Figure
Mean length of hospital stay for patients with and without sarcopenia for the two cancer cohorts. Length of hospital stay is shown as (a) that related to surgery and (b) the total for all patients. Averages are displayed as mean ± SEM
Source publication
OBJECTIVES: To assess body composition in patients with non-small cell lung cancer (NSCLC) and colorectal cancer using whole-body MRI and relate this to clinical outcomes. METHODS: 53 patients with NSCLC (28 males, 25 females; mean age 66.9) and 74 patients with colorectal cancer (42 males, 32 females; mean age 62.9) underwent staging whole-body MR...
Contexts in source publication
Context 1
... length of hospital stay for patients with and without sarcopenia for the two cancer cohorts is summarised in Table 3. In patients with colorectal cancer, there was no difference in the mean length of hospital stay in patients with and without sarcopenia. ...Context 2
... relationship between length of hospital stay and the body composition parameters for the two cohorts is summarised in Tables 4 and 5 (a summary of these results separately for each sex has been provided in the Supplementary Tables A3 and A4). In patients with colorectal cancer who underwent surgery, there was a positive relationship between FFM and SM indices and the length of hospital stay. ...Citations
... 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). Another added advantage of using image-based studies to define body composition is the ability to identify obese cancer patients with sarcopenia. ...
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.
Aims:
Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health. The purpose of the study was to systematically explore literature involving AI techniques for body composition assessment and observe general trends.
Methods:
We searched the following databases: Embase, Web of Science, and PubMed. There was a total of 354 search results. After removing duplicates, irrelevant studies, and reviews(a total of 303), 51 studies were included in the systematic review.
Results:
AI techniques have been studied for body composition analysis in the context of diabetes mellitus, hypertension, cancer and many specialized diseases. Imaging techniques employed for AI methods include CT (Computerized Tomography), MRI (Magnetic Resonance Imaging), ultrasonography, plethysmography, and EKG(Electrocardiogram). Automatic segmentation of body composition by deep learning with convolutional networks has helped determine and quantify muscle mass. Limitations include heterogeneity of study populations, inherent bias in sampling, and lack of generalizability. Different bias mitigation strategies should be evaluated to address these problems and improve the applicability of AI to body composition analysis.
Conclusions:
AI assisted measurement of body composition might assist in improved cardiovascular risk stratification when applied in the appropriate clinical context.
