AI-based CT Body Composition Identifies Myosteatosis as Key Mortality Predictor in Asymptomatic Adults

AI-based abdominal CT measurements of orthotopic and ectopic fat predict mortality and cardiometabolic disease risk in adults

OBJECTIVES

To evaluate the utility of CT-based abdominal fat measures for predicting the risk of death and cardiometabolic disease in an asymptomatic adult screening population.

METHODS

Fully automated AI tools quantifying abdominal adipose tissue (L3 level visceral [VAT] and subcutaneous [SAT] fat area, visceral-to-subcutaneous fat ratio [VSR], VAT attenuation), muscle attenuation (L3 level), and liver attenuation were applied to non-contrast CT scans in asymptomatic adults undergoing CT colonography (CTC). Longitudinal follow-up documented subsequent deaths, cardiovascular events, and diabetes. ROC and time-to-event analyses were performed to generate AUCs and hazard ratios (HR) binned by octile.

RESULTS

A total of 9223 adults (mean age, 57 years; 4071:5152 M:F) underwent screening CTC from April 2004 to December 2016. 549 patients died on follow-up (median, nine years). Fat measures outperformed BMI for predicting mortality risk-5-year AUCs for muscle attenuation, VSR, and BMI were 0.721, 0.661, and 0.499, respectively. Higher visceral, muscle, and liver fat were associated with increased mortality risk-VSR > 1.53, HR = 3.1; muscle attenuation < 15 HU, HR = 5.4; liver attenuation < 45 HU, HR = 2.3. Higher VAT area and VSR were associated with increased cardiovascular event and diabetes risk-VSR > 1.59, HR = 2.6 for cardiovascular event; VAT area > 291 cm2, HR = 6.3 for diabetes (p < 0.001). A U-shaped association was observed for SAT with a higher risk of death for very low and very high SAT.

CONCLUSION

Fully automated CT-based measures of abdominal fat are predictive of mortality and cardiometabolic disease risk in asymptomatic adults and uncover trends that are not reflected in anthropomorphic measures.

CLINICAL RELEVANCE STATEMENT

Fully automated CT-based measures of abdominal fat soundly outperform anthropometric measures for mortality and cardiometabolic risk prediction in asymptomatic patients.

KEY POINTS

Abdominal fat depots associated with metabolic dysregulation and cardiovascular disease can be derived from abdominal CT. Fully automated AI body composition tools can measure factors associated with increased mortality and cardiometabolic risk. CT-based abdominal fat measures uncover trends in mortality and cardiometabolic risk not captured by BMI in asymptomatic outpatients.

Sarcopenia is associated with short- and long-term mortality in patients with acute-on-chronic liver failure

BACKGROUND

While sarcopenia is recognized as a predictor of mortality in cirrhosis, its influence on acute-on-chronic liver failure (ACLF) remains uncertain. Despite multiple studies examining the impact of sarcopenia on short-term mortality in patients with ACLF, the sample size of these studies was limited, and their outcomes were inconsistent. Therefore, this study aimed to explore the impact of sarcopenia on both short- and long-term mortality in patients with ACLF.

METHODS

This retrospective cohort study included 414 patients with ACLF that were treated between January 2016 and September 2022. Sarcopenia was diagnosed based on the measurement of the skeletal muscle index at the third lumbar vertebra (L3-SMI). Subsequently, the patients were divided into sarcopenia and non-sarcopenia groups. We analysed the basic clinical data of the two groups. Multivariate Cox proportional analysis was used to analyse short-term (28 days) and long-term (1 year and overall) mortality rates.

