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Clinical Applications of CT Body Composition: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Tarig Elhakim.

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. 

  • artificial intelligence
  • machine learning
  • CT body composition

1. Introduction

Body mass index (BMI) has long been a key clinical metric that is used in predictive models to estimate the risk of developing chronic diseases and future mortality [1]. Unfortunately, BMI has several shortcomings and does not account for the distribution of fat in the body and does not distinguish between excess fat, proportion of bone mass or muscle [2]. Because BMI only measures excess weight, this measure cannot reflect the loss of muscle mass as in sarcopenia and sarcopenic obesity. Body composition metrics incorporate the proportion of body fat and skeletal muscles. Many non-invasive measures exist that analyze body composition starting from the traditional skin-fold tests to other advanced measures such as bioelectrical impedance analysis, dual-energy X-ray absorptiometry (DXA), hydrostatic (underwater) densitometry and air displacement plethysmography, among others [3]. Despite some studies suggesting their accuracy compared to BMI, many of these latter metrics are considered inappropriate for widespread clinical implementation because they are often complex to implement, expensive, and difficult to standardize [2]. As such, the CDC currently only recommend BMI as an indication of body composition and health risks, especially as other measures have no available reference standards or validated risk categories [2].

2. Clinical Applications of CT Body Composition

2.1. Cancer

Sarcopenia is associated with increased morbidity and mortality in multiple types of cancer [78][4] including pancreatic cancer [79][5], esophageal cancer [80][6], lung cancer [81][7], colorectal liver metastasis [82][8] and melanoma [83][9], among others. In a recent systematic review of CT body composition in abdominal malignancy, seven studies showed that low muscle mass was associated with a worse clinical outcome [46][10]. Sarcopenia was linked to adverse therapeutic and clinical outcomes including higher postoperative infections, systematic inflammation, chemotherapy toxicity and mortality in patients with abdominal malignancy [46][10]. In another multi-center retrospective study of preoperative CT body composition analysis in lung cancer patients undergoing lobectomy, skeletal muscle mass was an independent predictor of postoperative complications and increased hospital length of stay (LOS) [84][11]. Interestingly, low thoracic muscle mass was more effective than biological age in predicting postoperative events [85][12]. In the same population, sarcopenic obesity was an independent predictor of hospital LOS and postoperative complications. This highlights the role of CT body composition in identifying cancer patients who carry a high risk of worse clinical outcomes prior to surgery. Similar results have been reported in patients with hepatocellular carcinoma (HCC) [58][13]. CT body composition has been found to be predictive of patient outcomes in those receiving chemotherapy, radiotherapy, radio-frequency ablation, embolization, hepatectomy and liver transplant [44][14]. In a recent study evaluating the prognostic factors associated with overall survival in elderly patients with HCC receiving trans-arterial chemoembolization (TACE), the detection of muscle depletion and visceral adiposity was found to be independently associated with poor survival outcomes [86][15]. The same study found no relationship between BMI and survival [87][16]. Interestingly, the response to the first TACE session was better in those with low muscle mass and high visceral fat compared to those with normal body composition [87][16]. However, the former group had lower overall survival. As such, assessment of body composition may be an important clinical consideration for HCC patients undergoing TACE. Similarly, Faron et al. evaluated the role of sarcopenia to predict overall survival in those receiving yttrium-90 (Y90) trans-arterial radioembolization (TARE) [88][17]. Sarcopenia was found to be an independent prognostic marker of overall survival and can provide prognostic value in patients receiving Y90 TARE [89][18]. Another study assessed sarcopenia before and after treatment with TARE and found it to be predictive of post-TARE progressive HCC disease [90][19]. Similarly, HCC patients with sarcopenia undergoing radiofrequency ablation therapy were found to have a lower survival rate compared to nonsarcopenic patients [91][20]. In HCC patients undergoing hepatectomy, sarcopenia was associated with high rates of post-surgical complications [92,93][21][22]. One study showed that the 5-year survival rate was lower in those with sarcopenia compared to non-sarcopenic patients (58.2% vs. 82.4%, p = 0.0002) [94][23]. Additionally, having sarcopenia was associated with a worse tumor stage and microvascular invasion [95][24]. Another study showed that patients with sarcopenia have higher rates of morbidity and mortality after hepatectomy [85][12], similar to those who have diminished functional reserves [87][16]. When considering hepatectomy, it is important to assess the future liver remnant (FLR), the volume of liver to be left behind after resection [89][18]. Those with small FLR have a higher risk of post-hepatectomy liver failure [96][25]. Many of these patients undergo portal vein embolization (PVE) prior to hepatectomy so as to divert portal venous blood and trophic factors to the non-embolized section of the liver leading to liver hypertrophy of the non-resected liver segments. Those with insufficient hypertrophy are at increased risk of post-hepatectomy liver failure [97,98][26][27]. A recent study evaluated the role of CT body composition in predicting liver remnant hypertrophy following PVE in patients with colorectal liver metastasis. The study found that patients with sarcopenia had impaired liver hypertrophy after PVE [99][28]. Another study also found that the quantity and quality of skeletal muscle were associated with the degree of liver hypertrophy after PVE [95][24]. Low muscle mass on CT body composition was found to be an independent predictor of poor liver hypertrophy after PVE and increased the risk of post-hepatectomy liver failure [100][29]. These studies suggest that the assessment of CT body composition prior to PVE may be important for identifying patients at risk of post-hepatectomy complications. In addition to the prognostic association of sarcopenia with poor performance status, cancer progression and overall survival, it has also been linked to chemotherapy toxicity and response to therapy [101,102][30][31]. A recent retrospective study found decreased survival rates in sarcopenic patients receiving sorafenib chemotherapy for HCC compared to nonsarcopenic patients [103][32]. Additionally, sarcopenic patients were found to have a lower response to chemotherapy and lower disease control compared to nonsarcopenic patients [104][33]. Another study found sarcopenia to be associated with early dose chemotherapy toxicity [105][34]. These results raise the question of possible future adjustments of chemotherapy dose based on the amount of skeletal muscles that a patient has, to avoid extensive toxicity [106][35].

