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Yi X, Zhan H, Lyu J, Du J, Dai M, Zhao M, Zhang Y, Zhou C, Xu X, Fan Y, Li L, Dong B, Jiang X, Xiao Z, Zhou J, Zhao M, Zhang J, Fu Y, Chen T, Xu Y, Tian J, Liu Q, Zeng H. A chest CT-based nomogram for predicting survival in acute myeloid leukemia. BMC Cancer 2024; 24:458. [PMID: 38609917 PMCID: PMC11010287 DOI: 10.1186/s12885-024-12188-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 03/26/2024] [Indexed: 04/14/2024] Open
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.
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Affiliation(s)
- Xiaoping Yi
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Changsha, China
- National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Central South University, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
| | - Huien Zhan
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Juan Du
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Min Dai
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Min Zhao
- Department of Nuclear Medicine, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yu Zhang
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Cheng Zhou
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
| | - Xin Xu
- Department of Geriatrics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yi Fan
- Department of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Lin Li
- Department of Hematology, Hunan Provincial People' Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, China
| | - Baoxia Dong
- Department of Hematology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, Shanghai,, China
| | - Xinya Jiang
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, China
| | - Zeyu Xiao
- The Guangzhou Key Laboratory of Molecular and Functional Imaging for Clinical Translation, The First Affiliated Hospital of Jinan University, Guangzhou, China
| | - 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, Shenzhen, China
| | - Minyi Zhao
- Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Jian Zhang
- Department of Hematology, The Third Xiangya hospital, Central South University, Changsha, China
| | - Yan Fu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - 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, Shenzhen, China
| | - Yang Xu
- Department of Hematology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
| | - Qifa Liu
- Department of Hematology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Hui Zeng
- Department of Hematology, The First Affiliated Hospital of Jinan University, Guangzhou, China.
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