A Low Body Fat Mass Ratio Predicts Poor Prognosis in Patients with Advanced Non-Small Cell Lung Cancer

Oruç, Zeynep; Akbay, Ahmet; Kaplan, M.A.; Oruç, İdris; Urakçı, Zuhat; Küçüköner, Mehmet; Işıkdoğan, Abdurrahman

doi:10.1080/01635581.2022.2074064

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…Overall, sarcopenia was consistently associated with both poor OS and PFS across most subgroups of patients except for patients with BMI > 25kg/m 2 . The reason may be that, for cancer patients, body weight and body fat are also important indicators to reflect the nutritional status of patients and significantly affect the treatment outcome of patients ( 33 ). Therefore, for obese (BMI > 25kg/m 2 ) cancer patients, a comprehensive body composition analysis may be a better prognostic indicator more than a single myopenia.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors

Lyu

Yang²,

Xiao³

et al. 2023

Front. Nutr.

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ObjectivesIt remains controversial whether sarcopenia has any significant impact on the efficacy of epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) or immune checkpoint inhibitors (ICIs) in patients with advanced non-small cell lung cancer (NSCLC). Therefore, in this study, we aimed to assess the association between sarcopenia and clinical outcomes in patients with advanced NSCLC receiving EGFR-TKIs or ICIs as a first-line therapy.MethodsWe retrospectively enrolled 131 patients with advanced NSCLC treated with first-line EGFR-TKIs or ICIs between 1 March 2019 and 31 March 2021. To estimate sarcopenia, we calculated skeletal muscle index (SMI) as the ratio of skeletal muscle area (cm2) to height squared (m2). Associations between sarcopenia and overall survival (OS) and progression-free survival (PFS) were evaluated using the Kaplan–Meier method and log-rank tests, respectively. A Cox proportional hazards regression model was used to assess the factors associated with OS and PFS. The Student’s t-test or Mann–Whitney U test was used to compare the SMI between patients with or without objective response and disease control. The chi-squared test was used to compare adverse events (AEs) between patients with and without sarcopenia.ResultsAmong the 131 patients, 35 (26.7%) were diagnosed with sarcopenia. Sarcopenia was an independent predictor of poor OS and PFS (p < 0.05) overall and in the EGFR-TKI- and ICI-treated cohorts. Among all patients, those with sarcopenia showed significantly shorter OS and PFS than those without sarcopenia (median OS and PFS: 13.0 vs. 26.0 months and 6.4 vs. 15.1 months; both p < 0.001). These associations were consistent across the subtypes of most clinical characteristics. Statistically significant differences between the objective response (OR) and non-OR groups were also observed in the mean SMI (OR group, 43.89 ± 7.55 vs. non-OR group, 38.84 ± 7.11 cm2/m2; p < 0.001). In addition, we observed similar results with disease control (DC) and non-DC groups (DC group, 42.46 ± 7.64 vs. non-DCR group, 33.74 ± 4.31 cm2/m2; p < 0.001). The AEs did not differ significantly between the sarcopenia and non-sarcopenia groups.ConclusionSarcopenia before treatment might be a significant predictor of poor clinical outcomes (shorter OS and PFS, fewer ORs, less DC) in patients with advanced NSCLC treated with EGFR-TKIs or ICIs as the first-line therapy.

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Section: Discussionmentioning

confidence: 99%

Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors

Lyu

Yang²,

Xiao³

et al. 2023

Front. Nutr.

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“…Central obesity (high VFI) was associated not only with shorter survival times, but also with accelerated tumor growth, sharply contrasting the obesity paradox ( 54 ). In similar image-based studies, a higher SAT volume was associated with better progression free survival (PFS) and BFM ratio >22% was predictive of longer OS ( 55 , 58 ). Sakai et al reported increased length of hospital stay with increased skeletal muscle fat fraction and sarcopenia ( 59 ).…”

Section: Obesity and Survival Outcomes Of Lung Cancermentioning

confidence: 90%

“…In the above studies, a high BMI serves as a positive prognostic factor for OS only in some groups of patients. However, other studies have reported no effect of BMI on lung cancer survival after performing multi-variable analysis to eliminate confounders ( 53 - 56 , 58 ) ( Table 3 ). Due to the conflicting nature of the results obtained when BMI is used as the measure for obesity, authors have proposed a shift of focus to WC, WHR and imaging studies to better characterize obesity and body composition.…”

