Sarcopenia is an independent unfavorable prognostic factor of non-small cell lung cancer after surgical resection: A comprehensive systematic review and meta-analysis

Deng, Han‐Yu; Hou, Lingmi; Zha, Panpan; Huang, Kaili; Lei, Peng

doi:10.1016/j.ejso.2018.09.026

Cited by 69 publications

(73 citation statements)

References 25 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%

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%

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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“…The association between sarcopenia and mortality has been explored in many different populations, ranging from community-dwelling older adults [23] to nursing home residents [24]. Recently, a number of studies have found that sarcopenia can increase the risk of mortality among patients with certain types of cancer, such as lung cancer [25], gastric cancer [26], and colorectal cancer [27], indicating that sarcopenia can be a predictive factor in cancer patients. This study has an important implication for medical personnel.…”

Section: Discussionmentioning

confidence: 99%

Sarcopenia as a predictor of mortality in women with breast cancer: a meta-analysis and systematic review

et al. 2020

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Background: Breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death in women worldwide. Recently, studies have been published with inconsistent findings regarding whether sarcopenia is a risk factor for mortality in breast cancer patients. Therefore, the aim of this systematic review and meta-analysis was to systematically assess and quantify sarcopenia as a risk factor for mortality in breast cancer patients. Methods: In a systematic literature review of PubMed, EMBASE, and the Cochrane CENTRAL Library, we searched for observational studies written in English (from database inception until April 30, 2019) that reported an association between sarcopenia and breast cancer in women who were 18 years or older. Results: A total of six studies (5497 participants) were included in this meta-analysis. Breast cancer patients with sarcopenia were associated with a significantly higher risk of mortality, compared to breast cancer patients without sarcopenia (pooled HR-hazard ratio = 1.71, 95% CI: 1.25-2.33, I 2 = 59.1%). In addition, the results of age subgroup analysis showed that participants younger than 55 years with sarcopenia had a lower risk of mortality than participants aged 55 years and older with sarcopenia (pooled HR = 1.46, 95% CI: 1.24-1.72 versus pooled HR = 1.99, 95% CI: 1.05-3.78), whereas both have an increased risk of mortality compared to non-sarcopenic patients. Subgroup analyses regarding stage at diagnosis revealed an increased risk of mortality in non-metastatic patients compared to participants without sarcopenia (pooled HR = 1.91, 95% CI: 1.32-2.78), whereas the association was not significant in metastatic breast cancer patients. Other subgroup analyses were performed using different follow-up periods (> 5 years versus ≤5 years) and the results were different (pooled HR = 1.81, 95% CI: 1.23-2.65 versus pooled HR = 1.70, 95% CI: 0.80-3.62). Conclusions: The present study found that sarcopenia is a risk factor for mortality among female early breast cancer patients. It is imperative that more research into specific interventions aimed at treating sarcopenia be conducted in the near future in order to provide evidence which could lead to decreased mortality rates in breast cancer patients.

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“…Computed tomography-derived low muscle mass, as a marker of impaired performance, is a strong prognostic factor for mortality in patients with non-small cell lung cancer (NSCLC). [5][6][7] However, ideally, the evaluation of performance status should be cheap, fast, and easy to implement in routine practice. Handgrip strength is a simple and quick measure of muscle function.…”

Section: Introductionmentioning

confidence: 99%

Handgrip weakness, low fat‐free mass, and overall survival in non‐small cell lung cancer treated with curative‐intent radiotherapy

Burtin

Bezuidenhout

Sanders

et al. 2020

J cachexia sarcopenia muscle

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Background Assessment of handgrip strength and fat-free mass provides quick and objective information on muscle performance and mass that might complement subjective World Health Organization Performance Status (WHO PS). We investigated to what extent the presence of pre-treatment handgrip weakness and low fat-free mass index (FFMI) provides additional prognostic information on top of well-established prognostic factors (including WHO PS) in non-small cell lung cancer (NSCLC) patients selected for curative-intent (chemo)radiation. Methods Prospectively, patients with early and locally advanced NSCLC (stages I-III) treated with (chemo)radiation were enrolled. Handgrip weakness and low FFMI, derived from bioelectrical impedance analysis, were defined using normative values and were correlated with overall survival (OS). Results We included 936 patients (age 68 ± 10 years; 64% male; 19% stage I, 9% stage II, and 72% stage III disease; 26% handgrip weakness; 27% low FFMI). In patients with good performance status (WHO PS 0 or 1), handgrip weakness and low FFMI were significant prognostic factors for OS, after adjustment for age, gender, disease stage, and co-morbidities. The combined presence of handgrip weakness and low FFMI was a strong prognostic factor for OS when compared with patients with normal handgrip strength and FFMI (hazard ratio: 1.79, 95% confidence interval: 1.34-2.40, P < 0.0001). In patients with impaired performance status (WHO PS ≥ 2, 19% of sample), handgrip weakness and low FFMI were not related to OS. Conclusions In early and locally advanced NSCLC patients treated with curative-intent (chemo)radiation who have good WHO PS, patients with combined handgrip weakness and low FFMI have the worst prognosis.

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Sarcopenia is an independent unfavorable prognostic factor of non-small cell lung cancer after surgical resection: A comprehensive systematic review and meta-analysis

Cited by 69 publications

References 25 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

Sarcopenia as a predictor of mortality in women with breast cancer: a meta-analysis and systematic review

Handgrip weakness, low fat‐free mass, and overall survival in non‐small cell lung cancer treated with curative‐intent radiotherapy

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