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

Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients

BACKGROUND

This study aimed to construct a novel model and validate its predictive power in non-esophageal squamous cell carcinoma (NESCC) patients.

METHODS

This retrospective study included 151 patients between October 2006 and September 2016. The LASSO Cox and Random Survival Forest (RSF) models were developed with the help of hematological biomarkers and clinical characteristics. The concordance index (C-index) was used to assess the prognostic power of the LASSO Cox model, RSF model, and TNM staging. Based on the risk scores of the LASSO Cox and RSF models, we divided patients into low-risk and high-risk subgroups.

RESULTS

We constructed two models in NESCC patients according to LASSO Cox regression and RSF models. The RSF model reached a C-index of 0.841 (95% CI: 0.792-0.889) in the primary cohort and 0.880 (95% CI: 0.830-0.930) in the validation cohort, which was higher than the C-index of the LASSO Cox model 0.656 (95% CI: 0.580-0.732) and 0.632 (95% CI: 0.542-0.720) in the two cohorts. The integrated C/D area under the ROC curve (AUC) values for the LASSO Cox and RSF models were 0.701 and 0.861, respectively. In both two models, Kaplan-Meier survival analysis and the estimated restricted mean survival time (RMST) values indicated that the low-risk subgroup had a better prognostic outcome than the high-risk subgroup (p < 0.05).

CONCLUSIONS

The RSF model has better prediction power than the LASSO Cox and the TNM staging models. It has a guiding value for the choice of individualized treatment in patients with NESCC.

Comparison of Muscle and Fat Measurements Between True Noncontrast and Virtual Noncontrast Images From Three Phases of Dual-Energy Dynamic Liver CT

RATIONALE AND OBJECTIVES

To determine whether virtual noncontrast (VNC) images derived from three-phase dynamic dual-energy CT (DECT) imaging can reliably substitute true noncontrast (TNC) images in assessing muscle and fat with automatic segmentation software.

MATERIALS AND METHODS

The data from 476 dynamic liver DECT examinations performed between April 2019 and December 2020 were retrospectively analyzed. VNC images were generated from arterial (VNCa), portal-venous (VNCp), and delayed (VNCd) phase images. Automated software measured muscle, visceral fat (VF), and subcutaneous fat (SF) areas. Sarcopenia was defined using muscle-related indices. Differences in muscle and fat measurements, as well as sarcopenia prevalence, between TNC and VNC images were assessed using paired t tests and McNemar tests, respectively.

RESULTS

The average age of the 476 patients (307 men) was 58.4±12.5years. Muscle density and area differed significantly between the TNC and VNC images; TNC images showed higher mean muscle density and smaller skeletal muscle area (SMA) than all VNC images (P<0.001). VF and SF attenuations were significantly lower on TNC images than on all VNC images (P<0.001). The proportions of sarcopenic patients did not differ significantly between TNC and VNCp or VNCd, regardless of the muscle index used.

CONCLUSION

Despite significant differences in muscle and fat attenuations between TNC and VNC images, VNCp and VNCd may be acceptable alternatives for measuring muscle and fat areas. However, TNC and VNC images should not be used interchangeably for assessing tissue attenuation or specific muscle components such as LAMA or NAMA.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans for mortality prediction: a multicentre study

BACKGROUND

CT attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only used for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and to evaluate these measures for all-cause mortality risk stratification.

METHODS

We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry at four sites (Yale University, University of Calgary, Columbia University, and University of Ottawa), to define the chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle, subcutaneous adipose tissue, intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT) were quantified between automatically identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation and indexed volumes were evaluated for predicting all-cause mortality, adjusting for established risk factors and 18 other body composition measures via Cox regression models and Kaplan-Meier curves.

FINDINGS

The end-to-end processing time was less than 2 min per scan with no user interaction. Between 2009 and 2021, we included 11 305 participants from four sites participating in the REFINE SPECT registry, who underwent single-photon emission computed tomography cardiac scans. After excluding patients who had incomplete T5-T11 scan coverage, missing clinical data, or who had been used for EAT model training, the final study group comprised 9918 patients. 5451 (55%) of 9918 participants were male and 4467 (45%) of 9918 participants were female. Median follow-up time was 2·48 years (IQR 1·46-3·65), during which 610 (6%) patients died. High VAT, EAT, and IMAT attenuation were associated with an increased all-cause mortality risk (adjusted hazard ratio 2·39, 95% CI 1·92-2·96; p<0·0001, 1·55, 1·26-1·90; p<0·0001, and 1·30, 1·06-1·60; p=0·012, respectively). Patients with high bone attenuation were at reduced risk of death (0·77, 0·62-0·95; p=0·016). Likewise, high skeletal muscle volume index was associated with a reduced risk of death (0·56, 0·44-0·71; p<0·0001).

INTERPRETATION

CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers that can be automatically measured and offer important additional prognostic value.

FUNDING

The National Heart, Lung, and Blood Institute, National Institutes of Health.

The Ratio of Visceral to Subcutaneous Adipose Tissue Is Associated With Postoperative Anastomotic Leakage in Patients With Rectal Cancer With Gender Differences in Opposite Direction

BACKGROUND

Anastomotic leakage (AL) is a severe postoperative complication in colorectal cancer and exerts negative impacts on patients' outcomes. Studies have found that body composition measured by CT images was associated with increased overall postoperative complications in colorectal cancer; however, few focused on postoperative AL in rectal cancer. This study aimed to explore the association between body composition parameters measured by CT images and postoperative AL in patients with rectal cancer, with an emphasis on subgroup analysis by gender.

METHODS

From February 2014 to January 2020, a total of 444 patients with rectal adenocarcinoma who underwent radical proctectomy were included. Out of all patients, 21 developed AL after surgery. Body composition parameters, including the areas, mean CT values, height-normalized indices of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), intramuscular adipose tissue (IMAT) and skeletal muscle (SM) were derived from preoperative contrast-enhanced arterial phase CT images at the third lumbar level. The ratio of visceral to subcutaneous adipose tissue (VSR) was calculated. Clinical and body composition parameters were compared between the AL group and the non-AL group in all patients and separately in different genders.

RESULTS

Body composition parameters were not significantly different in the AL group and the non-AL group in all patients. However, most body composition parameters were significantly different between male and female patients. After separately analyzing by gender, VSR was significantly associated with postoperative AL in male and female. After multivariate regression, VSR remained an independent predictor for AL (OR: 0.1, p = 0.041 for male and OR: 39.1, p = 0.045 for female).

CONCLUSION

The VSR measured by CT images is an independent predictor for postoperative AL in patients with rectal cancer; however, it shows gender differences in opposite directions, serving as a protective factor in males, whereas as a risk factor in females.

Validating a Practical Correction for Intravenous Contrast on Computed Tomography-Based Muscle Density

OBJECTIVE

Computed tomography (CT) measured muscle density is prognostic of health outcomes. However, the use of intravenous contrast obscures prognoses by artificially increasing CT muscle density. We previously established a correction to equalize contrast and noncontrast muscle density measurements. While this correction was validated internally, the objective of this study was to obtain external validation using different patient cohorts, muscle regions, and CT series.

METHODS

CT images from 109 patients with kidney tumors who received abdominal CT scans with a multiphase intravenous contrast protocol were analyzed. Paraspinal muscle density measurements taken during noncontrast, venous phase, and delayed phase contrast scans were collected. An a priori correction of -7.5 Hounsfield units (HU) was applied to muscle measurements. Equivalence testing was utilized to determine statistical similarity.