RESULTS

A total of 414 patients were included, with a mean age of 52.88 ± 13.41 years. Among them, 318 (76.8%) were male, and 239 (57.7%) had sarcopenia. A total of 280 (67.6%) patients died during the study period. Among them, 153 patients died within 28 days (37%) and 209 patients died within 1 year (50.5%). We found that the 28-day, 1-year and overall mortality rates in the sarcopenia group were significantly higher than those in the non-sarcopenia group (37% vs. 22.3%, P < 0.01; 50.5% vs. 34.9%, P < 0.01; and 67.6% vs. 53.1%, P < 0.01, respectively). Multivariate Cox regression analysis revealed that sarcopenia was significantly associated with increased mortality. The hazard ratios for sarcopenia were 2.05 (95% confidence interval [CI] 1.41-3.00, P < 0.01) for 28-day mortality, 1.81 (95% CI 1.29-2.54, P < 0.01) for 1-year mortality and 1.82 (95% CI 1.30-2.55, P < 0.01) for overall mortality. In addition, muscle density and international normalized ratio were associated with short- and long-term mortality.

CONCLUSIONS

Sarcopenia is associated with both short- and long-term mortality in patients with ACLF. Therefore, regular monitoring for sarcopenia is important for these patients.

Cardiometabolic Characteristics of Obesity Phenotypes in Persons With HIV

Background

In the general population, it is established that adipose tissue depots pose various risks for cardiometabolic diseases. The interaction among obesity, HIV, and antiretroviral treatment promotes even greater risk for persons with HIV (PWH). As obesity is a heterogeneous condition, determining the specific obesity phenotypes present and their characteristics is critical to personalize care in PWH.

Methods

Visceral, sarcopenic, myosteatotic, hepatosteatotic, and metabolically healthy obesity phenotypes were determined by pre-established cut points after segmentation of computed tomography scans at the L3 vertebra. Multivariable linear regression modeling included anthropometrics, clinical biomarkers, and inflammatory factors while controlling for age, sex, race, and body mass index (BMI).

Results

Of 187 PWH, 86% were male, and the mean ± SD age and BMI were 51.2 ± 12.3 years and 32.6 ± 6.3 kg/m2. Overall, 59% had visceral obesity, 11% sarcopenic obesity, 25% myosteatotic obesity, 9% hepatosteatotic obesity, and 32% metabolically healthy obesity. The strongest predictor of visceral obesity was an elevated triglyceride:high-density lipoprotein (HDL) ratio. Increased subcutaneous fat, waist circumference, and HDL cholesterol were predictors of sarcopenic obesity. Diabetes status and elevated interleukin 6, waist circumference, and HDL cholesterol predicted myosteatotic obesity. An increased CD4+ count and a decreased visceral:subcutaneous adipose tissue ratio predicted hepatosteatotic obesity, though accounting for only 28% of its variability. Participants with metabolically healthy obesity were on average 10 years younger, had higher HDL, lower triglyceride:HDL ratio, and reduced CD4+ counts.

Conclusions

These findings show that discrete obesity phenotypes are highly prevalent in PWH and convey specific risk factors that measuring BMI alone does not capture. These clinically relevant findings can be used in risk stratification and optimization of personalized treatment regimens. This study is registered at ClinicalTrials.gov (NCT04451980).

Prognostic impact of myosteatosis in patients with colorectal cancer undergoing curative surgery: an updated systematic review and meta-analysis

Background

Colorectal cancer (CRC) is a global health concern, and identifying prognostic factors can improve outcomes. Myosteatosis is fat infiltration into muscles and is a potential predictor of the survival of patients with CRC.

Methods

This systematic review and meta-analysis aimed to assess the prognostic role of myosteatosis in CRC. PubMed, Embase, and Cochrane CENTRAL were searched up to 1 August 2023, for relevant studies, using combinations of the keywords CRC, myosteatosis, skeletal muscle fat infiltration, and low skeletal muscle radiodensity. Case-control, prospective, and retrospective cohort studies examining the association between myosteatosis and CRC outcomes after curative intent surgery were eligible for inclusion. Primary outcomes were overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS).

