Wegner Franz, Sieren Malte Maria, Grasshoff Hanna, Berkel Lennart, Rowold Christoph, Röttgerding Marcel Philipp, Khalil Soleiman, Mogadas Sam, Nensa Felix, Hosch René, Riemekasten Gabriela, Hamm Anna Franziska, von Bubnoff Nikolas, Barkhausen Jörg, Kloeckner Roman, Khandanpour Cyrus, Leitner Theo
Institute of Interventional Radiology, University Hospital Schleswig-Holstein, Lübeck, Germany.
Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering, Fraunhofer IMTE, Lübeck, Germany.
Sci Rep. 2025 Jul 21;15(1):26455. doi: 10.1038/s41598-025-11560-3.
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.
本研究的目的是评估基于体积分析的人工智能身体成分分析（BCA）算法在多发性骨髓瘤（MM）中的益处。因此，对一个包含91例MM患者的回顾性单中心队列进行了分析。该BCA算法由卷积神经网络驱动，基于常规CT扫描对组织成分和骨密度进行量化。研究了BCA数据与人口统计学/临床参数之间的相关性。确定了BCA内型，并比较了BCA衍生的患者聚类之间的生存率。具有高危细胞遗传学特征的患者心脏标志物指数值升高。在修订后的国际分期系统（R-ISS）各分类中，BCA参数未显示出显著差异。然而，与无疾病进展的患者相比，随访期间疾病进展或死亡的患者皮下脂肪组织和总脂肪组织体积均显著更低。聚类分析揭示了两种不同的BCA内型，其中一组显示出显著更好的生存率。此外，与仅基于高危细胞遗传学或R-ISS的模型相比，由临床参数和BCA数据组成的联合模型对疾病进展具有更高的预测能力。这些发现强调了BCA在改善MM患者分层和完善预后模型方面的潜力。
