A chest CT-based nomogram for predicting survival in acute myeloid leukemia
Xiaoping Yi,
Huien Zhan,
Jun Lyu,
Juan Du,
Min Dai,
Min Zhao,
Yu Zhang,
Cheng Zhou,
Xin Xu,
Yi Fan,
Lin Li,
Baoxia Dong,
Xinya Jiang,
Zeyu Xiao,
Jihao Zhou,
Minyi Zhao,
Jian Zhang,
Yan Fu,
Tingting Chen,
Yang Xu,
Jie Tian,
Qifa Liu,
Hui Zeng
Affiliations
Xiaoping Yi
Department of Radiology, Xiangya Hospital, Central South University
Huien Zhan
Department of Hematology, The First Affiliated Hospital of Jinan University
Jun Lyu
Department of Clinical Research, The First Affiliated Hospital of Jinan University
Juan Du
Department of Hematology, The First Affiliated Hospital of Jinan University
Min Dai
Department of Hematology, Nanfang Hospital, Southern Medical University
Min Zhao
Department of Nuclear Medicine, The Third Xiangya Hospital, Central South University
Yu Zhang
Department of Hematology, Nanfang Hospital, Southern Medical University
Cheng Zhou
Department of Hematology, Xiangya Hospital, Central South University
Xin Xu
Department of Geriatrics, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology
Yi Fan
Department of Hematology, The First Affiliated Hospital of Soochow University
Lin Li
Department of Hematology, Hunan Provincial People’ Hospital, The First Affiliated Hospital of Hunan Normal University
Baoxia Dong
Department of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine
Xinya Jiang
Department of Hematology, The First Affiliated Hospital of Jinan University
Zeyu Xiao
The Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, The First Affiliated Hospital of Jinan University
Jihao Zhou
Department of Hematology, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology
Minyi Zhao
Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University
Jian Zhang
Department of Hematology, The Third Xiangya hospital, Central South University
Yan Fu
Department of Radiology, Xiangya Hospital, Central South University
Tingting Chen
Department of Hematology, Shenzhen People’s Hospital, Second Clinical Medical College of Jinan University, First Affiliated Hospital of Southern University of Science and Technology
Yang Xu
Department of Hematology, The First Affiliated Hospital of Soochow University
Jie Tian
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences
Qifa Liu
Department of Hematology, Nanfang Hospital, Southern Medical University
Hui Zeng
Department of Hematology, The First Affiliated Hospital of Jinan University
Abstract Background The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to predict prognosis for acute myeloid leukemia (AML). Methods 952 patients with pathologically-confirmed AML were retrospectively enrolled between 2010 and 2020. CT-derived features (including body composition and subcutaneous fat features), were obtained from the initial chest CT images and were used to build models to predict the prognosis. A CT-derived MSF nomogram was constructed using multivariate Cox regression incorporating CT-based features. The performance of the prediction models was assessed with discrimination, calibration, decision curves and improvements. Results Three CT-derived features, including myosarcopenia, spleen_CTV, and SF_CTV (MSF) were identified as the independent predictors for prognosis in AML (P < 0.01). A CT-MSF nomogram showed a performance with AUCs of 0.717, 0.794, 0.796 and 0.792 for predicting the 1-, 2-, 3-, and 5-year overall survival (OS) probabilities in the validation cohort, which were significantly higher than the ELN risk model. Moreover, a new MSN stratification system (MSF nomogram plus ELN risk model) could stratify patients into new high, intermediate and low risk group. Patients with high MSN risk may benefit from intensive treatment (P = 0.0011). Conclusions In summary, the chest CT-MSF nomogram, integrating myosarcopenia, spleen_CTV, and SF_CTV features, could be used to predict prognosis of AML.
