Evidence map›Paper›PMID 40691492›Full record

ArticleScientific reports2025

AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma.

Franz Wegner, Malte Maria Sieren, Hanna Grasshoff, Lennart Berkel, Christoph Rowold, Marcel Philipp Röttgerding, Soleiman Khalil, Sam Mogadas, Felix Nensa, René Hosch and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Observational
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

17 authors.

Franz WegnerInstitute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany. franz.wegner@uksh.de.
Malte Maria SierenInstitute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Hanna GrasshoffClinic of Rheumatology and Clinical Immunology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Lennart BerkelInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany.
Christoph RowoldDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.
Marcel Philipp RöttgerdingDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.
Soleiman KhalilDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.
Sam MogadasInstitute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Felix NensaInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
René HoschInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.
Gabriela RiemekastenClinic of Rheumatology and Clinical Immunology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Anna Franziska HammDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.
Nikolas von BubnoffDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany.
Jörg BarkhausenInstitute of Radiology and Nuclear Medicine, University Hospital Schleswig-Holstein, Lübeck, Germany.
Roman KloecknerInstitute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Cyrus KhandanpourDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany. cyrus.khandanpour@uksh.de.
Theo LeitnerDepartment of Hematology and Oncology, University Cancer Center Schleswig-Holstein, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany. theo.leitner@uksh.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Body CompositionMultiple MyelomaTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsDisease ProgressionFemaleHumansMaleMiddle AgedNeural Networks, ComputerPrognosisRetrospective Studies

Identifiers

PMID40691492
PMCPMC12280154

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.