in these imaging data is becoming relevant even when no clinical symptoms are present. Therefore, radiology is pivotal in the staging and accurate management of patients with MM even in early phases of the disease. In this review, we describe the opportunities offered by quantitative imaging and radiomics in multiple myeloma. At the present time there is still high variability in the choice between various imaging methods to study MM patients and high variability in image interpretation with suboptimal agreement among readers even in tertiary centers. Therefore, the potential of medical imaging for patients affected by MM is still to be completely unveiled. In the coming years, new insights to study MM with medical imaging will derive from artificial intelligence (AI) and radiomics usage in different bone lesions and from the wide implementations of quantitative methods to report CT and MRI. Eventually, medical imaging data can be integrated with the patient's outcomes with the purpose of finding radiological biomarkers for predicting the prognostic flow and therapeutic response of the disease.

Keywords

artificial intelligence computed tomography diagnosis imaging multiple myeloma prognosis radiomics

References

  1. J Clin Oncol. 2017 Sep 1;35(25):2911-2918 [PMID: 28686535]
  2. Spine (Phila Pa 1976). 2010 Oct 15;35(22):E1221-9 [PMID: 20562730]
  3. Blood. 2018 Jul 5;132(1):59-66 [PMID: 29784643]
  4. Lancet Oncol. 2014 Nov;15(12):e538-48 [PMID: 25439696]
  5. Cancers (Basel). 2020 Mar 24;12(3): [PMID: 32213834]
  6. Phys Med Biol. 2016 Jul 7;61(13):R150-66 [PMID: 27269645]
  7. Skeletal Radiol. 2019 Jan;48(1):143-147 [PMID: 30003278]
  8. Breast. 2020 Feb;49:74-80 [PMID: 31739125]
  9. Eur Radiol. 2020 Dec;30(12):6635-6644 [PMID: 32661585]
  10. Blood Cancer J. 2017 Aug 25;7(8):e599 [PMID: 28841211]
  11. Radiother Oncol. 2021 Feb;155:188-203 [PMID: 33096167]
  12. Cancer Imaging. 2020 Apr 28;20(1):31 [PMID: 32345379]
  13. Hematology Am Soc Hematol Educ Program. 2015;2015:272-8 [PMID: 26637733]
  14. Skeletal Radiol. 2018 Mar;47(3):351-361 [PMID: 29222688]
  15. Sci Rep. 2015 Aug 17;5:13087 [PMID: 26278466]
  16. Eur J Radiol. 2020 Sep;130:109154 [PMID: 32629214]
  17. Radiology. 2016 Feb;278(2):563-77 [PMID: 26579733]
  18. Eur J Radiol. 2020 Oct;131:109214 [PMID: 32835853]
  19. Magn Reson Imaging. 2012 Nov;30(9):1323-41 [PMID: 22770690]
  20. Blood Cancer J. 2018 Oct 4;8(10):95 [PMID: 30287814]
  21. Insights Imaging. 2015 Apr;6(2):141-55 [PMID: 25763994]
  22. AJR Am J Roentgenol. 2016 Jun;206(6):1217-21 [PMID: 27058462]
  23. Expert Rev Precis Med Drug Dev. 2016;1(2):207-226 [PMID: 28042608]
  24. Semin Musculoskelet Radiol. 2011 Jul;15(3):269-80 [PMID: 21644200]
  25. J Clin Oncol. 2010 Mar 20;28(9):1606-10 [PMID: 20177023]
  26. Semin Musculoskelet Radiol. 2017 Nov;21(5):630-647 [PMID: 29025191]
  27. Clin Physiol Funct Imaging. 2017 Mar;37(2):143-147 [PMID: 26211508]
  28. Radiology. 2019 Apr;291(1):5-13 [PMID: 30806604]
  29. Acta Radiol. 2020 Dec 3;:284185120977035 [PMID: 33269940]
  30. Insights Imaging. 2016 Aug;7(4):481-4 [PMID: 27198551]
  31. Eur J Cancer. 2012 Mar;48(4):441-6 [PMID: 22257792]
  32. Eur J Radiol. 2019 Dec;121:108739 [PMID: 31733431]
  33. Eur Radiol. 2016 Jul;26(7):2089-98 [PMID: 26494640]
  34. Cancer. 2016 Sep 1;122(17):2708-14 [PMID: 27219108]
  35. Eur Radiol Exp. 2018 Nov 14;2(1):36 [PMID: 30426318]
  36. Insights Imaging. 2020 Jun 4;11(1):76 [PMID: 32500316]
  37. Eur Radiol. 2018 Dec;28(12):5083-5090 [PMID: 29882069]
  38. Rofo. 2019 Nov;191(11):998-1009 [PMID: 31137045]
  39. Eur J Nucl Med Mol Imaging. 2018 May;45(5):712-719 [PMID: 29270787]
  40. Am Soc Clin Oncol Educ Book. 2018 May 23;38:638-646 [PMID: 30231385]
  41. Medicina (Kaunas). 2020 Apr 23;56(4): [PMID: 32340143]
  42. Nat Rev Clin Oncol. 2017 Feb;14(2):100-113 [PMID: 27531699]
  43. J Clin Oncol. 2015 Sep 10;33(26):2863-9 [PMID: 26240224]
  44. Semin Musculoskelet Radiol. 2020 Jun;24(3):323-330 [PMID: 32987429]
  45. Radiology. 2014 Jun;271(3):805-13 [PMID: 24592961]
  46. Nat Rev Clin Oncol. 2017 Dec;14(12):749-762 [PMID: 28975929]

MeSH Term

Aged
Artificial Intelligence
Humans
Magnetic Resonance Imaging
Multiple Myeloma
Quality of Life
Tomography, X-Ray Computed
Journal Article Review
Article has an altmetric score of 1

Word Cloud

Created with Highcharts 10.0.0MMimagingpatientsdiseaselesionsbonelyticevenradiomicsmedicalMultipleamongyearsdetectiontomographyCTImagingMRIbecomingclinicaldatapresentThereforequantitativemultiplemyelomastillhighvariabilitymethodsstudyintelligenceMyelomasecondcommontypehematologicalalthoughrare40ageincidenceriseselderlysubjectsmanifestationsusuallyidentifiedhyperCalcemiaRenalfailureAnaemiaBoneCRABparticularextentnegativelyrelateddecreasedqualitylifegeneralincreasesmorbiditymortalityespeciallycomputerizedMagneticResonancecrucialviewpointseparateasymptomaticsymptomaticfocalrelevantsymptomsradiologypivotalstagingaccuratemanagementearlyphasesreviewdescribeopportunitiesofferedtimechoicevariousimageinterpretationsuboptimalagreementreaderstertiarycenterspotentialaffectedcompletelyunveiledcomingnewinsightswillderiveartificialAIusagedifferentwideimplementationsreportEventuallycanintegratedpatient'soutcomespurposefindingradiologicalbiomarkerspredictingprognosticflowtherapeuticresponseQuantitativeRadiomicsMyeloma:PotentialOpportunity?artificialcomputeddiagnosisprognosis

Similar Articles

Cited By