Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer
Abstract Objectives This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC). Methods Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were re...
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SpringerOpen
2025-02-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01910-0 |
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| author | Yilong Huang Hanxue Cun Zhanglin Mou Zhonghang Yu Chunmei Du Lan Luo Yuanming Jiang Yancui Zhu Zhenguang Zhang Xin Chen Bo He Zaiyi Liu |
| author_facet | Yilong Huang Hanxue Cun Zhanglin Mou Zhonghang Yu Chunmei Du Lan Luo Yuanming Jiang Yancui Zhu Zhenguang Zhang Xin Chen Bo He Zaiyi Liu |
| author_sort | Yilong Huang |
| collection | DOAJ |
| description | Abstract Objectives This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC). Methods Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were retrospectively enrolled in this multicenter study. Body composition metrics, including the area of skeletal muscle, intermuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, muscle radiodensity, and derivative parameters from five basic metrics mentioned before, were calculated based on preoperative non-contrast-enhanced chest CT images at L1 level. The Cox proportional hazards regression analysis was used to evaluate the association between body composition metrics and survival outcomes including overall survival (OS) and disease-free survival (DFS). Results A total of 2712 patients (mean age, 61.53 years; 1146 females) were evaluated. A total of 635 patients (23.41%) died. 465 patients (19.51%) experienced recurrence and/or distant metastasis. After multivariable adjustment, skeletal muscle index (SMI, HR = 0.86), intermuscular adipose index (IMAI, HR = 1.49), and subcutaneous adipose index (SAI, HR = 0.96) were associated with OS. Similar results were found after stratification by gender, TNM stage, and center. There was no significant association between all body composition metrics and DFS (all p > 0.05). The body composition metrics significantly enhance the model including clinicopathological factors, resulting in an improved AUC for predicting 1-year and 3-year OS, with AUC values of 0.707 and 0.733, respectively. Conclusions SMI, IMAI, and SAI body composition metrics have been identified as independent prognostic factors and may indicate mortality risk for resectable NSCLC patients. Critical relevance statement Our findings emphasize the significance of muscle mass, quality, and fat energy storage in clinical decision-making for patients with non-small cell lung cancer (NSCLC). Nutritional and exercise interventions targeting muscle quality and energy storage could be considered for patients with NSCLC. Key Points Multiparameter body composition analysis is associated with the clinical outcome in NSCLC patients. Assessing muscle mass, quality, and adipose tissue helps predict overall survival in NSCLC. The quantity and distribution of body composition can contribute to unraveling the adiposity paradox. Graphical Abstract |
| format | Article |
| id | doaj-art-47b1d7cda9e94013b0efe387c3f5e6c3 |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
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| series | Insights into Imaging |
| spelling | doaj-art-47b1d7cda9e94013b0efe387c3f5e6c32025-02-09T12:40:30ZengSpringerOpenInsights into Imaging1869-41012025-02-0116111210.1186/s13244-025-01910-0Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancerYilong Huang0Hanxue Cun1Zhanglin Mou2Zhonghang Yu3Chunmei Du4Lan Luo5Yuanming Jiang6Yancui Zhu7Zhenguang Zhang8Xin Chen9Bo He10Zaiyi Liu11Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of SciencesDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Intensive Care Unit, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Intensive Care Unit, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of TechnologyDepartment of Medical Imaging, The First Affiliated Hospital of Kunming Medical UniversityDepartment of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityAbstract Objectives This study investigates the association between baseline CT body composition parameters and clinical outcomes in patients with resectable non-small cell lung cancer (NSCLC). Methods Patients who underwent surgical resection for NSCLC between January 2006 and December 2017 were retrospectively enrolled in this multicenter study. Body composition metrics, including the area of skeletal muscle, intermuscular adipose tissue, subcutaneous adipose tissue, visceral adipose tissue, muscle radiodensity, and derivative parameters from five basic metrics mentioned before, were calculated based on preoperative non-contrast-enhanced chest CT images at L1 level. The Cox proportional hazards regression analysis was used to evaluate the association between body composition metrics and survival outcomes including overall survival (OS) and disease-free survival (DFS). Results A total of 2712 patients (mean age, 61.53 years; 1146 females) were evaluated. A total of 635 patients (23.41%) died. 465 patients (19.51%) experienced recurrence and/or distant metastasis. After multivariable adjustment, skeletal muscle index (SMI, HR = 0.86), intermuscular adipose index (IMAI, HR = 1.49), and subcutaneous adipose index (SAI, HR = 0.96) were associated with OS. Similar results were found after stratification by gender, TNM stage, and center. There was no significant association between all body composition metrics and DFS (all p > 0.05). The body composition metrics significantly enhance the model including clinicopathological factors, resulting in an improved AUC for predicting 1-year and 3-year OS, with AUC values of 0.707 and 0.733, respectively. Conclusions SMI, IMAI, and SAI body composition metrics have been identified as independent prognostic factors and may indicate mortality risk for resectable NSCLC patients. Critical relevance statement Our findings emphasize the significance of muscle mass, quality, and fat energy storage in clinical decision-making for patients with non-small cell lung cancer (NSCLC). Nutritional and exercise interventions targeting muscle quality and energy storage could be considered for patients with NSCLC. Key Points Multiparameter body composition analysis is associated with the clinical outcome in NSCLC patients. Assessing muscle mass, quality, and adipose tissue helps predict overall survival in NSCLC. The quantity and distribution of body composition can contribute to unraveling the adiposity paradox. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01910-0Lung cancerBody compositionDisease-free survivalOverall survivalComputed tomography |
| spellingShingle | Yilong Huang Hanxue Cun Zhanglin Mou Zhonghang Yu Chunmei Du Lan Luo Yuanming Jiang Yancui Zhu Zhenguang Zhang Xin Chen Bo He Zaiyi Liu Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer Insights into Imaging Lung cancer Body composition Disease-free survival Overall survival Computed tomography |
| title | Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer |
| title_full | Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer |
| title_fullStr | Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer |
| title_full_unstemmed | Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer |
| title_short | Multiparameter body composition analysis on chest CT predicts clinical outcomes in resectable non-small cell lung cancer |
| title_sort | multiparameter body composition analysis on chest ct predicts clinical outcomes in resectable non small cell lung cancer |
| topic | Lung cancer Body composition Disease-free survival Overall survival Computed tomography |
| url | https://doi.org/10.1186/s13244-025-01910-0 |
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