Results

A total of 10 studies with a total of 9,203 patients were included. The pooled hazard ratio (HR) for OS (myosteatosis vs. no myosteatosis) was 1.52 [95% confidence interval (CI), 1.38-1.67); for CSS, 1.67 (95% CI, 1.40-1.99); and for DFS, 1.89 (95% CI, 1.35-2.65).

Conclusion

In patients with CRC undergoing curative intent surgery, myosteatosis is associated with worse OS, CSS, and DFS. These findings underscore the importance of evaluating myosteatosis in patients with CRC to improve outcomes.

The association between computed tomography-based osteosarcopenia and osteoporotic vertebral fractures: a longitudinal study

PURPOSE

Osteoporosis and sarcopenia usually coexist in older population. The concept of osteosarcopenia has been proposed in recent years. However, studies on the relationship between osteosarcopenia and the risk of fracture are rare, and the association is unclear at present. This study aimed to investigate the association between osteosarcopenia evaluated based on chest computed tomography (CT) and osteoporotic vertebral fracture (OVF).

METHODS

This study recruited 7906 individuals aged 50 years and older who did not have OVFs and underwent chest CT for physical examination between July 2016 and September 2019. Subjects were followed up annually until June 2023. Osteosarcopenia was defined by a low muscle area of the erector spinae (< 25.4 cm2) and the bone attenuation (Hounsfield unit, HU < 135). Genant's grades were used to define OVFs. Control subjects were selected by Propensity Score Matching at a ratio 20:1. Cox proportional hazards models were used to assess the associations between osteosarcopenia and OVFs.

RESULTS

Of the 7906 participants included, 95 had a new OVF within a median follow-up of 3 years. A total of 1900 control subjects were matched. Individuals in the osteosarcopenia group had a higher prevalence of spinal fractures than those in normal group (16.4% vs. 0.4%, P < 0.001). Osteosarcopenia was independently associated with OVF (adjusted hazard ratio (aHR): 12.67, 95% confidence interval (CI) 3.79-42.40) and severe OVF (aHR = 14.07, 95% CI 1.84-107.66). Similar trends were observed in males, females and those subjects aged older than 60 years. Osteosarcopenia had good predictive efficacy for OVF (area under the curve = 0.836). A nomogram was also developed for clinical application.

CONCLUSION

Osteosarcopenia assessed based on chest CT was associated with OVF, and osteosarcopenia has good performance for vertebral fracture prediction.

Integration of new technologies in the multidisciplinary approach to primary liver tumours: The next-generation tumour board

Primary liver tumours, including benign liver tumours, hepatocellular carcinoma and cholangiocarcinoma, present a multifaceted challenge, necessitating a collaborative approach, as evidenced by the role of the multidisciplinary tumour board (MDTB). The approach to managing primary liver tumours involves specialised teams, including surgeons, radiologists, oncologists, pathologists, hepatologists, and radiation oncologists, coming together to propose individualised treatment plans. The evolving landscape of primary liver cancer treatment introduces complexities, particularly with the expanding array of systemic and locoregional therapies, alongside the potential integration of molecular biology and artificial intelligence (AI) into MDTBs in the future. Precision medicine demands collaboration across disciplines, challenging traditional frameworks. In the next decade, we anticipate the convergence of AI, molecular biology, pathology, and advanced imaging, requiring adaptability in MDTB structure to incorporate these cutting-edge technologies. Navigating this evolution also requires a focus on enhancing basic, translational, and clinical research, as well as boosting clinical trials through an upgraded use of MDTBs as hubs for scientific collaboration and raising literacy about AI and new technologies. In this review, we will delineate the current unmet needs in the clinical management of primary liver cancers, discuss our perspective on the future role of MDTBs in primary liver cancers ("next generation" MDTBs), and unravel the potential power and limitations of novel technologies that may shape the multidisciplinary care landscape for primary liver cancers in the coming decade.

