Artificial intelligence–based, volumetric assessment of the bone marrow metabolic activity in [18F]FDG PET/CT predicts survival in multiple myeloma
Journal article, 2024

Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)–based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool. Materials and methods:
Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients’ progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated.
Median 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 [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS.
The 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.

Multiple myeloma

Deep learning

Metabolic tumor volume (MTV)


Patient survival


Artificial intelligence

Total lesion glycolysis (TLG)


Christos Sachpekidis

German Cancer Research Center (DKFZ)

Olof Enqvist

Eigenvision AB

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Johannes Ulén

Eigenvision AB

Annette Kopp-Schneider

German Cancer Research Center (DKFZ)

Leyun Pan

German Cancer Research Center (DKFZ)

Elias K. Mai

German Cancer Research Center (DKFZ)

Marina Hajiyianni

German Cancer Research Center (DKFZ)

Maximilian Merz

Leipzig University

Marc S. Raab

German Cancer Research Center (DKFZ)

Anna Jauch

Heidelberg University

Hartmut Goldschmidt

German Cancer Research Center (DKFZ)

L. Edenbrandt

Sahlgrenska University Hospital

University of Gothenburg

Antonia Dimitrakopoulou-Strauss

German Cancer Research Center (DKFZ)

European Journal of Nuclear Medicine and Molecular Imaging

1619-7070 (ISSN) 1619-7089 (eISSN)

Vol. In Press

Areas of Advance

Health Engineering

Subject Categories


Radiology, Nuclear Medicine and Medical Imaging

Cancer and Oncology





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