Computed Tomography-Based Body Composition Is Not Consistently Associated with Outcome in Older Patients with Colorectal Cancer

Post-diagnosis adiposity and colorectal cancer prognosis: A Global Cancer Update Programme (CUP Global) systematic literature review and meta-analysis

The adiposity influence on colorectal cancer prognosis remains poorly characterised. We performed a systematic review and meta-analysis on post-diagnosis adiposity measures (body mass index [BMI], waist circumference, waist-to-hip ratio, weight) or their changes and colorectal cancer outcomes. PubMed and Embase were searched through 28 February 2022. Random-effects meta-analyses were conducted when at least three studies had sufficient information. The quality of evidence was interpreted and graded by the Global Cancer Update Programme (CUP Global) independent Expert Committee on Cancer Survivorship and Expert Panel. We reviewed 124 observational studies (85 publications). Meta-analyses were possible for BMI and all-cause mortality, colorectal cancer-specific mortality, and cancer recurrence/disease-free survival. Non-linear meta-analysis indicated a reverse J-shaped association between BMI and colorectal cancer outcomes (nadir at BMI 28 kg/m2). The highest risk, relative to the nadir, was observed at both ends of the BMI distribution (18 and 38 kg/m2), namely 60% and 23% higher risk for all-cause mortality; 95% and 26% for colorectal cancer-specific mortality; and 37% and 24% for cancer recurrence/disease-free survival, respectively. The higher risk with low BMI was attenuated in secondary analyses of RCTs (compared to cohort studies), among studies with longer follow-up, and in women suggesting potential methodological limitations and/or altered physiological state. Descriptively synthesised studies on other adiposity-outcome associations of interest were limited in number and methodological quality. All the associations were graded as limited (likelihood of causality: no conclusion) due to potential methodological limitations (reverse causation, confounding, selection bias). Additional well-designed observational studies and interventional trials are needed to provide further clarification.

Chemotoxicity and Associated Risk Factors in Colorectal Cancer: A Systematic Review and Meta-Analysis

BACKGROUND

Colorectal cancer (CRC) patients experience multiple types of chemotoxicity affecting treatment compliance, survival, and quality of life (QOL). Prior research shows clinician-reported chemotoxicity (i.e., grading scales or diagnostic codes) predicts rehospitalization and cancer survival. However, a comprehensive synthesis of clinician-reported chemotoxicity is still lacking.

OBJECTIVES

We conducted a systematic review and meta-analysis to determine chemotoxicity's prevalence and risk factors in CRC.

METHODS

A systematic search from 2009 to 2024 yielded 30 studies for review, with 25 included in the meta-analysis.

RESULTS

Pooled prevalences of overall, non-hematological, and hematological moderate-to-severe toxicities were 45.7%, 39.2%, and 25.3%, respectively. The most common clinician-reported chemotoxicities were gastrointestinal (GI) toxicity (22.9%) and neuropathy or neutropenia (17.9%). Significant risk factors at baseline were malnutritional status, frailty, impaired immune or hepato-renal functions, short telomere lengths, low gut lactobacillus levels, age, female sex, aggressive chemotherapy, and low QOL. Age was associated with neutropenia (β: -1.44) and GI toxicity (β:1.85) (p-values < 0.01). Older adults (>65 y.o.) had higher prevalences of overall (OR: 1.14) and GI (OR: 1.65) toxicities, but a lower prevalence of neutropenia (OR: 0.65) than younger adults (p-values < 0.05).

CONCLUSIONS

Our findings highlight the importance of closely monitoring and managing chemotoxicity in CRC patients receiving chemotherapy.

Low muscle quality on a procedural computed tomography scan assessed with deep learning as a practical useful predictor of mortality in patients with severe aortic valve stenosis

BACKGROUND & AIMS

Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans.

METHODS

This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass.

RESULTS

The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15-1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01-1.52], p = 0.04).

CONCLUSIONS

Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.