RESULTS

In the sample of 109 patients (mean age: 63 years [SD: 14.3]; 41.3% female), densities in smaller regions of interest within the paraspinal muscles and the entire paraspinal muscle density (PS) in venous and delayed phase contrast scans were higher than in noncontrast. Equivalence testing showed that average corrected contrast and noncontrast muscle densities were within 3 HU for both muscle measures for the total patient sample, and for a majority of male and female subsamples. The correction is suitable for regions of interests of venous contrast (90% CI: -1.90, -0.69 HU) and delayed contrast scans (90% CI: 0.075, 1.29 HU) and within the PS measures of venous contrast (90% CI: -2.04, -0.94 HU) and delayed contrast scans (90% CI: -0.11, 0.89 HU).

CONCLUSIONS

The previously established correction for contrast of -7.5 HU was applied in a new patient population, axial muscle region, muscle measurement size, and expanded on previously studied contrast phases. The correction produced contrast-corrected muscle densities that were statistically equivalent to noncontrast muscle densities. The simplicity of the correction gives clinicians a tool that seamlessly integrates into practice or research to improve harmonization of data between contrast and noncontrast scans.

Different cutoff values of the skeletal muscle mass and myosteatosis result in different clinical impact on overall survival in oncology. A subanalysis of a clinical trial

BACKGROUND

Body composition analysis, particularly the assessment of sarcopenia and myosteatosis, has emerged as a potential prognostic tool in oncology. However, the clinical implication of body composition parameters remains inconsistent, largely due to the variability in cutoff values used across studies. This study examines the influence on prevalence and prognostic influence of different cutoff values for sarcopenia and myosteatosis in patients in a standardized cohort from a large clinical trial (SORAMIC).

METHODS

This study included 179 patients with unresectable liver cancer from the palliative arm of the SORAMIC trial. Skeletal muscle index (SMI) was calculated by measuring the cross-sectional area of skeletal muscle at the third lumbar vertebra (L3) on baseline CT scans. We then applied 14 published cutoff definitions for sarcopenia (SMI) and 7 for myosteatosis (muscle attenuation) to determine their prevalence in this cohort. Cox regression models were used to analyze the relationship between sarcopenia, myosteatosis, and OS.

RESULTS

The prevalence of sarcopenia ranged from 8.9% (Van der Werf et al.) to 69.8% (Lanic et al.). Overall, 3 of the 14 cutoffs [Van Vledder et al. (HR = 1.53, p = 0.03), Coelen et al. (HR = 1.46, p = 0.03), and Derstine et al. (HR = 1.47, p = 0.04)] showed a relevant association with OS. Other cut off values were not associated with OS. The prevalence of myosteatosis varied between 10.1% (Nachit et al.) and 53.1% (Zhang et al.). One of the 7 cutoffs (Chu et al.) demonstrated a relevant association with OS (HR = 1.53, p = 0.03).

CONCLUSION

The large variability in prevalence and prognostic impact observed across different cutoff definitions underscores the urgent need for standardized, cancer-specific cutoff values for SMI and muscle attenuation. Establishing uniform criteria will enhance the reliability and clinical applicability of body composition metrics as prognostic tools in oncology. Further research should focus on refining these cutoffs and validating them across diverse cancer populations.

Opportunistic prognostication by computerized tomography (CT) in the emergency department: analysis on 1920 patients and creation of a simple and fast scoring system

PURPOSE

To use simple CT measurements of musculoskeletal and cardiovascular systems to create a CT-based score to predict mortality in patients admitted to the Emergency Department (ED).

METHODS

The study received IRB approval. Non-contrast abdominal CT of > 18 year old patients admitted to the ER between January 2019 and January 2020 were evaluated by a team of twelve radiologists to calculate: (1) diameter of the infrarenal aorta in millimeter; (2) cross sectional area and composition (Hounsfield units) of the psoas muscle at the third lumbar vertebra (LV); (3) bone density, as quantified at the first lumbar vertebra (LV); (4) presence or absence of dilated abdominal aorta. Thirty-day all-cause mortality (ACM) was determined through hospital and electronic records.

RESULTS

N = 1920 unique patients were evaluated. The mean age was 65 ± 19 years and 46% were female. Death occurred in 7.9% of patients by 30 days from admission. The derivation dataset comprised 1462 patients. At multivariable analysis, age (OR 1.02, 95% CI: 1.007-1.04, p = 0.005), psoas cross sectional area (OR 0.99, 95% CI: 0.997-0.999, p < 0.001) and density (OR 0.96, 95% CI: 0.95-0.98, p < 0.001), and dilated infrarenal aorta (OR 1.85, 95% CI: 1-3.28, p = 0.04) were predictors of the outcome. We accordingly derived a 4-item risk score. In the derivation dataset, the score yielded moderate-high discrimination, with an AUC of 0.73 and excellent diagnostic agreement. In the validation dataset (N = 458), discrimination was high (AUC = 0.83).

CONCLUSION

Simple measurements gathered during a standard CT may allow determining the risk of mortality in the heterogeneous patient population admitted to the ED in a cost- and time-effective manner.

Baseline Body Composition and 3D-Extracellular Volume Fraction for Predicting Pancreatic Fistula after Distal Pancreatectomy in Pancreatic Body and/or Tail Adenocarcinoma

RATIONALE AND OBJECTIVES

Clinically relevant postoperative pancreatic fistula (CR-POPF) is a threatening complication in body and/or tail pancreatic ductal adenocarcinoma (PDAC) receiving distal pancreatectomy (DP) and is difficult to predict preoperatively. We aimed to identify the role of baseline CT-based body composition analysis and extracellular volume (ECV) map in predicting the risk of CR-POPF preoperatively.

MATERIALS AND METHODS

A total of 329 resectable PDAC patients were enrolled and underwent multiphasic contrast-enhanced CT. Body composition indicators were calculated, and ECV maps were generated through multiphasic CT images. The differences in clinical variables and quantitative parameters between CR-POPF and non-CR-POPF patients were compared. Correlations between ECV fraction and pancreatic fibrosis stage were analyzed. Multivariate logistic regression was performed to screen the independent predictors and develop prediction models for CR-POPF. Receiver operating characteristic curve was utilized to evaluate the predictive performance.

RESULTS

Among 329 patients, 19.76% (65/329) developed CR-POPF. Albumin, pancreatic texture, and intraoperative blood loss were used to build the clinical model with an AUC of 0.764. ECV fraction and total muscle ratio (TMR) were chosen to build the radiological model with an AUC of 0.872. A combined nomogram integrated with albumin, ECV fraction, and TMR could significantly improve the discrimination ability to an AUC of 0.924 (Delong test, all p < 0.05). The ECV fraction showed high positive correlation with histological fibrosis grade (Spearman ρ = 0.81).

CONCLUSION

CT-based body composition analysis and ECV exhibited great potential for predicting CR-POPF in body and/or tail PDAC after DP. The combined nomogram could further improve the predictive performance.

Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study

BACKGROUND AND AIMS

Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET for attenuation correction and potentially contain valuable data about body tissue composition. Deep learning and image processing were combined to automatically quantify skeletal muscle (SM), bone and adipose tissue from these scans and then evaluate their associations with death or myocardial infarction (MI).

METHODS

In PET MPI from three sites, deep learning quantified SM, bone, epicardial adipose tissue (EAT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT). Sex-specific thresholds for abnormal values were established. Associations with death or MI were evaluated using unadjusted and multivariable models adjusted for clinical and imaging factors.