2.2. Liver Disease

Studies have shown an association between CT body composition and severity of liver disease [100,107,108][29][36][37]. Liver cirrhosis is strongly associated with sarcopenia [109][38]. The distribution of body fat is a major predictor of complications and outcomes in patients with cirrhosis, both before and after liver transplantation [110][39]. Therapy for liver disease is also associated with alterations in body composition. For instance, transjugular intrahepatic portosystemic stent (TIPS), a standard therapy in many patients with portal hypertension, is associated with improved fat-free mass and fluid-free body weight [104,111,112][33][40][41]. Artu et al. utilized CT scans to measure body composition in patients post-TIPS placement and found an improvement in sarcopenia and decreased visceral-to-subcutaneous fat ratio following intervention [113][42]. Additionally, Pang et al. were able to demonstrate that pre-TIPS blood ammonia had a positive association with post-TIPS BMI [114][43]. These studies demonstrate the importance of CT body composition analysis before and after treatments in liver disease. In addition to its association with response to therapies, CT body composition can also be used to predict the etiology of liver disease. Zou et al. developed a deep learning algorithm using Google’s DeepLabv3+ in which body composition was automatically extracted [115][44]. Their study showed that patients with NAFLD cirrhosis had decreased muscle mass and a significant increase in visceral and subcutaneous fat compared to those with non-NAFLD cirrhosis. The study also showed higher levels of accuracy of CT body composition compared to that of BMI in distinguishing the two patient populations. These findings highlight the potential role of CT body composition in risk prediction and stratification in liver disease.

2.3. Inflammatory Bowel Disease (IBD)

Analysis of abdominal CT body composition can also aid in disease prognostication in patients with Crohn’s Disease and ulcerative colitis (IBD). IBD is a gastrointestinal inflammatory disorder associated with malabsorption resulting in low skeletal muscle mass, decreased bone mineral density and therefore a dynamic change in body composition especially in patients with Crohn’s disease [116][45]. Abdominal CT-based opportunistic screening has been utilized in several studies for prognostication in IBD. Changes in CT body composition metrics in patients with IBD are correlated with disease duration and severity [117][46]. The pathogenesis of Crohn’s disease is associated with increased visceral adiposity as identified through CT body composition. In patients with increased visceral adiposity, studies have reported a more complicated disease course [118][47], higher postoperative complication rates [119][48] and higher rates of disease recurrence [120][49], Grillot J et al. also reported worse Crohn’s disease outcomes with sarcopenia and visceral adiposity [114][43]. Another similar study found that muscle volume is strongly associated with hospital length of stay and that both, muscle volume and visceral adiposity, are strongly associated with intestinal resection rates [121][50]. These results highlight that early screening and detection of body composition changes in patients with Crohn’s disease may help in risk stratification and may inform early nutritional and pharmacological interventions, potentially improving patients’ outcomes and quality of life [122][51].