Sarcopenia diagnosis in patients with colorectal cancer: a review of computed tomography-based assessments and emerging ways to enhance practicality

Traditionally, cancer treatment has focused on the stages of the disease; however, recent studies have highlighted the importance of considering the overall health status of patients in the prognosis of cancer. Loss of skeletal muscle, known as sarcopenia, has been found to significantly affect outcomes in many different types of cancers, including colorectal cancer. In this review, we discuss the guidelines for diagnosing sarcopenia, with a specific focus on CT-based assessments. Many groups worldwide, including those in Europe and Asia, have introduced their own diagnostic guidelines for sarcopenia. Seemingly similar yet subtle discrepancies, particularly in the cutoff values used, limit the use of these guidelines in the general population, warranting a more universal guideline. Although CT-based measurements, such as skeletal muscle index and radiodensity, have shown promise in predicting outcomes, the lack of standardized values in these measurements hinders their universal adoption. To overcome these limitations, innovative approaches are being developed to assess changes in muscle mass trajectories and introduce new indices, such as skeletal and appendicular muscle gauges. Additionally, machine learning models have shown superior performance in predicting sarcopenic status, providing an alternative to CT-based diagnosis, particularly after surgery. CT has tremendous benefits and a significant role in visually as well as quantitatively retrieving information on patient body composition. In order to compensate for the limitation of standard cutoff value, 3-dimensional analysis of the CT, artificial intelligence-based body composition analysis, as well as machine learning algorithms for data interpretation and analysis have been proposed and are being utilized. In conclusion, despite the varying definitions of sarcopenia, CT-based measurements coupled with machine-learning models are promising for evaluating patients with cancer. Standardization efforts can improve diagnostic accuracy, reduce the reliance on CT examinations, and make sarcopenia assessments more accessible in clinical settings.

The association of body composition on chemotherapy toxicities in non-metastatic colorectal cancer patients: a systematic review

BACKGROUND

In recent years, certain body composition measures, assessed by computed tomography (CT), have been found to be associated with chemotherapy toxicities. This review aims to explore available data on the relationship between skeletal muscle and adiposity, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), intramuscular and intermuscular adipose tissue and their association with chemotherapy toxicity in non-metastatic colorectal cancer (CRC) patients.

METHODS

A systematic literature search following PRISMA guidelines was conducted in Medline, Embase, Cochrane and Web of Science, for papers published between 2011 and 2023. The search strategy combined keywords and MESH terms relevant to 'body composition', 'chemotherapy toxicities', and 'non-metastatic colorectal cancer'.

RESULTS

Out of 3868 studies identified, six retrospective studies fulfilled the inclusion criteria with 1024 eligible patients. Low skeletal muscle mass was strongly associated with increased incidence of both chemotherapy toxicities and dose-limiting toxicity (DLT). The association of VAT, intramuscular and intermuscular adiposity was heterogeneous and inconclusive. There was no association between SAT and chemotherapy intolerance. No universal definitions or cut-offs for sarcopenia and obesity were noted. All studies utilized 2-dimensional (2D) CT slices for CT body composition assessment with varied selection on the vertebral landmark and inconsistent reporting of tissue-defining Hounsfield unit (HU) measurements.

CONCLUSION

Low skeletal muscle is associated with chemotherapy toxicities in non-metastatic CRC. However, quality evidence on the role of adiposity is limited and heterogeneous. More studies are needed to confirm these associations with an emphasis on a more coherent body composition definition and an approach to its assessment, especially regarding sarcopenia.

Different computed tomography parameters for defining myosteatosis in patients with advanced non-small cell lung cancer

BACKGROUND & AIMS

Myosteatosis, excess muscle fat infiltration, is a novel prognostic factor in cancer patients. To define myosteatosis, skeletal muscle radiodensity (SMD) is most commonly used, while intramuscular adipose tissue (IMAT) is newly introduced. We aimed to compare SMD-defined and IMAT-defined myosteatosis for predicting overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC) and to explore whether patients with both low SMD and high IMAT had a shorter OS than patients with low SMD or high IMAT alone.

