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

## TL;DR

AI-based body composition analysis of CT scans can predict disease progression in multiple myeloma patients, improving patient stratification and prognosis.

## Contribution

This study introduces AI-based body composition analysis as a novel tool for predicting disease outcomes in multiple myeloma.

## Key findings

- Patients with progressive disease had significantly lower adipose tissue volumes.
- Cluster analysis identified two BCA-endotypes with differing survival rates.
- A combined model of clinical and BCA data outperformed traditional models in predicting disease progression.

## 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.

The online version contains supplementary material available at 10.1038/s41598-025-11560-3.