Section: Obesity and Survival Outcomes Of Lung Cancermentioning

confidence: 98%

Obesity and lung cancer—a narrative review

Vedire¹,

Kalvapudi²,

Yendamuri³

2023

J Thorac Dis

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Background and Objective A highly nuanced relationship exists between obesity and lung cancer. The association between obesity and lung cancer risk/prognosis varies depending on age, gender, race, and the metric used to quantify adiposity. Increased body mass index (BMI) is counterintuitively associated with decreased lung cancer incidence and mortality, giving rise to the term ‘obesity paradox’. Potential explanations for this paradox are BMI being a poor measure of obesity, confounding by smoking and reverse causation. A literature search of this topic yields conflicting conclusions from various authors. We aim to clarify the relationship between various measures of obesity, lung cancer risk, and lung cancer prognosis. Methods The PubMed database was searched on 10 August 2022 to identify published research studies. Literature published in English between 2018 and 2022 were included. Sixty-nine publications were considered relevant, and their full text studied to collate information for this review. Key Content and Findings Lower lung cancer incidence and better prognosis was associated with increased BMI even after accounting for smoking and pre-clinical weight loss. Individuals with high BMI also responded better to treatment modalities such as immunotherapy compared to individuals with a normal BMI. However, these associations varied highly depending on age, gender, and race. Inability of BMI to measure body habitus is the main driver behind this variability. The use of anthropometric indicators and image-based techniques to quantify central obesity easily and accurately is on the rise. Increase in central adiposity is associated with increased incidence and poorer prognosis of lung cancer, contrasting BMI. Conclusions The obesity paradox may arise due to the improper use of BMI as a measure of body composition. Measures of central obesity better portray the deleterious effects of obesity and are more appropriate to be discussed when talking about lung cancer. The use of obesity metrics based on anthropometric measurements and imaging modalities has been shown to be feasible and practical. However, a lack of standardization makes it difficult to interpret the results of studies using these metrics. Further research must be done to understand the association between these obesity metrics and lung cancer.

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“…Based on the L3 vertebral body level, there were at least three phenotypes of physical compositions, containing visceral fat area (VFA), subcutaneous fat area (SFA), together with skeletal muscle index (SMI) are associated with the prognosis of NSCLC patients. [164][165][166] Based on the above research, some scholars 167…”

Section: Survival and Prognosismentioning

confidence: 99%

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

Yuan,

An,

Zhu

et al. 2024

CMAR

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As a disease with high morbidity and high mortality, lung cancer has seriously harmed people’s health. Therefore, early diagnosis and treatment are more important. PET/CT is usually used to obtain the early diagnosis, staging, and curative effect evaluation of tumors, especially lung cancer, due to the heterogeneity of tumors and the differences in artificial image interpretation and other reasons, it also fails to entirely reflect the real situation of tumors. Artificial intelligence (AI) has been applied to all aspects of life. Machine learning (ML) is one of the important ways to realize AI. With the help of the ML method used by PET/CT imaging technology, there are many studies in the diagnosis and treatment of lung cancer. This article summarizes the application progress of ML based on PET/CT in lung cancer, in order to better serve the clinical. In this study, we searched PubMed using machine learning, lung cancer, and PET/CT as keywords to find relevant articles in the past 5 years or more. We found that PET/CT-based ML approaches have achieved significant results in the detection, delineation, classification of pathology, molecular subtyping, staging, and response assessment with survival and prognosis of lung cancer, which can provide clinicians a powerful tool to support and assist in critical daily clinical decisions. However, ML has some shortcomings such as slightly poor repeatability and reliability.

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A Low Body Fat Mass Ratio Predicts Poor Prognosis in Patients with Advanced Non-Small Cell Lung Cancer

Cited by 6 publications

References 31 publications

Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors

Prognostic value of sarcopenia in patients with lung cancer treated with epidermal growth factor receptor tyrosine kinase inhibitors or immune checkpoint inhibitors

Obesity and lung cancer—a narrative review

Machine Learning in Diagnosis and Prognosis of Lung Cancer by PET-CT

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