METHODS

We consecutively and prospectively recruited adult patients with stage IIIB or IV NSCLC at a teaching hospital. The mean SMD of all skeletal muscle areas and the area of IMAT on the unenhanced chest computed tomography (CT) images at the 12th thoracic vertebral level were segmented using Mimics version 21.0. Myosteatosis was defined by either low SMD (SMD-defined myosteatosis) or high IMAT (IMAT-defined myosteatosis). The optimal cutoffs for low SMD and high IMAT were also determined using the maximally selected rank statistics method. We calculated hazard ratios (HRs) and the corresponding confidence intervals (CIs) to evaluate the associations of OS with low SMD, high IMAT, and a combination of them.

RESULTS

We included 565 patients (345 men and 220 women; mean age 58.5 ± 9.0 years). Lower IMAT exhibited a tendency toward a favorable prognosis in men (p = 0.0015) and women (p < 0.0001); whereas higher SMD tended to have a favorable prognosis in men (p = 0.0006) and women (p < 0.0001). At baseline, 423 (74.9 %) participants had high IMAT, 432 (76.5 %) participants had low SMD and 370 (65.5 %) participants had both high IMAT and low SMD. Compared to those without either high IMAT or low SMD, the participants with either high IMAT or low SMD had a shorter OS, while the participants with both High IMAT and Low SMD had the shortest OS (log-rank p < 0.0001). After adjustment for the same confounders, high IMAT (HR, 1.44; 95 % CI, 1.10-1.87) and low SMD (HR, 1.92; 95 % CI, 1.36-2.43) were separately associated with poor prognosis. Moreover, the combination of high IMAT and low SMD indicated a higher risk of poor prognosis (HR, 2.43; 95 % CI, 1.62-3.66).

CONCLUSIONS

Both SMD-defined and IMAT-defined myosteatosis are highly prevalent in patients with advanced NSCLC and may serve as independent prognostic factors for OS. The diagnosis of myosteatosis might consider a combination of low SMD and high IMAT because this would help identify patients at a higher risk of mortality.

The impact of muscle mass and myosteatosis on mortality in critically ill patients with Sars-Cov2-related pneumonia

BACKGROUND & AIMS

Sars-Cov-2 pneumonia can lead to severe complications, requiring invasive mechanical ventilation and admission to the intensive care unit (ICU). Low muscle quantity and quality (the latter evaluated by the amount of ectopic fat infiltration in the muscle [myosteatosis]) at ICU admission are associated with worse outcomes in critically ill patients. The purpose of the present study is to assess muscle mass and myosteatosis of paravertebral skeletal muscle, in critically ill patients with Sars-Cov2 pneumonia and its association with mortality.

METHODS

We conducted a retrospective observational study in 110 critically ill patients with severe Sars-Cov-2 pneumonia that had a high - resolution chest Computerized Tomography (HR-CT) at ICU admission. We acquired CT images at the level of the thoracic 12 (T12) vertebral body and measured skeletal muscle area (SMA), intermuscular adipose tissue (IMAT), and low attenuation muscle area (LAMA). Patients were followed until ICU mortality or discharge.

RESULTS

Patients were 59.8 ± 8.1 years old, 77% were male. Seventy-nine percent of patients were considered at nutritional risk, and 22% were obese. Average Acute Physiology and Chronic Health Evaluation II (APACHE II) score was 17 ± 5.4, and the overall ICU mortality was 48,2% (53/110). At ICU admission, both parameters of myosteatosis were associated with higher mortality (IMAT [per 10% increase] HR: 2.01 (95% Confidence Interval [CI] 1.27 to 3.17), P = 0.003; LAMA HR [per 10% increase]: 1.53 (95% CI 1.10 to 2.13), P = 0.012).

CONCLUSION

Myosteatosis as assessed by CT scans plays a relevant role as a prognostic marker in critically ill patients with Sars-Cov2 severe pneumonia.

Can AI-based body composition assessment outperform body surface area in predicting dose-limiting toxicities for colonic cancer patients on chemotherapy?

