Evidence map›Paper›PMID 33494449›Full record

ReviewMedicina (Kaunas, Lithuania)2021

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

Alberto Stefano Tagliafico, Alida Dominietto, Liliana Belgioia, Cristina Campi, Daniela Schenone, Michele Piana

Open access · goldAbstract readReview
In one paragraph

Review in Medicina (Kaunas, Lithuania), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 13 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Artificial intelligence-based application in multiple myeloma.European journal of nuclear medicine and molecular imaging · 2024
    Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Radiomic Analysis of Positron-Emission Tomography and Computed Tomography Images to Differentiate between Multiple Myeloma and Skeletal Metastases.Indian journal of nuclear medicine : IJNM : the official journal of the Society of Nuclear Medicine, India
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 1 country.

Alberto Stefano TagliaficoDepartment of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy.
Alida DominiettoDepartment of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy.
Liliana BelgioiaDepartment of Health Sciences (DISSAL), University of Genoa, 16129 Genoa, Italy.ORCID 0000-0003-0508-6344
Cristina CampiDepartment of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy.ORCID 0000-0003-2105-8554
Daniela SchenoneDepartment of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy.
Michele PianaDepartment of Mathematics (DIMA), University of Genoa, 16129 Genoa, Italy.
University of Genoa · ITOspedale Policlinico San Martino · ITSuperconducting and other Innovative Materials and Devices Institute · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Multiple MyelomaAgedArtificial IntelligenceHumansMagnetic Resonance ImagingQuality of LifeTomography, X-Ray Computedartificial intelligencecomputed tomographydiagnosisimagingmultiple myelomaprognosisradiomics

Identifiers

PMID33494449
PMCPMC7912483
OpenAlexW3125100834

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.