RESULTS

This study included 10 085 patients, with median age 68 (interquartile range 59-76) and 5767 (57%) male. Body tissue segmentations were completed in 102 ± 4 s. Higher VAT density was associated with an increased risk of death or MI in both unadjusted [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.37-1.43] and adjusted (HR 1.24, 95% CI 1.19-1.28) analyses, with similar findings for IMAT, SAT, and EAT. Patients with elevated VAT density and reduced myocardial flow reserve had a significantly increased risk of death or MI (adjusted HR 2.49, 95% CI 2.23-2.77).

CONCLUSIONS

Volumetric body tissue composition can be obtained rapidly and automatically from standard cardiac PET/CT. This new information provides a detailed, quantitative assessment of sarcopenia and cardiometabolic health for physicians.

Synergic value of 3D CT-derived body composition and triglyceride glucose body mass for survival prognostic modeling in unresectable pancreatic cancer

Background

Personalized and accurate survival risk prognostication remains a significant challenge in advanced pancreatic ductal adenocarcinoma (PDAC), despite extensive research on prognostic and predictive markers. Patients with PDAC are prone to muscle loss, fat consumption, and malnutrition, which is associated with inferior outcomes. This study investigated the use of three-dimensional (3D) anthropometric parameters derived from computed tomography (CT) scans and triglyceride glucose-body mass index (TyG-BMI) in relation to overall survival (OS) outcomes in advanced PDAC patients. Additionally, a predictive model for 1 year OS was developed based on body components and hematological indicators.

Methods

A retrospective analysis was conducted on 303 patients with locally advanced PDAC or synchronous metastases undergoing first-line chemotherapy, all of whom had undergone pretreatment abdomen-pelvis CT scans. Automatic 3D measurements of subcutaneous and visceral fat volume, skeletal muscle volume, and skeletal muscle density (SMD) were assessed at the L3 vertebral level by an artificial intelligence assisted diagnosis system (HY Medical). Various indicators including TyG-BMI, nutritional indicators [geriatric nutritional risk index (GNRI) and prealbumin], and inflammation indicators [(C-reactive protein (CRP) and neutrophil to lymphocyte ratio (NLR)] were also recorded. All patients underwent follow-up for at least 1 year and a dynamic nomogram for personalized survival prediction was constructed.

Results

We included 211 advanced PDAC patients [mean (standard deviation) age, 63.4 ± 11.2 years; 89 women (42.2) %)]. Factors such as low skeletal muscle index (SMI) (P = 0.011), high visceral to subcutaneous adipose tissue area ratio (VSR) (P < 0.001), high visceral fat index (VFI) (P < 0.001), low TyG-BMI (P = 0.004), and low prealbumin (P = 0.001) were identified as independent risk factors associated with 1 year OS. The area under the curve of the established dynamic nomogram was 0.846 and the calibration curve showed good consistency. High-risk patients (> 211.9 points calculated using the nomogram) had significantly reduced survival rates.

Conclusion

In this study, the proposed nomogram model (with web-based tool) enabled individualized prognostication of OS and could help to guide risk-adapted nutritional treatment for patients with unresectable PDAC or synchronous metastases.

ALM adjusted by BMI or weight predicts adverse health outcomes in middle-aged and elderly patients with type 2 diabetes

The role of skeletal muscle in the prognosis of patients with Type 2 Diabetes Mellitus (T2DM) remains unclear. This study aimed to systematically evaluate the impact of different muscle-mass adjustment standards on adverse health outcomes in middle-aged and elderly T2DM patients. Retrospective cohort study. A total of 1,818 T2DM patients aged 50 years or older were included in this study. The cohort comprised 45.7% females, with a median age of 63 years. Variables closely correlated with total lean mass (TLM) and appendicular lean mass (ALM) were selected as adjustment indicators. The primary composite endpoints were all-cause mortality, cardiovascular disease (CVD), and fragility fractures. Cox proportional hazards models were used to estimate the risk associated with each indicator, and phenotypic characteristics of high-risk patients were evaluated. During a median follow-up of 63 months, 436 patients reached the primary endpoint. ALM/BMI and ALM/weight were negatively correlated with adverse outcomes in both sexes, even after adjusting for confounding factors (males: ALM/BMI (hazard ratio [HR] = 0.998, 95% confidence interval [CI] = 0.996-0.999, P = 0.005) and ALM/weight (HR = 0.924, 95% CI = 0.864-0.987, P = 0.020); females: ALM/BMI (HR = 0.998, 95% CI = 0.996-1.000, P = 0.030) and ALM/weight (HR = 0.917, 95% CI = 0.860-0.978, P = 0.008), respectively). Individuals with lower ALM/BMI and ALM/weight have poorer metabolic status, greater fat accumulation, more complications, and a lower muscle-to-fat ratio. Our findings demonstrate that both ALM/BMI and ALM/weight can predict adverse health outcomes, suggesting their potential as practical, clinically relevant markers for sarcopenia in T2DM.

Low Skeletal Muscle Density Assessed by Abdominal Computerized Tomography Predicts Outcome in Children With Chronic Kidney Disease

OBJECTIVES

Skeletal muscle loss and abnormal fat distribution are predictors of poor clinical outcomes in adults with chronic kidney disease (CKD). However, the relationship between body composition (muscle mass and adipose tissue) and prognosis in children with CKD has not been well elucidated.

METHODS

The retrospective single-center study enrolled children with CKD and healthy group who underwent an abdominal computerized tomography examination and compared the body composition of the third lumbar spine (L3) between the 2 groups. We defined the primary outcome as hemodialysis, peritoneal dialysis, kidney transplantation, or death. Logistic regression analysis was applied to assess the connection between low skeletal muscle density (SMD) and clinical and demographic variables. Multivariate Cox regression analysis was used to evaluate the risk factors for progression to the primary outcome. Kaplan-Meier survival analysis was performed to compare the effect of different body composition on event-free survival rate.

RESULTS

Thirty-two patients with CKD [estimated glomerular filtration rate: 14.89 (8.86, 29.88) (mL/min/1.73 m2)] and 66 heathy subjects [estimated glomerular filtration rate: 135.72 (121.70, 161.29) (mL/min/1.73 m2)] were recruited in our study. From the assessment of body composition assessed by computerized tomography, skeletal muscle area, SMD, and skeletal muscle index in the CKD group was lower than those in the healthy group (P < .05). On the other hand, visceral fat area and visceral fat index in the CKD group were significantly higher than those in the healthy group (P < .05). In logistic regression analysis, triglyceride (odds ratio: 8.635, 95% confidence interval (CI): 1.153-64.687) was independently associated with low SMD. After adjusting clinical data and body composition, high serum albumin (hazard ratio: 0.873, 95% CI: 0.798-0.955) and high SMD (hazard ratio: 0.895, 95% CI: 0.822-0.974) were protective factors for delaying renal failure. Based on the Kaplan-Meier analysis, only the group with low SMD had lower event-free survival in comparison to the reference group (P < .05).

CONCLUSIONS

These findings suggest that there is significant skeletal muscle loss and decrease in SMD in CKD children. Notably, low SMD is indicative of poor prognosis in CKD children.

Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality

BACKGROUND

Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing from prognostication models, as these measurements require organ segmentation not routinely performed clinically by radiologists. We hypothesize that artificial intelligence facilitates accurate extraction of abdominal CT body composition data, allowing better prediction of outcomes.

METHODS

We conducted a retrospective, single-center observational study of kidney transplant candidates wait-listed between January 1, 2007, and December 31, 2017, with available CT data. Validated deep learning models quantified body composition including fat, aortic calcification, bone density, and muscle mass. Logistic regression was used to compare body composition data to Expected Post-Transplant Survival Score (EPTS) as a predictor of 5-year wait-list mortality.

