AUTHOR=Yuan Tao , Zhang Yaxin , Liu Yawu , Gao Guodong , Quan Guanmin 

TITLE=Impact of body composition metrics extracted from QCT and the corresponding nomogram on the evaluation of survival prognosis in AML patients

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1498024

DOI=10.3389/fonc.2025.1498024

ISSN=2234-943X

ABSTRACT=ObjectivesThis study aimed to investigate whether the body composition metrics extracted from quantitative CT (QCT) are associated with the survival prognosis of acute myeloid leukemia (AML) patients and to evaluate the impact of a nomogram based on QCT and clinical–physical factors in predicting the prognosis of AML.MethodsThe clinical factors and QCT metrics of 127 AML patients undergoing initial chest CT were analyzed retrospectively. The AML patients were divided into favorable and poor prognosis groups based on the threshold of median overall survival (OS). A QCT metrics- and clinical factors-derived nomogram was constructed using multivariate Cox regression. The performance of the nomogram was assessed with a receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsCompared to patients in the favorable survival prognosis group, patients with poor prognosis were older (p = 0.027), had higher risk stratification (p = 0.006), more positive minimal residual disease (MRD) (p = 0.014), lower skeletal muscle index (SMI) (p = 0.045), and a higher incidence of volumetric bone mineral density (vBMD) ≤ 120 (p = 0.035). Older age, higher risk stratification, positive MRD, and SMI < 15.74cm2/m2 were independent risk factors for poor prognosis in AML patients. The areas under the ROC curve (AUCs) of the nomogram, which included SMI and independent clinical factors, for predicting 1- and 2-year OS were 0.792 and 0.794, respectively. The calibration curve and DCA demonstrated the good performance of the nomogram prediction model.ConclusionsSarcopenia revealed by QCT, integrated into a nomogram with age, risk stratification, and MRD, can facilitate individualized prediction of survival prognosis in AML patients.