Adjusting for the effect of IV contrast on automated CT body composition measures during the portal venous phase

OBJECTIVE

Fully-automated CT-based algorithms for quantifying numerous biomarkers have been validated for unenhanced abdominal scans. There is great interest in optimizing the documentation and reporting of biophysical measures present on all CT scans for the purposes of opportunistic screening and risk profiling. The purpose of this study was to determine and adjust the effect of intravenous (IV) contrast on these automated body composition measures at routine portal venous phase post-contrast imaging.

METHODS

Final study cohort consisted of 1,612 older adults (mean age, 68.0 years; 594 women) all imaged utilizing a uniform CT urothelial protocol consisting of pre-contrast, portal venous, and delayed excretory phases. Fully-automated CT-based algorithms for quantifying numerous biomarkers, including muscle and fat area and density, bone mineral density, and solid organ volume were applied to pre-contrast and portal venous phases. The effect of IV contrast upon these body composition measures was analyzed. Regression analyses, including square of the Pearson correlation coefficient (r2), were performed for each comparison.

RESULTS

We found that simple, linear relationships can be derived to determine non-contrast equivalent values from the post-contrast CT biomeasures. Excellent positive linear correlation (r2 = 0.91-0.99) between pre- and post-contrast values was observed for all automated soft tissue measures, whereas moderate positive linear correlation was observed for bone attenuation (r2 = 0.58-0.76). In general, the area- and volume-based measurement require less adjustment than attenuation-based measures, as expected.

CONCLUSION

Fully-automated quantitative CT-biomarker measures at portal venous phase abdominal CT can be adjusted to a non-contrast equivalent using simple, linear relationships.

Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults

BACKGROUND

Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosis and sarcopenia by identifying metabolic clusters simultaneously. We also investigated the prognostic value of metabolic phenotyping by CT model for long-term mortality.

METHODS

The derivation set (n = 516; 75% train set, 25% internal test set) was constructed using age- and sex-stratified random sampling from two community-based cohorts. Data from participants in the individual health assessment programme (n = 380) were used as the external test set 1. Semi-automatic quantification of body compositions at multiple levels of abdominal CT scans was performed to train a multi-layer perceptron (MLP)-based multi-label classification model. External test set 2 to test the prognostic value of the model output for mortality was built using data from individuals who underwent abdominal CT in a tertiary-level institution (n = 10 141).

RESULTS

The mean ages of the derivation and external sets were 62.8 and 59.7 years, respectively, without difference in sex distribution (women 50%) or body mass index (BMI; 23.9 kg/m2). Skeletal muscle density (SMD) and bone density (BD) showed a more linear decrement across age than skeletal muscle area. Alternatively, an increase in visceral fat area (VFA) was observed in both men and women. Hierarchical clustering based on multi-level CT body composition parameters revealed three distinctive phenotype clusters: normal, MS and osteosarcopenia clusters. The L3 CT-parameter-based model, with or without clinical variables (age, sex and BMI), outperformed clinical model predictions of all outcomes (area under the receiver operating characteristic curve: MS, 0.76 vs. 0.55; osteoporosis, 0.90 vs. 0.79; sarcopenia, 0.85 vs. 0.81 in external test set 1; P < 0.05 for all). VFA contributed the most to the MS predictions, whereas SMD, BD and subcutaneous fat area were features of high importance for detecting osteoporosis and sarcopenia. In external test set 2 (mean age 63.5 years, women 79%; median follow-up 4.9 years), a total of 907 individuals (8.9%) died during follow-up. Among model-predicted metabolic phenotypes, sarcopenia alone (adjusted hazard ratio [aHR] 1.55), MS + sarcopenia (aHR 1.65), osteoporosis + sarcopenia (aHR 1.83) and all three combined (aHR 1.87) remained robust predictors of mortality after adjustment for age, sex and comorbidities.

CONCLUSIONS

A CT body composition-based MLP model detected MS, osteoporosis and sarcopenia simultaneously in community-dwelling and hospitalized adults. Metabolic phenotypes predicted by the CT MLP model were associated with long-term mortality, independent of covariates.