RESULTS

In all, 899 patients were followed for a median 943 days (interquartile range 320-1,697). Of 899, 589 (65.5%) were men and 680 of 899 (75.6%) were White, non-Hispanic. Of 899, 167 patients (18.6%) died while on the waiting list. Myosteatosis (defined as the lowest tertile of muscle attenuation) and increased total aortic and abdominal calcification were associated with increased 5-year wait-list mortality. Logistic regression showed that imaging parameters performed similarly to EPTS at predicting 5-year wait-list mortality (area under receiver operating characteristic curve 0.70 [0.64-0.75] versus 0.67 [0.62-0.72], respectively), and combining body composition parameters with EPTS led to a slight improved survival prediction (area under receiver operating characteristic curve = 0.72, 95% confidence interval 0.66-0.76).

CONCLUSIONS

Fully automated quantification of body composition in kidney transplant candidates is feasible. Myosteatosis and atherosclerosis are associated with 5-year wait-list mortality.

Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis

PURPOSE

Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker.

MATERIALS AND METHODS

In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low=

RESULTS

A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P =0.066; 44 vs. 14 mo for high vs. low Fat index, P <0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P =0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P =0.01) and Myosteatosis (HR=1.12, P =0.005) on overall survival.

CONCLUSION

The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.

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Key Words

• idiopathic pulmonary fibrosis
• lung diseases
• interstitial
• body composition
• tomography
• x-ray computed
• machine learning
• survival analysis
• sarcopenia
• adipose tissue

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MESH Headings

• Humans
• Idiopathic Pulmonary Fibrosis/diagnostic imaging
• Idiopathic Pulmonary Fibrosis/mortality
• Idiopathic Pulmonary Fibrosis/physiopathology
• Female
• Male
• Aged
• Body Composition
• Retrospective Studies
• Imaging, Three-Dimensional/methods
• Middle Aged
• Tomography, X-Ray Computed/methods
• Predictive Value of Tests
• Survival Rate
• Prognosis
• Aged, 80 and over
• Sarcopenia/diagnostic imaging

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Affiliation(s)

Luca Salhöfer Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Francesco Bonella Department of Pneumology, Center for Interstitial and Rare Lung Diseases, University Hospital Essen, Essen, Germany
Mathias Meetschen Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Lale Umutlu Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Michael Forsting Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Benedikt Michael Schaarschmidt Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Marcel Klaus Opitz Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Jens Kleesiek Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Rene Hosch Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Sven Koitka Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Vicky Parmar Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Felix Nensa Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Johannes Haubold Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany

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Biological age model using explainable automated CT-based cardiometabolic biomarkers for phenotypic prediction of longevity

We derive and test a CT-based biological age model for predicting longevity, using an automated pipeline of explainable AI algorithms that quantifies skeletal muscle, abdominal fat, aortic calcification, bone density, and solid abdominal organs. We apply these AI tools to abdominal CT scans from 123,281 adults (mean age, 53.6 years; 47% women; median follow-up, 5.3 years). The final weighted CT biomarker selection was based on the index of prediction accuracy. The CT model significantly outperforms standard demographic data for predicting longevity (IPA = 29.2 vs. 21.7; 10-year AUC = 0.880 vs. 0.779; p < 0.001). Age- and sex-corrected survival hazard ratio for the highest-vs-lowest risk quartile was 8.73 (95% CI,8.14-9.36) for the CT biological age model, and increased to 24.79 after excluding cancer diagnoses within 5 years of CT. Muscle density, aortic plaque burden, visceral fat density, and bone density contributed the most. Here we show a personalized phenotypic CT biological age model that can be opportunistically-derived, regardless of clinical indication, to better inform risk assessment.

Low Skeletal Muscle Radiodensity Predicts Response to CDK4/6 Inhibitors Plus Aromatase Inhibitors in Advanced Breast Cancer

BACKGROUND

Recent evidence indicates that a dysregulated host metabolism influences treatment outcomes in patients with breast cancer. We investigated the association of computed tomography (CT)-derived body composition indices with therapeutic responses in patients with hormone receptor-positive, HER2-negative advanced breast cancer (ABC) on endocrine plus CDK4/6 inhibitor (CDK4/6i) treatment.

METHODS

The study involved a retrospective cohort of patients with ABC at the Yonsei Cancer Center who received CDK4/6i and aromatase inhibitors as first-line therapy between January 2017 and October 2020. Body composition parameters were estimated from the non-enhanced CT images of the third lumbar spine by commercialized deep learning software. Patients with low skeletal muscle radiodensity (SMD) were defined as patients with SMD of low tertile (≤ 28.7 Hounsfield Units). The primary outcome was progression-free survival (PFS).

RESULTS

Among the 247 female participants (median age, 53 years; mean body mass index [BMI], 23.7 kg/m2), 45.7% had disease progression or death during a median follow-up of 36.4 months. After adjusting for age and visceral metastasis, SMD was the only independent predictor among body composition parameters for worse PFS (adjusted hazard ratio [HR] = 1.20 per standard deviation decrement, 95% CI: 1.01-1.42, p = 0.041), whereas BMI, muscle area, and fat area were not. Participants with low SMD had a higher risk of progression than those without (PFS, 27.2 vs. 51.1 months, p = 0.009; adjusted HR 1.84, 95% CI: 1.22-2.76, p = 0.003). Strong associations between low SMD and poor PFS were observed in groups with pre-menopause status (HR, 3.04 vs. 1.19 in post-menopause; 95% CI: 1.54-5.99, p for interaction < 0.05) and without visceral metastases (HR, 2.95 vs. 1.19 in with visceral metastases; 95% CI: 1.59-5.49, p for interaction < 0.05).

CONCLUSIONS

CT-defined low SMD predicts poor treatment outcomes in patients with ABC undergoing first-line treatment with aromatase inhibitors and CDK4/6i.

The Impact of Radiation Dose on CT-Based Body Composition Analysis: A Large-Animal Study

BACKGROUND

CT-based body composition analysis (BCA) enables the extraction of biomarkers from routine CT data. The influence of body composition on the prognosis of different patient groups has been highlighted in recent years. Typically, the segmentation of muscle and fat compartments is performed with a thresholding-based subsegmentation, which might be influenced by the image noise as a function of radiation dose. This study was performed to investigate the impact of the radiation dose on a fully automated, volumetric CT-based BCA.

METHODS

In this animal study, 20 Göttingen minipigs were subjected to CT scans on six occasions under five different dose settings with gradations compared to the control given in % from volumetric CT dose index (CTDIvol) of the control (5%, 10%, 20%, 40%, control [10.01 mGy]). A database with full dose (FD) and quarter dose (QD) CT scans from The Cancer Imaging Archive served as a human validation cohort. A previously open-source published and validated BCA network was applied to each scan. The following features were extracted as volumes (mL): bone, muscle, subcutaneous adipose tissue (SAT), intermuscular and intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT) and total adipose tissue (TAT). Statistical significance was assessed by a one-way ANOVA with Tukey's multiple comparisons or Kruskal-Wallis with Dunn's post-hoc tests. The correlation between feature volumes in the dose gradations and the control group was analysed using the Spearman or Pearson method.