2.4. Kidney Disease

Paradoxically, higher BMIs are associated with better survival in patients with chronic kidney disease (CKD) [123,124,125][52][53][54]. However, due to the limitations of BMI, it is not fully known whether the increase in survival is associated with levels of adipose tissue or lean mass. Patients with CKD tend to have fluid retention that cannot be differentiated with BMI. Lin et al. showed, through using a body composition monitor–multifrequency bioimpedance spectroscopy device, that a high lean tissue index, not high BMI or high fat tissue index, predicted a lower risk of adverse outcomes in CKD patients [126][55]. These findings illustrate the importance of body composition analysis and its association with outcomes in patients with kidney disease. Fully automated CT-based body composition analysis shows great promise as it can detect total muscle mass and quantify muscle wasting which is frequently seen in this patient population [127][56]. It has been already shown that body composition analysis can accurately predict urinary creatinine excretion, creatinine clearance, and glomerular filtration rate (GFR) [21][57]. A recent study showed that machine-learning CT body composition analysis can estimate creatinine excretion with a high degree of accuracy [75][58]. These fully automated body composition analyses can validate Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation results and replace burdensome 24-h urine collection with spot urine collection, paving the way for integrated diagnostics that use multidisciplinary data for better patient care [128,129][59][60]. Furthermore, there have been recent efforts to fully automate kidney segmentation by measuring kidney, cortex and medulla volumes, which will provide a wide range of clinical applications such as evaluating renal donor suitability and prognosticating outcomes [130][61]. Other studies have found a correlation between high visceral adipose tissue and poor outcomes in patients with kidney disease [131,132][62][63]. Sarcopenia was also found to have a strong association with increased mortality and morbidity in patients with this condition [133][64]. Other studies have shown that skeletal muscle and visceral adipose tissue derived from CT scans are stronger predictors of renal disease prognosis and can outperform established clinical parameters for risk stratification [134][65]. In summary, utilization of CT body composition to accurately quantify muscle mass and calculate visceral-to-subcutaneous fat ratio has the capability of aiding prognostication in patients with renal disease.

2.5. COVID-19

Several studies have shown the association between CT body composition parameters and the severity of COVID-19 disease. Hocaoglu et al. and Ufuk et al. utilized CT to measure pectoralis muscle volume and density. They found that low pectoralis muscle density correlated with increased COVID-19 severity and worse outcomes [135,136][66][67]. Chandarana et al. showed that CT-derived muscle adipose tissue measurements at the L3 vertebral level were significantly higher in patients with more severe symptoms of COVID-19; consequently, those patients had a higher risk of hospitalization [137][68]. Similarly, Bunnell et al. performed body composition segmentation using an in-house automated algorithm trained specifically at the L4 vertebral level and found that COVID-19 patients with high visceral adipose tissue/subcutaneous adipose tissue ratio and high intermuscular adipose tissue have worse outcomes [138][69]. Another study analyzed paravertebral muscle at the 12th thoracic vertebra in COVID-19 patients and found that muscle loss is a predictor of intensive care admission in COVID-19 patients. Taken together, these findings suggest that CT body composition analysis can help predict adverse clinical events and outcomes in patients with COVID-19.

2.6. Cardiovascular Diseases

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide [139][70]. CT-based opportunistic screening can help detect cardiovascular diseases pre-symptomatically, thus allowing early preventative care to decrease future adverse clinical events and healthcare costs. O’Connor et al. showed that the abdominal aortic calcification score using semiautomated CT quantifications is a better predictor of cardiovascular events than the Framingham risk score (FRS) [73][71]. Other studies have shown that controlling the progression of abdominal aortic calcification was associated with decreased risk of mortality, coronary artery disease, stroke and heart failure [140,141][72][73]. By detecting aortic calcification early using CT-based opportunistic screening, appropriate interventions can be applied to those patients to address their underlying risk and prevent future cardiovascular mortality. Similarly, Pickhardt et al. defined several automated CT-based body composition biomarkers that can predict major cardiovascular events, including quantification of aortic calcification, muscle density, visceral/subcutaneous and liver fat and bone mineral density. These metrics outperformed clinical parameters such as the FRS and BMI for risk prediction [65][74]. Recently, Magudia et al. described a retrospective study of 9752 outpatient routine CT scans of black people and white people with no recent history of cancer or cardiovascular diseases [142][75]. Using a fully automated AI approach, the SMA, VFA and SFA were extracted from the L3 vertebra, then adjusted to age, race and sex, and associated with subsequent myocardial infarction and the risk of stroke within 5 years from the scan. Interestingly, the VFA had a significant association with the risk of developing MI (HR 1.31, p = 0.04) and Stroke (HR 1.46, p = 0.04) while BMI, weight, SFA and SMA had no association. This suggests the importance of incorporating SFA instead of BMI in cardiovascular risk models. By providing a better assessment of a person’s cardiometabolic profile, CT-based body composition analysis shows great promise than established clinical parameters in improving pre-symptomatic detection and risk-stratification of patients vulnerable to adverse cardiovascular events and can augment the current risk prediction models.

2.7. Critical Illness

CT body composition also plays a role in improving care in critically ill patients. Toledo et al. demonstrated that critically ill patients with sarcopenia have a lower 30-day survival, higher hospital mortality, and higher complication rates [22][76]. Weijs et al. reported that sarcopenia on CT, during early stages of a critical illness, is strongly associated with a high risk of mortality in mechanically ventilated critically ill patients [23][77]. Early identification of at-risk patients can help inform any necessary interventions for better outcomes in this critically ill population.

2.8. Contrast Dose Adjustment

Iodinated contrast dosing is currently calculated based on total body weight, regardless of adipose and muscle content. However, patients with various body composition indexes, such sarcopenic obesity and athletes with high muscle content, can suffer from overdosing or underdosing. To alleviate this concern, CT body composition analysis has been shown to allow appropriate contrast dosing for each patient during the process of CT scanning [28][78].

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