Prognostic Value of Sarcopenia in Lung Cancer

Yang, Ming; Shen, Yanjiao; Lu, Tan; Liu, Weimin

doi:10.1016/j.chest.2019.04.115

Cited by 177 publications

(87 citation statements)

References 36 publications

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“…18 In previous studies, it has been shown that in NSCLC patients, the prevalence of sarcopenia varied from 43% to 46.8%. 2,19,20 The pervasiveness of sarcopenia in our study was 40.4%, which is consistent with the literature. The early diagnosis of sarcopenia can be significantly improved by the precise measurement of muscle loss associated with it.…”

Section: Discussionsupporting

confidence: 92%

“…Sarcopenia was initially defined as age-related skeletal muscle mass wasting and was reported to be caused by cancer or other underlying diseases. 2,3 There is evidence that sarcopenia is linked to a worse overall prognosis in various cancers, and the pervasiveness of sarcopenia in lung cancer is more significant compared to many other types of cancer. 18 In previous studies, it has been shown that in NSCLC patients, the prevalence of sarcopenia varied from 43% to 46.8%.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless, the determinants of prognosis include more than tumorspecific factors, and physical factors, such as body mass index and performance status, are also influential. 2 Defined as the physical component of syndromes characterized by the significant loss of skeletal muscle mass and function, sarcopenia can result in physical disability, declining quality of life, and death. 3 At present, it has been confirmed as a poor prognostic indicator of various cancers.…”

Section: Introductionmentioning

confidence: 99%

“…Also, the single cross-sectional area of muscle on abdominal CT or MRI at the third lumbar vertebra (L3) are considered gold standards for estimating body composition, as it is linearly correlated to whole body skeletal muscle mass, and currently used to define sarcopenia in trials. 1,2,4 Chest CT is considered as the standard method employed to diagnose and follow-up lung cancer; however, it has rarely extended to the L3 level. 2,5 In recent research with the majority of patients suffering lung cancer, only 65% received useful scans to assess SMM at the L3 level, hindering its application to assess the status of sarcopenia in those patients.…”

Section: Introductionmentioning

confidence: 99%

“…1,2,4 Chest CT is considered as the standard method employed to diagnose and follow-up lung cancer; however, it has rarely extended to the L3 level. 2,5 In recent research with the majority of patients suffering lung cancer, only 65% received useful scans to assess SMM at the L3 level, hindering its application to assess the status of sarcopenia in those patients. 6 Radiomics, defined as an algorithm-based large scale quantitative analysis of imaging features, can reveal disease features and reflect underlying pathophysiology.…”

Section: Introductionmentioning

confidence: 99%

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Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Dong

et al. 2020

Thoracic Cancer

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Background Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ability of chest CT radiomics combined with machine learning classifiers to identify sarcopenia in advanced non‐small cell lung cancer (NSCLC) patients. Methods This study retrospectively analyzed CT images of 99 patients with NSCLC. Skeletal muscle radiomics were extracted from a single axial slice of the chest CT scan at the 12th thoracic vertebrae level. In total, 854 radiomic and clinical features were obtained from each patient. Feature selection was conducted with FeatureSelector module, optimal key features were fed into the lightGBM classifier for model construction, and Bayesian optimization was adopted to tune hyperparameters. The model's performance was evaluated by specificity, sensitivity, accuracy, precision, F1‐score, Matthew's correlation coefficient (MCC), Cohen's kappa coefficient (Kappa), and AUC. Results A total of 40 patients were found to have sarcopenia. Five optimal features were selected. In the base lightGBM model, the specificity, sensitivity, accuracy, precision, F1‐score, AUC, MCC, Kappa of validation set were 0.889, 0.750, 0.833, 0.818, 0.783, 0.819, 0.649, 0.648, respectively. After Bayesian hyperparameter tuning, the optimized lightGBM model achieved better prediction performance, and the corresponding values were 0.944, 0.833, 0.900, 0.909, 0.870, 0.889, 0.791, 0.789, respectively. Conclusions Chest CT‐based radiomics has the potential to identify sarcopenia in NSCLC patients with the lightGBM classifier, and the optimal lightGBM model via Bayesian hyperparameter tuning demonstrated better performance. Key points Significant findings of the study Our study demonstrates that chest CT‐based radiomics combined with lightGBM classifier has the ability to identify sarcopenia in NSCLC patients. What this study adds Skeletal muscle radiomics would be a potential biomarker for sarcopenia identity in NSCLC patients.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Dong