RESULTS

All BCA features remained consistent up to the 10% group and showed no significant differences compared with the control. In the lowest dose group (5%), there were significant differences concerning the muscle (5% = 1295 mL [211 mL], control = 1338 mL [248 mL]; p = 0.032) and VAT volumetry (5% = 353 mL [208 mL], control = 312 mL [204 mL]; p = 0.026) with median differences of -3.13% (muscle) and 12.3% (VAT), respectively. Significant and strong positive correlations were observed between all low-dose groups and the control (r > 0.977, p < 0.001). The human validation analysis yielded constant volumes for every BCA feature with a strong positive correlation (r > 0.933, p < 0.001).

CONCLUSIONS

Fully automated BCA maintains consistent results in various low-dose settings. Significant deviations are only observed after more than 90% dose reduction in the lowest dose settings (5%), which are currently not used in the clinical routine. This large-animal study demonstrates the consistency of fully automated BCA in different dose settings and may therefore facilitate its integration into the clinical routine.

CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans

BACKGROUND

Body composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.

METHODS

A retrospective dataset of 205 contrast-enhanced thoraco-abdominal CT examinations was used for training, while 54 scans from a publicly available dataset were used for independent testing. Two radiology residents performed manual segmentation, identifying the centers of the L1 and L3 vertebrae and segmenting the corresponding axial slices. MultiResUNet was used to identify CT slices intersecting the L1 and L3 vertebrae, and its performance was evaluated using the mean absolute error (MAE). Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA's performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland-Altman analyses.

RESULTS

On the independent dataset, CompositIA achieved a MAE of about 5 mm in detecting slices intersecting the L1 and L3 vertebrae, with a MAE < 10 mm in at least 85% of cases and a vDSC greater than 0.85 in segmenting axial slices. Regression and Bland-Altman analyses demonstrated a strong linear relationship and good agreement between automated and manual scores (p values < 0.001 for all indices), with mean PREs ranging from 5.13% to 15.18%.

CONCLUSION

CompositIA facilitated the automated quantification of body composition scores, achieving high precision in independent testing.

RELEVANCE STATEMENT

CompositIA is an automated, open-source pipeline for quantifying body composition indices from CT scans, simplifying clinical assessments, and expanding their applicability.

KEY POINTS

Manual body composition assessment from CTs is time-consuming and prone to errors. CompositIA was trained on 205 CT scans and tested on 54 scans. CompositIA demonstrated mean percentage relative errors under 15% compared to manual indices. CompositIA simplifies body composition assessment through an artificial intelligence-driven and open-source pipeline.

Impact of imaging biomarkers from body composition analysis on outcome of endovascularly treated acute ischemic stroke patients

BACKGROUND

We investigate the association of imaging biomarkers extracted from fully automated body composition analysis (BCA) of computed tomography (CT) angiography images of endovascularly treated acute ischemic stroke (AIS) patients regarding angiographic and clinical outcome.

METHODS

Retrospective analysis of AIS patients treated with mechanical thrombectomy (MT) at three tertiary care-centers between March 2019-January 2022. Baseline demographics, angiographic outcome and clinical outcome evaluated by the modified Rankin Scale (mRS) at discharge were noted. Multiple tissues, such as muscle, bone, and adipose tissue were acquired with a deep-learning-based, fully automated BCA from CT images of the supra-aortic angiography.

RESULTS

A total of 290 stroke patients who underwent MT due to cerebral vessel occlusion in the anterior circulation were included in the study. In the univariate analyses, among all BCA markers, only the lower sarcopenia marker was associated with a poor outcome (P=0.007). It remained an independent predictor for an unfavorable outcome in a logistic regression analysis (OR 0.6, 95% CI 0.3 to 0.9, P=0.044). Fat index (total adipose tissue/bone) and myosteatosis index (inter- and intramuscular adipose tissue/total adipose tissue*100) did not affect clinical outcomes.

CONCLUSION

Acute ischemic stroke patients with a lower sarcopenia marker are at risk for an unfavorable outcome. Imaging biomarkers extracted from BCA can be easily obtained from existing CT images, making it readily available at the beginning of treatment. However, further research is necessary to determine whether sarcopenia provides additional value beyond established outcome predictors. Understanding its role could lead to optimized, individualized treatment plans for post-stroke patients, potentially improving recovery outcomes.

Poor muscle quality: A hidden and detrimental health condition in obesity

Poor muscle quality (MQ) is a hidden health condition in obesity, commonly disregarded and underdiagnosed, associated with poor health-related outcomes. This narrative review provides an in-depth exploration of MQ in obesity, including definitions, available assessment methods and challenges, pathophysiology, association with health outcomes, and potential interventions. MQ is a broad term that can include imaging, histological, functional, or metabolic assessments, evaluating beyond muscle quantity. MQ assessment is highly heterogeneous and requires further standardization. Common definitions of MQ include 1) muscle-specific strength (or functional MQ), the ratio between muscle strength and muscle quantity, and 2) muscle composition (or morphological MQ), mainly evaluating muscle fat infiltration. An individual with obesity might still have normal or higher muscle quantity despite having poor MQ, and techniques for direct measurements are needed. However, the use of body composition and physical function assessments is still limited in clinical practice. Thus, more accessible techniques for assessing strength, muscle mass, and composition should be further explored. Obesity leads to adipocyte dysfunction, generating a low-grade chronic inflammatory state, which leads to mitochondrial dysfunction. Adipocyte and mitochondrial dysfunction result in metabolic dysfunction manifesting clinically as insulin resistance, dyslipidemia, and fat infiltration into organs such as muscle, which in excess is termed myosteatosis. Myosteatosis decreases muscle cell function and insulin sensitivity, creating a vicious cycle of inflammation and metabolic derangements. Myosteatosis increases the risk of poor muscle function, systemic metabolic complications, and mortality, presenting prognostic potential. Interventions shown to improve MQ include nutrition, physical activity/exercise, pharmacology, and metabolic and bariatric surgery.

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.

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.

Intrapatient Changes in CT-Based Body Composition After Initiation of Semaglutide (Glucagon-Like Peptide-1 Receptor Agonist) Therapy

BACKGROUND. The long-acting glucagon-like peptide-1 receptor agonist semaglutide is used to treat type 2 diabetes or obesity in adults. Clinical trials have observed associations of semaglutide with weight loss, improved control of diabetes, and cardiovascular risk reduction. OBJECTIVE. The purpose of this study was to evaluate intrapatient changes in body composition after initiation of semaglutide therapy by applying an automated suite of CT-based artificial intelligence (AI) body composition tools. METHODS. This retrospective study included adult patients who were receiving semaglutide treatment and who, between January 2016 and November 2023, underwent abdominopelvic CT within both 5 years before and 5 years after initiation of semaglutide. An automated suite of previously validated CT-based AI body composition tools was applied to scans obtained before semaglutide initiation (hereafter, presemaglutide scans) and scans obtained after semaglutide initiation (hereafter, postsemaglutide scans) to quantify visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) area, skeletal muscle area and attenuation, intermuscular adipose tissue (IMAT) area, liver volume and attenuation, and trabecular bone mineral density (BMD). Patients with weight loss of 5 kg or more and those with weight gain of 5 kg or more between the scans were compared. RESULTS. The study included 241 patients (151 women and 90 men; mean age, 60.4 ± 12.4 [SD] years). In the weight-loss group (n = 67), the postsemaglutide scan, compared with the presemaglutide scan, showed a decrease in VAT area (309.4 vs 341.1 cm2, p < .001), SAT area (371.4 vs 410.7 cm2, p < .001), muscle area (179.2 vs 193.0, p < 0.001), and liver volume (2379.0 vs 2578 HU, p = .009) and an increase in liver attenuation (74.5 vs 67.6 HU, p = .03). In the weight-gain group (n = 48), the postsemaglutide scan, compared with the presemaglutide scan, showed an increase in VAT area (334.0 vs 312.8, p = .002), SAT area (485.8 vs 448.8 cm2, p = .01), and IMAT area (48.4 vs 37.6, p = .009) and a decrease in muscle attenuation (5.9 vs 13.1, p < .001). Other comparisons were not statistically significant (p > .05). CONCLUSION. Patients using semaglutide who lost versus gained weight showed distinct patterns of changes in CT-based body composition measures. Those with weight loss had overall favorable shifts in measures related to cardiometabolic risk. A decrease in muscle attenuation in those with weight gain is consistent with decreased muscle quality. CLINICAL IMPACT. Among patients using semaglutide, automated CT-based AI tools provide biomarkers of changes in body composition beyond those that are evident by standard clinical measures.

