Quantitative Imaging and Radiomics in Multiple Myeloma: A Potential Opportunity?
Alberto Stefano TagliaficoAlida DominiettoLiliana BelgioiaCristina CampiDaniela SchenoneMichele PianaPublished in: Medicina (Kaunas, Lithuania) (2021)
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.
Keyphrases
- high resolution
- end stage renal disease
- artificial intelligence
- magnetic resonance imaging
- ejection fraction
- multiple myeloma
- chronic kidney disease
- contrast enhanced
- healthcare
- prognostic factors
- peritoneal dialysis
- bone mineral density
- type diabetes
- deep learning
- machine learning
- adipose tissue
- squamous cell carcinoma
- risk assessment
- case report
- electronic health record
- soft tissue
- skeletal muscle
- sensitive detection
- climate change
- pet ct
- patient reported outcomes
- depressive symptoms
- positron emission tomography
- real time pcr
- clinical decision support
- drug induced