et al. 2020

Thoracic Cancer

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Cancer Cachexia

Eid¹,

Njeim²,

Khuri³

et al. 2022

Holland‐Frei Cancer Medicine

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Overview Cachexia is a systemic wasting syndrome of progressive muscle and, usually, adipose loss, multi‐organ dysfunction, and wide metabolic derangement, often exacerbated by anorexia and diminished nutritional intake. More than half of all cancer patients develop cachexia, which directly causes profound morbidity and substantially reduced survival. New large‐scale high‐dimensional clinical and experimental studies are revealing unexpected ground truths that cachexia frequently occurs in early stages of many cancers; has several distinct clinical phenotypes; increases complications of and reduces response to cancer‐directed therapies. More precise tools for early assessment, diagnosis, and multi‐modal interventions, including quantitative image‐based body composition analysis, molecular biomarkers, and new targeted therapeutics are in advanced development. These advances, with rapidly expanding basic research into causal molecular mechanisms, are transforming our fundamental understanding of this complex disease as being integral with cancer biology, treatment response, and patient outcomes.

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Preoperative paraspinous muscle sarcopenia and physical performance as prognostic indicators in non‐small‐cell lung cancer

Tanaka

Ozeki

Mizuno

et al. 2021

J cachexia sarcopenia muscle

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Background Despite the associations of both preoperative sarcopenia and physical performance with post‐operative mortality in non‐small‐cell lung cancer (NSCLC), there have been no comprehensive studies of the impact of physical status on prognosis. This study was performed to investigate the prognostic significance of preoperative sarcopenia and physical performance in NSCLC. Methods This retrospective cohort study was performed in NSCLS patients undergoing curative lung resection at a university hospital between January 2014 and December 2017. The patients were divided into four groups according to the skeletal muscle index [sarcopenia (lowest sex‐specific tertile) and non‐sarcopenia] and 6 min walking distance (6MWD) [short distance (<400 m) and long distance (≥400 m)]. Sarcopenia was assessed by preoperative cross‐sectional areas of right and left paraspinous muscles at the level of the 12th thoracic vertebra from computed tomography images, and physical performance was determined by preoperative 6MWD. The primary and secondary endpoints were post‐operative overall survival (OS) and disease‐free survival (DFS). Results The 587 patients [mean age: 68.5 ± 8.8 years, 399 men (68%)] included in the study were divided into the non‐sarcopenia/long‐distance group (58%), sarcopenia/long‐distance group (26%), non‐sarcopenia/short‐distance group (9%), and sarcopenia/short‐distance group (7%). A total of 109 (18.6%) deaths and 209 (35.6%) combined endpoints were observed over a mean follow‐up of 3.1 ± 1.3 years. After adjusting for other covariates, the sarcopenia/short‐distance group showed significant associations with shorter OS (hazard ratio, 3.38; 95% confidence interval, 1.79–6.37; P < 0.001) and DFS (hazard ratio, 2.11; 95% confidence, 1.27–3.51; P = 0.004) compared with the non‐sarcopenia/long‐distance group on multivariate analyses. Although not significant, adding skeletal muscle index and 6MWD to the pre‐existing risk model increased the area under the curve on time‐dependent receiver operating characteristic curve analysis for OS and DFS, except within 2 years of surgery. Conclusions The presence of both preoperative paraspinous muscle sarcopenia and short distance in 6MWD had an adverse effect on post‐operative prognosis in patients with NSCLC, suggesting that preoperative assessment of thoracic sarcopenia and physical performance may be useful for risk stratification of surgical candidates with potential for targeted interventions.

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Prognostic Value of Sarcopenia in Lung Cancer

Cited by 177 publications

References 36 publications

Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Identifying sarcopenia in advanced non‐small cell lung cancer patients using skeletal muscle CT radiomics and machine learning

Cancer Cachexia

Preoperative paraspinous muscle sarcopenia and physical performance as prognostic indicators in non‐small‐cell lung cancer

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