Deep learning-based body composition analysis from whole-body magnetic resonance imaging to predict all-cause mortality in a large western population

BACKGROUND

Manually extracted imaging-based body composition measures from a single-slice area (A) have shown associations with clinical outcomes in patients with cardiometabolic disease and cancer. With advances in artificial intelligence, fully automated volumetric (V) segmentation approaches are now possible, but it is unknown whether these measures carry prognostic value to predict mortality in the general population. Here, we developed and tested a deep learning framework to automatically quantify volumetric body composition measures from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population.

METHODS

The framework was developed using data from two large Western European population-based cohort studies, the UK Biobank (UKBB) and the German National Cohort (NAKO). Body composition was defined as (i) subcutaneous adipose tissue (SAT), (ii) visceral adipose tissue (VAT), (iii) skeletal muscle (SM), SM fat fraction (SMFF), and (iv) intramuscular adipose tissue (IMAT). The prognostic value of the body composition measures was assessed in the UKBB using Cox regression analysis. Additionally, we extracted body composition areas for every level of the thoracic and lumbar spine (i) to compare the proposed volumetric whole-body approach to the currently established single-slice area approach on the height of the L3 vertebra and (ii) to investigate the correlation between volumetric and single slice area body composition measures on the level of each vertebral body.

FINDINGS

In 36,317 UKBB participants (mean age 65.1 ± 7.8 years, age range 45-84 years; 51.7% female; 1.7% [634/36,471] all-cause deaths; median follow-up 4.8 years), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81-0.91], p = 0.00023), VSMFF (aHR: 1.06, 95% CI [1.02-1.10], p = 0.0043), and VIMAT (aHR: 1.19, 95% CI [1.05-1.35], p = 0.0056) and mortality after adjustment for demographics (age, sex, BMI, race) and cardiometabolic risk factors (alcohol consumption, smoking status, hypertension, diabetes, history of cancer, blood serum markers). This association was attenuated when using traditional single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area body composition measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R = 0.947), at L3 for VAT (R = 0.892), SM (R = 0.944), and at L4 for IMAT (R = 0.546) (all p < 0.0001). A similar pattern was found in 23,725 NAKO participants (mean age 53.9 ± 8.3 years, age range 40-75; 44.9% female).

INTERPRETATION

Automated volumetric body composition assessment from whole-body MRI predicted mortality in a large Western population beyond traditional clinical risk factors. Single slice areas were highly correlated with volumetric body composition measures but their association with mortality attenuated after multivariable adjustment. As volumetric body composition measures are increasingly accessible using automated techniques, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions.

FUNDING

This project was conducted using data from the German National Cohort (NAKO) (www.nako.de). The NAKO is funded by the Federal Ministry of Education and Research (BMBF) [project funding reference numbers: 01ER1301A/B/C, 01ER1511D, and 01ER1801A/B/C/D], federal states of Germany and the Helmholtz Association, the participating universities and the institutes of the Leibniz Association. This research has been conducted using the UK Biobank Resource under Application Number 80337. MJ was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)-518480401. VKR was funded by American Heart Association Career Development Award 935176 and National Heart, Lung, and Blood Institute-K01HL168231.

Automated extraction of body composition metrics from abdominal CT or MR imaging: A scoping review

PURPOSE

To review methodological approaches for automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and skeletal muscle from abdominal cross-sectional imaging for body composition analysis.

METHOD

Four databases were searched for publications describing automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and/or skeletal muscle from abdominal CT or MR imaging between 2019 and 2023. Included reports were evaluated to assess how imaging modality, cohort size, vertebral level, model dimensionality, and use of a volume or single slice affected segmentation accuracy and/or clinical utility. Exclusion criteria included reports not in English language, manual or semi-automated segmentation methods, reports prior to 2019 or solely of paediatric patients, and those not describing the use of abdominal CT or MR.

RESULTS

After exclusions, 172 reports were included in the review. CT imaging was utilised approximately four times as often as MRI, and segmentation accuracy did not significantly differ between the two modalities. Cohort size had no significant effect on segmentation accuracy. There was little evidence to refute the current practice of extracting body composition metrics from the third lumbar vertebral level. There was no clear benefit of using a 3D model to perform segmentation over a 2D approach.

CONCLUSION

Automated segmentation of intra-abdominal soft tissues for body composition analysis is an intense area of research activity. Segmentation accuracy is not affected by cross-sectional imaging modality. Extracting metrics from a single slice at the third lumbar vertebral level is a common approach, however, extracting metrics from a volumetric slab surrounding this level may increase the resilience of the technique, which is important for clinical translation. A paucity of publicly available datasets led to most reports using different data sources, preventing direct comparison of segmentation techniques. Future efforts should prioritise creating a standardised dataset to facilitate benchmarking of different algorithms and subsequent clinical adoption.

The prognostic value of myosteatosis in pancreatic cancer: A systematic review and meta-analysis

BACKGROUND AND AIMS

The phenomenon of myosteatosis, characterized by the accumulation of ectopic fat within and surrounding skeletal muscle, has been identified as a potential adverse factor in the prognosis of individuals with cancer. This systematic review and meta-analysis sought to examine the association between myosteatosis and survival rates as well as postoperative complications in patients diagnosed with pancreatic cancer (PC).

METHODS

A systematic search was conducted on Web of Science, Embase, and Pubmed until March 25, 2024, to identify pertinent articles assessing the prognostic significance of myosteatosis in patients with PC, utilizing the search terms: myosteatosis, PC, and prognosis. The selected studies were utilized to investigate the prognostic impact of myosteatosis on the survival of PC patients. Forest plots and pooled effects models were employed to present the findings of this meta-analysis. The quality of the included studies was evaluated using the Newcastle-Ottawa Scale (NOS). A total of 565 studies were initially identified from the three databases, with 14 retrospective cohort studies ultimately included in the final quantitative analysis.

RESULTS

The meta-analysis revealed a significant association between myosteatosis and both overall survival (OS) [Hazard Ratio (HR): 1.55, 95 % Confidence Interval (CI): 1.40-1.72, P < 0.001, I2 = 0.0 %] and recurrence-free survival (RFS) (HR 1.48, 95 % CI: 1.17-1.86, P = 0.001, I2 = 0.0 %) in patients diagnosed with PC. Subgroup analyses revealed that myosteatosis continued to be a negative prognostic factor in PC across various treatment modalities, patient populations, and myosteatosis assessment methods. Additionally, myosteatosis was identified as a risk factor for postoperative complications, with a pooled odds ratio of 2.20 (95 % CI: 1.45-3.35, P < 0.001, I2 = 37.5 %). All included studies achieved NOS scores of 6 or higher, indicating a relatively high level of methodological quality.

CONCLUSION

These results suggest that myosteatosis is significantly associated with both survival outcomes and postoperative complications in patients with PC.

