AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma
Abstract The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantifi...
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| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-11560-3 |
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| author | Franz Wegner Malte Maria Sieren Hanna Grasshoff Lennart Berkel Christoph Rowold Marcel Philipp Röttgerding Soleiman Khalil Sam Mogadas Felix Nensa René Hosch Gabriela Riemekasten Anna Franziska Hamm Nikolas von Bubnoff Jörg Barkhausen Roman Kloeckner Cyrus Khandanpour Theo Leitner |
| author_facet | Franz Wegner Malte Maria Sieren Hanna Grasshoff Lennart Berkel Christoph Rowold Marcel Philipp Röttgerding Soleiman Khalil Sam Mogadas Felix Nensa René Hosch Gabriela Riemekasten Anna Franziska Hamm Nikolas von Bubnoff Jörg Barkhausen Roman Kloeckner Cyrus Khandanpour Theo Leitner |
| author_sort | Franz Wegner |
| collection | DOAJ |
| description | Abstract The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. Correlations between BCA data and demographic/clinical parameters were investigated. BCA-endotypes were identified and survival rates were compared between BCA-derived patient clusters. Patients with high-risk cytogenetics exhibited elevated cardiac marker index values. Across Revised-International Staging System (R-ISS) categories, BCA parameters did not show significant differences. However, both subcutaneous and total adipose tissue volumes were significantly lower in patients with progressive disease or death during follow-up compared to patients without progression. Cluster analysis revealed two distinct BCA-endotypes, with one group displaying significantly better survival. Furthermore, a combined model composed of clinical parameters and BCA data demonstrated a higher predictive capability for disease progression compared to models based solely on high-risk cytogenetics or R-ISS. These findings underscore the potential of BCA to improve patient stratification and refining prognostic models in MM. |
| format | Article |
| id | doaj-art-5cee6133d78f4f899063d17c5b4a09f0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-5cee6133d78f4f899063d17c5b4a09f02025-08-20T03:46:04ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-11560-3AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myelomaFranz Wegner0Malte Maria Sieren1Hanna Grasshoff2Lennart Berkel3Christoph Rowold4Marcel Philipp Röttgerding5Soleiman Khalil6Sam Mogadas7Felix Nensa8René Hosch9Gabriela Riemekasten10Anna Franziska Hamm11Nikolas von Bubnoff12Jörg Barkhausen13Roman Kloeckner14Cyrus Khandanpour15Theo Leitner16Institute of Interventional Radiology, University Hospital Schleswig-HolsteinInstitute of Interventional Radiology, University Hospital Schleswig-HolsteinClinic of Rheumatology and Clinical Immunology, University Hospital Schleswig-HolsteinInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig-HolsteinDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckInstitute of Interventional Radiology, University Hospital Schleswig-HolsteinInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital EssenInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital EssenClinic of Rheumatology and Clinical Immunology, University Hospital Schleswig-HolsteinDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig-HolsteinInstitute of Interventional Radiology, University Hospital Schleswig-HolsteinDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of LübeckAbstract The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). Therefore, a retrospective monocentric cohort of 91 MM patients was analyzed. The BCA algorithm, powered by a convolutional neural network, quantified tissue compartments and bone density based on routine CT scans. Correlations between BCA data and demographic/clinical parameters were investigated. BCA-endotypes were identified and survival rates were compared between BCA-derived patient clusters. Patients with high-risk cytogenetics exhibited elevated cardiac marker index values. Across Revised-International Staging System (R-ISS) categories, BCA parameters did not show significant differences. However, both subcutaneous and total adipose tissue volumes were significantly lower in patients with progressive disease or death during follow-up compared to patients without progression. Cluster analysis revealed two distinct BCA-endotypes, with one group displaying significantly better survival. Furthermore, a combined model composed of clinical parameters and BCA data demonstrated a higher predictive capability for disease progression compared to models based solely on high-risk cytogenetics or R-ISS. These findings underscore the potential of BCA to improve patient stratification and refining prognostic models in MM.https://doi.org/10.1038/s41598-025-11560-3 |
| spellingShingle | Franz Wegner Malte Maria Sieren Hanna Grasshoff Lennart Berkel Christoph Rowold Marcel Philipp Röttgerding Soleiman Khalil Sam Mogadas Felix Nensa René Hosch Gabriela Riemekasten Anna Franziska Hamm Nikolas von Bubnoff Jörg Barkhausen Roman Kloeckner Cyrus Khandanpour Theo Leitner AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma Scientific Reports |
| title | AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma |
| title_full | AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma |
| title_fullStr | AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma |
| title_full_unstemmed | AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma |
| title_short | AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma |
| title_sort | ai based body composition analysis of ct data has the potential to predict disease course in patients with multiple myeloma |
| url | https://doi.org/10.1038/s41598-025-11560-3 |
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