AI-based 3D-segmentation quantifies sarcopenia in multiple myeloma patients
Background: Sarcopenia is characterized by a loss of muscle mass and strength, resulting in functional limitations and an increased risk of falls, injuries and fractures. The aim of this study was to obtain detailed information on skeletal muscle changes in patients with multiple myeloma (MM) during...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
26 September 2025
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| In: |
Diagnostics
Year: 2025, Volume: 15, Issue: 19, Pages: 1-12 |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics15192466 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.3390/diagnostics15192466 Verlag, kostenfrei, Volltext: https://www.mdpi.com/2075-4418/15/19/2466 
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| Author Notes: | Thuy-Duong Do, Tobias Nonnenmacher, Marieke Burghardt, Stefanie Zschäbitz, Marina Hajiyianni, Elias Karl Mai, Marc-Steffen Raab, Carsten Müller-Tidow, Hans-Ulrich Kauczor, Hartmut Goldschmidt and Ulrike Dapunt |
| Summary: | Background: Sarcopenia is characterized by a loss of muscle mass and strength, resulting in functional limitations and an increased risk of falls, injuries and fractures. The aim of this study was to obtain detailed information on skeletal muscle changes in patients with multiple myeloma (MM) during treatment. - Methods: A total of 51 patients diagnosed with MM who had undergone whole-body low-dose computed tomography acquisition prior to induction therapy (T1) and post autologous stem cell transplantation (T2) were examined retrospectively. Total volume (TV), muscle volume (MV) and intramuscular adipose tissue volume (IMAT) of the autochthonous back muscles, the iliopsoas muscle and the gluteal muscles were evaluated on the basis of the resulting masks of the BOA tool with the fully automated combination of TotalSegmentator and a body composition analysis. An in-house trained artificial intelligence network was used to obtain a fully automated three-dimensional segmentation assessment. - Results: Patients’ median age was 58 years (IQR 52–66), 38 were male and follow-up CT-scans were performed after a mean of 11.8 months (SD ± 3). Changes in MV and IMAT correlated significantly with Body-Mass-Index (BMI) (r = 0.7, p < 0.0001). Patients (n = 28) with a decrease in BMI (mean −2.2 kg/m2) during therapy lost MV (T1: 3419 cm3, IQR 3176–4000 cm3 vs. T2: 3226 cm3, IQR 3014–3662 cm3, p < 0.0001) whereas patients (n = 20) with an increased BMI (mean +1.4 kg/m2) showed an increase in IMAT (T1: 122 cm3, IQR 96.8–202.8 cm3 vs. T2: 145.5 cm3, IQR 115–248 cm3, p = 0.0002). Loss of MV varied between different muscle groups and was most prominent in the iliopsoas muscle (−9.8%) > gluteus maximus (−9.1%) > gluteus medius (−5.8%) > autochthonous back muscles (−4.3%) > gluteus minimus (−1.5%). Increase in IMAT in patients who gained weight was similar between muscle groups. - Conclusions: The artificial intelligence-based three-dimensional segmentation process is a reliable and time-saving method to acquire in-depth information on sarcopenia in MM patients. Loss of MV and increase in IMAT were reliably detectable and associated with changes in BMI. Loss of MV was highest in muscles with more type 2 muscle fibers (fast-twitch, high energy) whereas muscles with predominantly type 1 fibers (slow-twitch, postural control) were less affected. This study provides valuable insight into muscle changes of MM patients during treatment, which might aid in tailoring exercise interventions more precisely to patients’ needs. |
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| Item Description: | Gesehen am 09.02.2026 |
| Physical Description: | Online Resource |
| ISSN: | 2075-4418 |
| DOI: | 10.3390/diagnostics15192466 |