Opportunity and Opportunism in Artificial Intelligence-Powered Data Extraction: A Value-Centered Approach

Radiologists' traditional role in the diagnostic process is to respond to specific clinical questions and reduce uncertainty enough to permit treatment decisions to be made. This charge is rapidly evolving due to forces such as artificial intelligence (AI), big data (opportunistic imaging, imaging prognostication), and advanced diagnostic technologies. A new modernistic paradigm is emerging whereby radiologists, in conjunction with computer algorithms, will be tasked with extracting as much information from imaging data as possible, often without a specific clinical question being posed and independent of any stated clinical need. In addition, AI algorithms are increasingly able to predict long-term outcomes using data from seemingly normal examinations, enabling AI-assisted prognostication. As these algorithms become a standard component of radiology practice, the sheer amount of information they demand will increase the need for streamlined workflows, communication, and data management techniques. In addition, the provision of such information raises reimbursement, liability, and access issues. Guidelines will be needed to ensure that all patients have access to the benefits of this new technology and guarantee that mined data do not inadvertently create harm. In this Review, we discuss the challenges and opportunities relevant to radiologists in this changing landscape, with an emphasis on ensuring that radiologists provide high-value care.

Effect of Intravenous Contrast on CT Body Composition Measurements in Patients with Intraductal Papillary Mucinous Neoplasm

BACKGROUND

The effect of differing post-contrast phases on CT body composition measurements is not yet known.

METHODS

A fully automated AI-based body composition analysis using DAFS was performed on a retrospective cohort of 278 subjects undergoing pre-treatment triple-phase CT for pancreatic intraductal papillary mucinous neoplasm. The CT contrast phases included noncontrast (NON), arterial (ART), and venous (VEN) phases. The software selected a single axial CT image at mid-L3 on each phase for body compartment segmentation. The areas (cm2) were calculated for skeletal muscle (SM), intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). The mean Hounsfield units of skeletal muscle (SMHU) within the segmented regions were calculated. Bland-Altman and Chi Square analyses were performed.

RESULTS

SM-NON had a lower percentage of bias [LOA] than SM-ART, -0.7 [-7.6, 6.2], and SM-VEN, -0.3 [-7.6, 7.0]; VAT-NON had a higher percentage of bias than ART, 3.4 [-18.2, 25.0], and VEN, 5.8 [-15.0, 26.6]; and this value was lower for SAT-NON than ART, -0.4 [-14.9, 14.2], and VEN, -0.5 [-14.3, 13.4]; and higher for IMAT-NON than ART, 5.9 [-17.9, 29.7], and VEN, 9.5 [-17.0, 36.1]. The bias in SMHU NON [LOA] was lower than that in ART, -3.8 HU [-9.8, 2.1], and VEN, -7.8 HU [-14.8, -0.8].

CONCLUSIONS

IV contrast affects the voxel HU of fat and muscle, impacting CT analysis of body composition. We noted a relatively smaller bias in the SM, VAT, and SAT areas across the contrast phases. However, SMHU and IMAT experienced larger bias. During threshold risk stratification for CT-based measurements of SMHU and IMAT, the IV contrast phase should be taken into consideration.

Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

Clinically, the body mass index remains the most frequently used metric of overall obesity, although it is flawed by its inability to account for different adipose (i.e., visceral, subcutaneous, and inter/intramuscular) compartments, as well as muscle mass. Numerous prior studies have demonstrated linkages between specific adipose or muscle compartments to outcomes of multiple diseases. Although there are no universally accepted standards for body composition measurement, many studies use a single slice at the L3 vertebral level. In this study, we use computed tomography (CT) studies from patients in The Cancer Genome Atlas (TCGA) to compare current L3-based techniques with volumetric techniques, demonstrating potential limitations with level-based approaches for assessing outcomes. In addition, we identify gene expression signatures in normal kidney that correlate with fat and muscle body composition traits that can be used to predict sex-specific outcomes in renal cell carcinoma.

Body composition differences in patients with Metabolic Dysfunction-Associated Steatotic Liver Disease

Background

Although body composition (BC) has been associated with Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD), there is little evidence of differences in BC in patients with MASLD regarding body mass index (BMI). The aim of this study was to determine differences in BC in terms of BMI and metabolic comorbidities in patients with MASLD.

Materials and methods

It is a cross-sectional study with patients who attended the check-up unit. Liver steatosis was evaluated by controlled attenuation parameter, and patients were classified into five groups according to BMI, presence of MASLD, and metabolic characteristics: <25 kg/m2 non-MASLD; <25 kg/m2-MASLD; Overweight-MASLD; Metabolically Healthy Obese (MHO)-MASLD; and Metabolically Unhealthy Obese (MUO)-MASLD. BC was assessed by bioelectrical impedance and a Bioimpedance Vectorial Analysis (BIVA) was carried out. Differences in BC were analyzed by a One-Way ANOVA test. Univariate and multivariate analyses were performed for factors associated with abnormal BC.

Results

A total of 316 patients were included. 59% (n = 189) were male, with a mean age of 49 ± 10 years. Fat% significantly higher according to BMI was not different between BMI <25 kg/m2-MASLD and Overweight-MASLD groups. Skeletal muscle mass (SMM) was significantly lower in obesity groups with respect to overweight and normal weight groups (p < 0.05); however, no differences were observed in the post-hoc analysis. Extracellular Water/Intracellular Water ratio was significantly higher in the MHO-MASLD group and MUO-MASLD group compared with the BMI <25 kg/m2 non-MASLD group and with the BMI <25 kg/m2-MASLD group. Abnormal Waist Circumference (WC) and liver steatosis were independent factors associated with abnormal BC.

Conclusion

BC in MASLD patients varies according to BMI increase; changes could be explained by loss of SMM and not necessarily by the presence of metabolic abnormalities. High WC and the presence of steatosis are independent factors associated with altered BC.

Computed tomography-based body composition indicative of diabetes after hypertriglyceridemic acute pancreatitis

BACKGROUND

Post‑acute pancreatitis prediabetes/diabetes mellitus (PPDM‑A) is one of the common sequelae of acute pancreatitis (AP). The aim of our study was to build a machine learning (ML)-based prediction model for PPDM-A in hypertriglyceridemic acute pancreatitis (HTGP).

METHODS

We retrospectively enrolled 165 patients for our study. Demographic and laboratory data and body composition were collected. Multivariate logistic regression was applied to select features for ML. Support vector machine (SVM), linear discriminant analysis (LDA), and logistic regression (LR) were used to develop prediction models for PPDM-A.

RESULTS

65 patients were diagnosed with PPDM-A, and 100 patients were diagnosed with non-PPDM-A. Of the 84 body composition-related parameters, 15 were significant in discriminating between the PPDM-A and non-PPDM-A groups. Using clinical indicators and body composition parameters to develop ML models, we found that the SVM model presented the best predictive ability, obtaining the best AUC=0.796 in the training cohort, and the LDA and LR model showing an AUC of 0.783 and 0.745, respectively.

CONCLUSIONS

The association between body composition and PPDM-A provides insight into the potential pathogenesis of PPDM-A. Our model is feasible for reliably predicting PPDM-A in the early stages of AP and enables early intervention in patients with potential PPDM-A.

CT-based body composition analysis and pulmonary fat attenuation volume as biomarkers to predict overall survival in patients with non-specific interstitial pneumonia

BACKGROUND

Non-specific interstitial pneumonia (NSIP) is an interstitial lung disease that can result in end-stage fibrosis. We investigated the influence of body composition and pulmonary fat attenuation volume (CTpfav) on overall survival (OS) in NSIP patients.