PURPOSE

Gold standard chemotherapy dosage is based on body surface area (BSA); however many patients experience dose-limiting toxicities (DLT). We aimed to evaluate the effectiveness of BSA, two-dimensional (2D) and three-dimensional (3D) body composition (BC) measurements derived from Lumbar 3 vertebra (L3) computed tomography (CT) slices, in predicting DLT in colon cancer patients.

METHODS

203 patients (60.87 ± 12.42 years; 97 males, 47.8%) receiving adjuvant chemotherapy (Oxaliplatin and/or 5-Fluorouracil) were retrospectively evaluated. An artificial intelligence segmentation model was used to extract 2D and 3D body composition measurements from each patients' single mid-L3 CT slice as well as multiple-L3 CT scans to produce a 3D BC report. DLT was defined as any incidence of dose reduction or discontinuation due to chemotherapy toxicities. A receiver operating characteristic (ROC) analysis was performed on BSA and individual body composition measurements to demonstrate their predictive performance.

RESULTS

A total of 120 (59.1%) patients experienced DLT. Age and BSA did not vary significantly between DLT and non-DLT group. Females were significantly more likely to experience DLT (p = 4.9 × 10-3). In all patients, the predictive effectiveness of 2D body composition measurements (females: AUC = 0.50-0.54; males: AUC = 0.50-0.61) was equivalent to that of BSA (females: AUC = 0.49; males: AUC = 0.58). The L3 3D skeletal muscle volume was the most predictive indicator of DLT (AUC of 0.66 in females and 0.64 in males).

CONCLUSION

Compared to BSA and 2D body composition measurements, 3D L3 body composition measurements had greater potential to predict DLT in CRC patients receiving chemotherapy and this was sex dependent.

Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer

OBJECTIVES

Skeletal muscle gauge (SMG) was recently introduced as an imaging indicator of sarcopenia. Computed tomography is essential for measuring SMG; thus, the use of SMG is limited to patients who undergo computed tomography. We aimed to develop a machine learning algorithm using clinical and inflammatory markers to predict SMG in patients with colorectal cancer.

METHODS

The least absolute shrinkage and selection operator regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of the least absolute shrinkage and selection operator model, defined as linear predictor (LP)-SMG, was compared using the area under the receiver operating characteristic curve and decision curve analysis in the test set.

RESULTS

A total of 1094 patients with colorectal cancer were enrolled and randomly categorized into training (n = 656) and test (n = 438) sets. Low SMG was identified in 142 (21.6%) and 90 (20.5%) patients in the training and test sets, respectively. According to multivariable analysis of the test sets, LP-SMG was identified as an independent predictor of low SMG (odds ratio = 1329.431; 95% CI, 271.684-7667.996; P < .001). Its predictive performance was similar in the training and test sets (area under the receiver operating characteristic curve = 0.846 versus 0.869; P = .427). In the test set, LP-SMG had better outcomes in predicting SMG than single clinical variables, such as sex, height, weight, and hemoglobin.

CONCLUSIONS

LP-SMG had superior performance than single variables in predicting low SMG. This machine learning model can be used as a screening tool to detect sarcopenic status without using computed tomography during the treatment period.

Adiposity and cancer survival: a systematic review and meta-analysis

PURPOSE

The increasing availability of clinical imaging tests (especially CT and MRI) that directly quantify adipose tissue has led to a rapid increase in studies examining the relationship of visceral, subcutaneous, and overall adiposity to cancer survival. To summarize this emerging body of literature, we conducted a systematic review and meta-analysis of imaging-measured as well as anthropometric proxies for adipose tissue distribution and cancer survival across a wide range of cancer types.

METHODS

Using keywords related to adiposity, cancer, and survival, we conducted a systematic search of the literature in PubMed and MEDLINE, Embase, and Web of Science Core Collection databases from database inception to 30 June 2021. We used a random-effect method to calculate pooled hazard ratios (HR) and corresponding 95% confidence intervals (CI) within each cancer type and tested for heterogeneity using Cochran's Q test and the I2 test.

