Non-small cell lung cancer (NSCLC) is among the most prevalent and fatal malignancies worldwide. Although the tumour-node-metastasis (TNM) staging system remains the primary tool for assessing prognosis, it does not fully explain the variability in survival outcomes among patients with similar cancer stages. This has led to increased interest in additional prognostic factors that may offer a more comprehensive understanding of patient risk profiles.
One such factor is body composition, particularly skeletal muscle mass and fat distribution, which can significantly influence disease progression and treatment response. Recent research has demonstrated that multiparameter body composition analysis using preoperative computed tomography (CT) scans can provide valuable prognostic information. By evaluating skeletal muscle index (SMI), subcutaneous adipose index (SAI) and intermuscular adipose index (IMAI), clinicians can better predict overall survival (OS) and disease-free survival (DFS) in patients with resectable NSCLC. A recent article published in Insights into Imaging explores the impact of these parameters on clinical outcomes and their potential integration into routine patient assessments.
The Prognostic Value of Skeletal Muscle Mass
Skeletal muscle plays a fundamental role in maintaining metabolic balance, immune function and overall physiological resilience. Muscle mass depletion has been associated with poorer prognosis across multiple cancers, including NSCLC. Low SMI, which reflects the amount of muscle relative to body size, is particularly relevant as it is linked to an increased risk of adverse events following surgery, such as post-operative complications and reduced survival rates.
CT imaging allows for precise quantification of skeletal muscle at the first lumbar vertebra (L1) level, which serves as a reliable alternative to the traditional third lumbar vertebra (L3) measurement. Studies have shown that NSCLC patients with higher SMI exhibit better survival outcomes due to improved functional reserves, which help withstand the stress of surgery and cancer treatments. Additionally, reduced muscle mass can weaken immune responses, impairing the body’s ability to combat cancer progression.
While skeletal muscle depletion is well-recognised as a negative prognostic factor, muscle quality also plays an important role. Increased fat infiltration within muscle tissue, known as myosteatosis, has been linked to worse outcomes. Intermuscular adipose tissue (IMAT), which accumulates between muscle fibres, contributes to metabolic dysfunction and systemic inflammation. The IMAI, which adjusts IMAT for body size, provides a more accurate assessment of muscle fat infiltration than absolute IMAT measurements. Higher IMAI values have been correlated with lower survival rates, suggesting that muscle fat infiltration is a key determinant of prognosis in NSCLC.
The Impact of Fat Distribution on Survival Outcomes
The relationship between adipose tissue and cancer prognosis is complex, with studies highlighting both protective and detrimental effects depending on the type and location of fat. The concept of the "obesity paradox"—where a higher body mass index (BMI) appears to confer a survival advantage in cancer patients—has been widely debated. However, BMI alone does not distinguish between visceral and subcutaneous fat, which have distinct biological effects on cancer progression.
Subcutaneous adipose tissue (SAT), which is measured through the subcutaneous adipose index (SAI), appears to have a protective effect. Higher SAI values have been associated with improved survival in NSCLC, possibly due to the role of SAT in energy storage and its potential tumour-suppressive effects. Conversely, visceral adipose tissue (VAT), which surrounds internal organs, has been linked to worse clinical outcomes in other cancers. However, in NSCLC, VAT does not show a strong negative impact on survival, indicating that different adipose tissue compartments may exert varied effects depending on cancer type.
IMAI, which reflects intramuscular fat deposition, has emerged as a particularly significant factor in NSCLC prognosis. Unlike SAI, which is associated with improved survival, higher IMAI values have been linked to poorer outcomes. This suggests that fat infiltration into muscle tissues is a more relevant predictor of mortality than overall body fat levels. The presence of excess intramuscular fat may contribute to systemic inflammation and metabolic disturbances, exacerbating disease progression and reducing treatment efficacy.
Integrating Body Composition Metrics into Clinical Decision-Making
Incorporating body composition metrics into standard prognostic models can significantly enhance the accuracy of survival predictions. Traditional clinicopathological factors such as TNM staging and patient demographics remain essential, but adding SMI, SAI and IMAI measurements improves predictive power. Research has demonstrated that integrating these body composition parameters into clinical models leads to a marked increase in the accuracy of one-year and three-year survival forecasts.
Identifying patients with unfavourable body composition profiles allows clinicians to implement early interventions aimed at improving survival outcomes. Nutritional support and exercise regimens targeting muscle preservation and fat redistribution may offer a viable strategy for enhancing treatment response and reducing post-operative complications. Given the strong prognostic value of body composition, these metrics should be considered in routine preoperative assessments for NSCLC patients undergoing curative surgery.
The use of multiparameter body composition analysis in NSCLC prognosis represents a promising advancement in oncological imaging. By leveraging CT-derived metrics such as SMI, SAI and IMAI, clinicians can refine risk stratification and tailor interventions to individual patient needs. Muscle mass and quality have emerged as crucial determinants of overall survival, while subcutaneous and intramuscular fat distribution further influences prognosis.
These findings highlight the potential benefits of integrating body composition analysis into standard oncological practice. Identifying at-risk patients early and implementing targeted interventions could improve treatment outcomes and enhance overall patient care. Future research should focus on refining predictive models and developing clinical guidelines that incorporate body composition metrics into routine NSCLC management. With imaging technology progressing, CT-based body composition analysis may become a standard tool in precision oncology, offering a more nuanced approach to cancer prognosis and treatment planning.
Source: Insights into Imaging
Image Credit: iStock
