ArticleEuropean journal of nuclear medicine and molecular imaging2024
Artificial intelligence-based, volumetric assessment of the bone marrow metabolic activity in [
Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The [European journal of nuclear medicine and molecular imaging · 2026Pooled it
- FDG-PET medullary total tumor volume highlights high-risk patients with newly diagnosed multiple myeloma in CASSIOPEIA trial.Blood advances · 2026Trial
- Validation of novel low-dose CT methods for quantifying bone marrow in the appendicular skeleton of patients with multiple myeloma: initial results from the [European journal of nuclear medicine and molecular imaging · 2026Trial
- Automated Deauville Score computation from baseline [¹⁸F]FDG PET/CT predicts progression-free survival in multiple myeloma: a radiogenomic framework.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- AI-Quantified ¹¹C-MET PET/CT bone marrow metabolic activity for prognostic assessment in newly diagnosed multiple myeloma.European journal of nuclear medicine and molecular imaging · 2026Article
- Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [European journal of nuclear medicine and molecular imaging · 2026Article
- Lesion conspicuity by size in [EJNMMI research · 2026Article
- Recent Updates and Advancements in the Diagnosis and Management of Plasma Cell Dyscrasias.Cureus · 2026Review
- Bone disease burden does not impact overall survival in newly diagnosed patients with multiple myeloma - a single center, retrospective imaging analysis on 119 patients.Journal of bone oncology · 2025Article
- Refining PET-based response in extramedullary multiple myeloma using total lesion glycolysis.Leukemia · 2025Article
- A feasibility study of [18F] FDG PET/CT radiomics in predicting high-risk cytogenetic abnormalities in multiple myeloma.EJNMMI research · 2025Article
- Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine.The British journal of radiology · 2025Review
- Whole-body low-dose computed tomography in patients with newly diagnosed multiple myeloma predicts cytogenetic risk: a deep learning radiogenomics study.Skeletal radiology · 2025Article
- Prognostic significance of FDG-PET/CT based radiomics analysis in newly-diagnosed multiple myeloma: a comparative study with clinical assessment.Frontiers in oncology · 2025Article
- Long-axial-field of view in prostate cancer next generation imaging: the launch pad of theragnostic.European journal of nuclear medicine and molecular imaging · 2024Article
- Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [EJNMMI research · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMultiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [ MATERIALS AND
methodsForty-four, previously untreated MM patients underwent whole-body [
resultsMedian follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [
conclusionsThe AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.
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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.