RESULTS

We included 203 records for this review, of which 128 records were utilized for quantitative analysis among 10 cancer types: breast, colorectal, gastroesophageal, head and neck, hepatocellular carcinoma, lung, ovarian, pancreatic, prostate, and renal cancer. We found that imaging-measured visceral, subcutaneous, and total adiposity were not significantly associated with increased risk of overall mortality, death from primary cancer, or cancer progression among patients diagnosed with these 10 cancer types; however, we found significant or high heterogeneity for many cancer types. For example, heterogeneity was similarly high when the pooled HRs (95% CI) for overall mortality associated with visceral adiposity were essentially null as in 1.03 (0.55, 1.92; I2 = 58%) for breast, 0.99 (0.81, 1.21; I2 = 71%) for colorectal, versus when they demonstrated a potential increased risk 1.17 (0.85, 1.60; I2 = 78%) for hepatocellular carcinoma and 1.62 (0.90, 2.95; I2 = 84%) for renal cancer.

CONCLUSION

Greater adiposity at diagnosis (directly measured by imaging) is not associated with worse survival among cancer survivors. However, heterogeneity and other potential limitations were noted across studies, suggesting differences in study design and adiposity measurement approaches, making interpretation of meta-analyses challenging. Future work to standardize imaging measurements and data analyses will strengthen research on the role of adiposity in cancer survival.

Skeletal muscle radiodensity and cancer outcomes: A scoping review of the literature

Patients with cancer are more prone to experience myosteatosis than healthy individuals. The aim of this review was to summarize the methodologies applied for low skeletal muscle radiodensity (SMD) assessment in oncology patients, as well as to describe the major findings related to SMD and cancer outcomes. This scoping review included studies that were published until November 2020 in English, Portuguese, or Spanish; were performed in humans diagnosed with cancer, adult and/or elderly, of both sexes; investigated SMD through computed tomography of the region between the third and fifth lumbar vertebrae, considering at least two muscular groups; and evaluated clinical and/or surgical outcomes. Eighty-eight studies met the inclusion criteria (n = 37,583 patients). Survival was the most evaluated outcome. Most studies reported a significant association between low SMD and unfavorable outcomes. However, this relationship was not clear for survival, antineoplastic treatment, and surgical complications, potentially because of the unstandardized approaches for the assessment of SMD and inadequate study design. Future studies should address these issues to provide an in-depth understanding of the clinical relevance of SMD in cancer outcomes as well as how SMD is influenced by individuals and tumor-related characteristics in patients with cancer.

Updated systematic review and meta-analysis on diagnostic issues and the prognostic impact of myosteatosis: A new paradigm beyond sarcopenia

Myosteatosis, which is excessive fat infiltration in the skeletal muscle, is now considered a distinct disease from sarcopenia. Advances in imaging technique have made muscle parameters an evaluable biomarker, and many studies have proved association between myosteatosis and aging or disease process. However, the diagnosis and clinical impact of myosteatosis have not been well established. Thus, we aim to provide a systematic summary with a qualitive review of 73 eligible studies regarding these issues. First, the most widely used modality to diagnose myosteatosis is abdominal computed tomography, based on evaluation of the muscle radiodensity of the total abdominal muscle area predominantly at the L3 vertebral level. However, there was significant heterogeneity in the diagnostic methods and cutoff values used to diagnose myosteatosis (32 different cutoff values among 73 studies). Second, the clinical impact of myosteatosis on prognosis was very straightforward, and most studies have shown a negative impact of myosteatosis on overall survival and complications related to underlying diseases. However, the mechanism of the myosteatosis on mortality has not been explored well, and metabolic dysfunction (i.e. insulin resistance, systemic inflammation) would be a possible explanation. Providing systemic review of current issues can elucidate future directions for developing standardized diagnosis and management of myosteatosis.