Abdominal computed tomography measurements of body composition and waitlist mortality in kidney transplant candidates

Body mass index is often used to determine kidney transplant (KT) candidacy. However, this measure of body composition (BC) has several limitations, including the inability to accurately capture dry weight. Objective computed tomography (CT)-based measures may improve pre-KT risk stratification and capture physiological aging more accurately. We quantified the association between CT-based BC measurements and waitlist mortality in a retrospective study of 828 KT candidates (2010-2022) with clinically obtained CT scans using adjusted competing risk regression. In total, 42.5% of candidates had myopenia, 11.4% had myopenic obesity (MO), 68.8% had myosteatosis, 24.8% had sarcopenia (probable = 11.2%, confirmed = 10.5%, and severe = 3.1%), and 8.6% had sarcopenic obesity. Myopenia, MO, and sarcopenic obesity were not associated with mortality. Patients with myosteatosis (adjusted subhazard ratio [aSHR] = 1.62, 95% confidence interval [CI]: 1.07-2.45; after confounder adjustment) or sarcopenia (probable: aSHR = 1.78, 95% CI: 1.10-2.88; confirmed: aSHR = 1.68, 95% CI: 1.01-2.82; and severe: aSHR = 2.51, 95% CI: 1.12-5.66; after full adjustment) were at increased risk of mortality. When stratified by age, MO (aSHR = 2.21, 95% CI: 1.28-3.83; P interaction = .005) and myosteatosis (aSHR = 1.95, 95% CI: 1.18-3.21; P interaction = .038) were associated with elevated risk only among candidates <65 years. MO was only associated with waitlist mortality among frail candidates (adjusted hazard ratio = 2.54, 95% CI: 1.28-5.05; P interaction = .021). Transplant centers should consider using BC metrics in addition to body mass index when a CT scan is available to improve pre-KT risk stratification at KT evaluation.

survex: an R package for explaining machine learning survival models

SUMMARY

Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.

AVAILABILITY AND IMPLEMENTATION

survex is available under the GPL3 public license at https://github.com/modeloriented/survex and on CRAN with documentation available at https://modeloriented.github.io/survex.

Predictive Value of CT Biomarkers in Lung Transplantation Survival: Preliminary Investigation in a Diverse, Underserved, Urban Population

INTRODUCTION

Survival following lung transplant is low. With limited donor lung availability, predicting post-transplant survival is key. We investigated the predictive value of pre-transplant CT biomarkers on survival.

METHODS

In this single-center retrospective cohort study of adults in a diverse, underserved, urban lung transplant program (11/8/2017-5/20/2022), chest CTs were analyzed using TeraRecon to assess musculature, fat, and bone. Erector spinae and pectoralis muscle area and attenuation were analyzed. Sarcopenia thresholds were 34.3 (women) and 38.5 (men) Hounsfield Units (HU). Visceral and subcutaneous fat area and HU, and vertebral body HU were measured. Demographics and pre-transplant metrics were recorded. Survival analyses included Kaplan-Meier and Cox proportional hazard.

RESULTS

The study cohort comprised 131 patients, 50 women, mean age 60.82 (SD 10.15) years, and mean follow-up 1.78 (SD 1.23) years. Twenty-nine percent were White. Mortality was 32.1%. Kaplan-Meier curves did not follow the proportional hazard assumption for sex, so analysis was stratified. Pre-transplant EMR metrics did not predict survival. Women without sarcopenia at erector spinae or pectoralis had 100% survival (p = 0.007). Sarcopenia did not predict survival in men and muscle area did not predict survival in either sex. Men with higher visceral fat area and HU had decreased survival (p = 0.02). Higher vertebral body density predicted improved survival in men (p = 0.026) and women (p = 0.045).

CONCLUSION

Pre-transplantation CT biomarkers had predictive value in lung transplant survival and varied by sex. The absence of sarcopenia in women, lower visceral fat attenuation and area in men, and higher vertebral body density in both sexes predicted survival in our diverse, urban population.