METHODS

In this retrospective single-center study, 71 NSIP patients with a median age of 65 years (interquartile range 21.5), 39 females (55%), who had a computed tomography from August 2009 to February 2018, were included, of whom 38 (54%) died during follow-up. Body composition analysis was performed using an open-source nnU-Net-based framework. Features were combined into: Sarcopenia (muscle/bone); Fat (total adipose tissue/bone); Myosteatosis (inter-/intra-muscular adipose tissue/total adipose tissue); Mediastinal (mediastinal adipose tissue/bone); and Pulmonary fat index (CTpfav/lung volume). Kaplan-Meier analysis with a log-rank test and multivariate Cox regression were used for survival analyses.

RESULTS

Patients with a higher (> median) Sarcopenia and lower (< median) Mediastinal Fat index had a significantly better survival probability (2-year survival rate: 83% versus 71% for high versus low Sarcopenia index, p = 0.023; 83% versus 72% for low versus high Mediastinal fat index, p = 0.006). In univariate analysis, individuals with a higher Pulmonary fat index exhibited significantly worse survival probability (2-year survival rate: 61% versus 94% for high versus low, p = 0.003). Additionally, it was an independent risk predictor for death (hazard ratio 2.37, 95% confidence interval 1.03-5.48, p = 0.043).

CONCLUSION

Fully automated body composition analysis offers interesting perspectives in patients with NSIP. Pulmonary fat index was an independent predictor of OS.

RELEVANCE STATEMENT

The Pulmonary fat index is an independent predictor of OS in patients with NSIP and demonstrates the potential of fully automated, deep-learning-driven body composition analysis as a biomarker for prognosis estimation.

KEY POINTS

This is the first study assessing the potential of CT-based body composition analysis in patients with non-specific interstitial pneumonia (NSIP). A single-center analysis of 71 patients with board-certified diagnosis of NSIP is presented Indices related to muscle, mediastinal fat, and pulmonary fat attenuation volume were significantly associated with survival at univariate analysis. CT pulmonary fat attenuation volume, normalized by lung volume, resulted as an independent predictor for death.

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.

Correlation of HbA1c levels with CT-based body composition biomarkers in diabetes mellitus and metabolic syndrome

Diabetes mellitus and metabolic syndrome are closely linked with visceral body composition, but clinical assessment is limited to external measurements and laboratory values including hemoglobin A1c (HbA1c). Modern deep learning and AI algorithms allow automated extraction of biomarkers for organ size, density, and body composition from routine computed tomography (CT) exams. Comparing visceral CT biomarkers across groups with differing glycemic control revealed significant, progressive CT biomarker changes with increasing HbA1c. For example, in the unenhanced female cohort, mean changes between normal and poorly-controlled diabetes showed: 53% increase in visceral adipose tissue area, 22% increase in kidney volume, 24% increase in liver volume, 6% decrease in liver density (hepatic steatosis), 16% increase in skeletal muscle area, and 21% decrease in skeletal muscle density (myosteatosis) (all p < 0.001). The multisystem changes of metabolic syndrome can be objectively and retrospectively measured using automated CT biomarkers, with implications for diabetes, metabolic syndrome, and GLP-1 agonists.

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.

Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.

AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study

Background

Computed tomography attenuation correction (CTAC) scans are routinely obtained during cardiac perfusion imaging, but currently only utilized for attenuation correction and visual calcium estimation. We aimed to develop a novel artificial intelligence (AI)-based approach to obtain volumetric measurements of chest body composition from CTAC scans and evaluate these measures for all-cause mortality (ACM) risk stratification.

Methods

We applied AI-based segmentation and image-processing techniques on CTAC scans from a large international image-based registry (four sites), to define chest rib cage and multiple tissues. Volumetric measures of bone, skeletal muscle (SM), subcutaneous, intramuscular (IMAT), visceral (VAT), and epicardial (EAT) adipose tissues were quantified between automatically-identified T5 and T11 vertebrae. The independent prognostic value of volumetric attenuation, and indexed volumes were evaluated for predicting ACM, adjusting for established risk factors and 18 other body compositions measures via Cox regression models and Kaplan-Meier curves.

Findings

End-to-end processing time was <2 minutes/scan with no user interaction. Of 9918 patients studied, 5451(55%) were male. During median 2.5 years follow-up, 610 (6.2%) patients died. High VAT, EAT and IMAT attenuation were associated with increased ACM risk (adjusted hazard ratio (HR) [95% confidence interval] for VAT: 2.39 [1.92, 2.96], p<0.0001; EAT: 1.55 [1.26, 1.90], p<0.0001; IMAT: 1.30 [1.06, 1.60], p=0.0124). Patients with high bone attenuation were at lower risk of death as compared to subjects with lower bone attenuation (adjusted HR 0.77 [0.62, 0.95], p=0.0159). Likewise, high SM volume index was associated with a lower risk of death (adjusted HR 0.56 [0.44, 0.71], p<0.0001).

Interpretations

CTAC scans obtained routinely during cardiac perfusion imaging contain important volumetric body composition biomarkers which can be automatically measured and offer important additional prognostic value.

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.

Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment

Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles play an important role in disease prognosis; as such, quantitative analysis of the respiratory muscles is crucial to assess the status of the respiratory system and the quality of life in patients. In this study, we aimed to develop an automated approach for the segmentation and classification of three types of respiratory muscles from computed tomography (CT) images using artificial intelligence. With a dataset of approximately 600,000 thoracic CT images from 3,200 individuals, we trained the model using the Attention U-Net architecture, optimized for detailed and focused segmentation. Subsequently, we calculated the volumes and densities from the muscle masks segmented by our model and performed correlation analysis with pulmonary function test (PFT) parameters. The segmentation models for muscle tissue and respiratory muscles obtained dice scores of 0.9823 and 0.9688, respectively. The classification model, achieving a generalized dice score of 0.9900, also demonstrated high accuracy in classifying thoracic region muscle types, as evidenced by its F1 scores: 0.9793 for the pectoralis muscle, 0.9975 for the erector spinae muscle, and 0.9839 for the intercostal muscle. In the correlation analysis, the volume of the respiratory muscles showed a strong correlation with PFT parameters, suggesting that respiratory muscle volume may serve as a potential novel biomarker for respiratory function. Although muscle density showed a weaker correlation with the PFT parameters, it has a potential significance in medical research.

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).

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.

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.

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.

Nutritional status and changes in muscle and adipose tissue determined by computed tomography as predictors of mortality in hospitalized patients

The aim of the present study was to investigate whether nutritional status and changes in muscle and adipose tissue determined by computed tomography are predictors of mortality in hospitalized patients. This was a prospective cohort study involving patients ≥ 20 years of age hospitalized in a public hospital. Sociodemographic and clinical variables were collected from electronic medical records. Nutritional variables were determined. All patients were followed prospectively until the hospital outcome, which could be discharge or death. Body composition was defined from computed tomography images, with the identification of myopenia, myosteatosis, and myopenic obesity. The sample comprised 542 patients. The mortality rate was 10.7% (95% CI: 6.4-15.0%). The independent predictors of mortality were malnutrition, assessed with the subjective global assessment (hazard ratio: 4.18; 95% CI: 1.01-17.22; p = 0.047), and myopenic obesity (hazard ratio: 2.82; 95% CI: 1.11-7.20; p = 0.029). The findings of the present study add to the limited evidence in the literature that body composition is associated with outcomes in hospitalized patients.

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.