Psoas Muscle Volume and Attenuation are Better Predictors than Muscle Area for Hospital Readmission in Older Patients after Transcatheter Aortic Valve Implantation

OBJECTIVE

This study aimed to determine whether the psoas muscle volume (PMV) and its muscle attenuation (MA) are associated with hospital readmission after transcatheter aortic valve implantation (TAVI).

METHOD

We included 113 older patients with aortic stenosis who underwent TAVI at Sakakibara Heart Institute (mean age 85 ± 5 years, 69% women). We measured PMV and psoas muscle area (PMA) as well as total muscle area (TMA) at the third lumbar vertebra using preoperative computed tomography (CT) images. The crude values of the PMV, PMA, and TMA were normalized by dividing by height squared.

RESULTS

The median follow-up period was 724 days (interquartile range: 528-730 days), and there were 25 all-cause readmissions during the follow-up period (22% of all patients). In the multivariate Cox regression analysis adjusted for age, sex, and EuroSCORE II, the PMV and its MA and crude PMA were significantly associated with all-cause readmission [HR: 0.957 (0.930-0.985), p = 0.003, HR: 0.927 (0.862-0.997), p = 0.040], whereas the PMA and TMA and each MA were not (all p > 0.05). The groups with low PMV and MA had significantly higher incidences of all-cause readmission than that with high PMV and MA (log-rank test: p = 0.011).

CONCLUSION

PMV and its MA measured from preoperative CT images were independent predictors of all-cause readmission in TAVI patients.

Pathophysiological mechanisms explaining poor clinical outcome of older cancer patients with low skeletal muscle mass

Low skeletal muscle mass is highly prevalent in older cancer patients and affects 5% to 89% depending on the type and stage of cancer. Low skeletal muscle mass is associated with poor clinical outcomes such as post-operative complications, chemotherapy toxicity and mortality in older cancer patients. Little is known about the mediating pathophysiological mechanisms. In this review, we summarize proposed pathophysiological mechanisms underlying the association between low skeletal muscle mass and poor clinical outcomes in older cancer patients including a) systemic inflammation; b) insulin-dependent glucose handling; c) mitochondrial function; d) protein status and; e) pharmacokinetics of anticancer drugs. The mechanisms of altered myokine balance negatively affecting the innate and adaptive immune system, and altered pharmacokinetics of anticancer drugs leading to a relative overdosage of anticancer drugs are best-substantiated. The effects of glucose intolerance and circulating mitochondrial DNA as a consequence of low skeletal muscle mass are topics of interest for future research. Restoring myokine balance through physical exercise, exercise mimetics, neuro-muscular activation and adapting anticancer drug dosing on skeletal muscle mass could be targeted approaches to improve clinical outcomes in older cancer patients with low skeletal muscle mass.

The Predictive Value of Low Skeletal Muscle Mass Assessed on Cross-Sectional Imaging for Anti-Cancer Drug Toxicity: A Systematic Review and Meta-Analysis

Low skeletal muscle mass (LSMM) is increasingly recognized for its predictive value for adverse events in cancer patients. In specific, the predictive value of LSMM has been demonstrated for anti-cancer drug toxicity in a variety of cancer types and anti-cancer drugs. However, due to the limited sample size and study populations focused on a single cancer type, an overall predictive value of LSMM for anti-cancer drug toxicity remains unknown. Therefore, this review aims to provide a comprehensive overview of the predictive value of LSMM and perform a meta-analysis to analyse the overall effect. A systematic search was conducted of MEDLINE, Scopus, EMBASE, and Cochrane. Inclusion criteria were skeletal muscle mass (SMM) evaluated with computed tomography (CT) or magnetic resonance imaging (MRI), articles published in English, SMM studied in humans, SMM measurement normalized for height, and patients did not receive an intervention to treat or prevent LSMM. A meta-analysis was performed using a random-effects model and expressed in odds ratio (OR) with 95% confidence interval (CI). Heterogeneity was assessed using χ2 and I2 statistics. The search yielded 907 studies. 31 studies were included in the systematic review. Sample sizes ranged from 21 to 414 patients. The occurrence of LSMM ranged from 12.2% to 89.0%. The most frequently studied cancer types were oesophageal, renal, colorectal, breast, and head and neck cancer. Patients with LSMM had a higher risk of severe toxicity (OR 4.08; 95% CI 2.48-6.70; p < 0.001) and dose-limiting toxicity (OR 2.24; 95% CI 1.28-3.92; p < 0.001) compared to patients without LSMM. To conclude, the predictive value of LSMM for anti-cancer drug toxicity can be observed across cancer types. This information increases the need for further research into interventions that could treat LSMM as well as the possibility to adapt treatment regimens based on the presence of LSMM.

Are computed tomography-based measures of specific abdominal muscle groups predictive of adverse outcomes in older cancer patients?

PURPOSE

It is unknown whether computed tomography (CT)-based total abdominal muscle measures are representative of specific abdominal muscle groups and whether analysis of specific abdominal muscle groups are predictive of the risk of adverse outcomes in older cancer patients.

METHODS

Retrospective single-center cohort study in elective colon cancer patients aged ≥65 years. CT-based skeletal muscle (SM) surface area, muscle density and intermuscular adipose tissue (IMAT) surface area were determined for rectus abdominis; external- and internal oblique and transversus abdominis (lateral muscles); psoas; and erector spinae and quadratus lumborum (back muscles). Outcomes were defined as severe postoperative complications (Clavien-Dindo score >2) and long-term survival (median follow-up 5.2 years).

RESULTS

254 older colon cancer patients were included (median 73.6 years, 62.2% males). Rectus abdominis showed the lowest SM surface area and muscle density and the back muscles showed the highest IMAT surface area. Psoas muscle density, and lateral muscle density and percentage IMAT were associated with severe postoperative complications independent of gender, age and cancer stage.

CONCLUSIONS

CT-based total abdominal muscle quantity and quality do not represent the heterogeneity that exists between specific muscle groups. The potential added value of analysis of specific muscle groups in predicting adverse outcomes in older (colon) cancer patients should be further addressed in prospective studies.

Prognostic impact of myosteatosis in patients with colorectal cancer: a systematic review and meta-analysis

BACKGROUND

Myosteatosis has been reported to be a novel biomarker that could predict survival outcomes in patients with colorectal cancer. However, results have been conflicting. This systematic review and meta-analysis aimed to evaluate the long-term impact of myosteatosis on the survival of these patients.

METHODS

A systematic search of PubMed, Embase, and Cochrane up to 27 November 2019 generated 7022 records. Studies that reported hazard ratio (HR) for overall survival, cancer-specific survival, or disease-free survival based on myosteatosis or radiodensity were included. A total of 110 full-text articles were considered for inclusion, and 14 were selected for qualitative analysis. Inverse variance method was used with random effects model for data analysis.

RESULTS

The total number of enrolled patients included in the meta-analysis was 6518 for univariate and 8572 for multivariate HR analysis, from 12 and 10 studies, respectively. Patients with myosteatosis had a significant increase in overall mortality compared with non-myosteatosis patients by both univariate analysis [HR 1.38, 95% confidence interval (CI) 1.21 to 1.58, P < 0.00001] and multivariate analysis (HR 1.55, 95% CI 1.23 to 1.96, P < 0.00001). In subgroup analysis based on studies that reported HRs of both sarcopenia and myosteatosis, the negative effect of myosteatosis on overall survival was independent of sarcopenia using univariate values (sarcopenia HR 1.48, 95% CI 1.14 to 1.91, P = 0.003 vs. myosteatosis HR 1.51, 95% CI 1.17 to 1.96, P = 0.002) and multivariate values (sarcopenia HR 1.28, 95% CI 1.09 to 1.49, P = 0.002 vs. myosteatosis HR 1.38, 95% CI 1.07 to 1.80, P = 0.001).

CONCLUSIONS

This meta-analysis demonstrates that myosteatosis is associated with worse overall survival in patients with colorectal cancer. More investigation is needed to standardize the measurement protocol for myosteatosis and to further optimize its prognostic power for colorectal cancer patients.