Computed tomography-based body composition profile as a screening tool for geriatric frailty detection

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.

A systematic review of the relationship between body composition including muscle, fat, bone, and body water and frailty in Asian residents

International guidelines suggested that overweight and underweight are risk factors for frailty. However, body composition, which directly affects body weight, was not mentioned as a risk factor. We aimed to investigate whether the body composition, including muscle, fat, bone, and body water, is a risk factor for frailty. MEDLINE, Cumulative Index to Nursing and Allied Health Literature, and Scopus were searched up to June 03, 2022. We included cohort studies or observational studies using a cross-sectional design that reported an association between body composition and frailty. Two reviewers assessed the quality of the included cohort studies. Furthermore, we examined whether body composition as a risk factor for frailty varies depending on the participant's place of residence. Of the 3871 retrieved studies, 77 were ultimately included, 7 of which were cohort studies. The risk-of-bias evaluation in each cohort study showed that all studies had at least one concern. Low lean mass, waist circumference-defined abdominal obesity, and bone mineral density were significantly associated with frailty in the cohort studies. The results of bone mineral density were conflicted in the cross-sectional studies. Considering the participants' place of residence, a significant association between lower-extremity muscle mass and frailty was demonstrated, particularly among Asian residents. Low lean mass and abdominal obesity were likely risk factors for frailty. These results could be useful for developing frailty prevention strategies and could have a positive impact on individual health management. Further, future studies are needed because body composition affecting frailty may differ by race.

Opportunistic Assessment for Parathyroid Adenoma on CT: A Retrospective Cohort Study Evaluating Primary Hyperparathyroidism-Associated Morbidity Over 10 Years of Follow-Up

BACKGROUND. Primary hyperparathyroidism (PHPT) is underdiagnosed. Opportunistic imaging-based parathyroid gland assessment is a proposed strategy for identifying patients at increased risk of undiagnosed PHPT. However, whether this approach is likely to identify individuals with clinically significant disease is unknown. OBJECTIVE. This study's objective was to assess for associations of the presence of an enlarged parathyroid gland on contrast-enhanced CT with clinical outcomes causally related to PHPT. METHODS. This retrospective cohort study included patients 18 years old or older with at least one contrast-enhanced chest or neck CT examination performed from January 2012 to December 2012, at least one noncontrast CT examination covering the chest or neck region without a date restriction, and at least one clinical encounter in the health system from January 2022 to December 2022. A neuroradiologist reviewed the CT examinations to determine the presence versus absence of an enlarged parathyroid gland on the 2012 study. Patient demographics, serum calcium results, and diagnosis codes for clinical outcomes causally related to PHPT were extracted from the EHR. Calcium results and diagnosis codes were classified as preexisting if documented before and as incident if documented after the 2012 contrast-enhanced CT examination. RESULTS. The cohort included 1198 patients (593 men and 605 women; mean age, 51.6 years), of whom 43 (3.6%) were assessed as having an enlarged parathyroid gland on the 2012 contrast-enhanced CT examination. PHPT was diagnosed in 16.3% of patients with, versus 0.3% of patients without, an enlarged parathyroid gland (p < .001). After adjustment for age, sex, race, and ethnicity, the presence of an enlarged parathyroid gland on contrast-enhanced CT was associated with significantly increased odds of preexisting nephrolithiasis (OR = 2.71; p = .03), hypercalcemia (OR = 5.30; p < .001), and PHPT (OR = 12.59; p = .008) as well as increased odds of incident osteopenia or osteoporosis (OR = 2.78; p = .008), nephrolithiasis (OR = 4.95; p < .001), hypercalcemia (OR = 7.58; p < .001), and PHPT (OR = 148.01; p < .001). CONCLUSION. An enlarged parathyroid gland indicated increased risk of PHPT as well as increased risk of preexisting and incident clinical conditions causally related to PHPT. CLINICAL IMPACT. Opportunistic CT-based assessment is a promising strategy for identifying patients at increased risk of undiagnosed PHPT; such assessment could potentially prevent some PHPT-related complications through earlier diagnosis and treatment.

The Feasibility and Validity of Sarcopenia Assessment Using Standard of Care Stroke Imaging

INTRODUCTION

Sarcopenia, an age-related syndrome defined by low muscularity, loss of muscle strength, and performance, is increasingly recognized as a potential contributor to disability following acute stroke. It is challenging to assess functionally in the acute post-stroke setting. Radiological assessment of skeletal musculature using standard of care CT neck imaging has recently been described. We sought to determine its feasibility and explore associations between CT-defined sarcopenia, validated frailty and functional indices and outcome at 18 months.

METHODS

Imaging and clinical data from a prospective cohort study were used. Frailty and functional indices were collected, including the NIH Stroke Scale, Barthel Index for Activities of Daily Living, Fried frailty phenotype, Lawton Instrumental Activities of Daily Living (IADL) Scale, the Frail Non-Disabled (FiND) Questionnaire and pre-stroke modified Rankin Scale. Single transverse slices of neck CT angiograms obtained at the time of acute stroke diagnosis were assessed for skeletal muscle area using ImageJ software; a skeletal muscle index (SMI) was calculated. The relationship between sarcopenia, frailty and functional indices and death or disability at 18 months was assessed using binary logistic regression.

RESULTS

Of 86 potentially eligible patients, 73 were included. It was possible to perform skeletal muscle analysis on the CT scans of all included patients. SMI and functional or frailty indices were not closely correlated. SMI alone was independently related to death or disability at 18 months. The addition of SMI to the abbreviated FiND score appeared to strengthen its associations and prognostic value.

CONCLUSION

This study demonstrates initial feasibility of CT-based skeletal muscle assessment in patients with acute stroke. The relationships with functional and frailty measures as well as short term outcomes including the ability to execute activities of daily living are required to be explored and validated in a larger, external cohort.

Older Tissue Age Derived From Abdominal Computed Tomography Biomarkers of Muscle, Fat, and Bone Is Associated With Chronic Conditions and Higher Mortality

OBJECTIVE

To determine whether body composition derived from medical imaging may be useful for assessing biologic age at the tissue level because people of the same chronologic age may vary with respect to their biologic age.

METHODS

We identified an age- and sex-stratified cohort of 4900 persons with an abdominal computed tomography scan from January 1, 2010, to December 31, 2020, who were 20 to 89 years old and representative of the general population in Southeast Minnesota and West Central Wisconsin. We constructed a model for estimating tissue age that included 6 body composition biomarkers calculated from abdominal computed tomography using a previously validated deep learning model.

RESULTS

Older tissue age associated with intermediate subcutaneous fat area, higher visceral fat area, lower muscle area, lower muscle density, higher bone area, and lower bone density. A tissue age older than chronologic age was associated with chronic conditions that result in reduced physical fitness (including chronic obstructive pulmonary disease, arthritis, cardiovascular disease, and behavioral disorders). Furthermore, a tissue age older than chronologic age was associated with an increased risk of death (hazard ratio, 1.56; 95% CI, 1.33 to 1.84) that was independent of demographic characteristics, county of residency, education, body mass index, and baseline chronic conditions.

CONCLUSION

Imaging-based body composition measures may be useful in understanding the biologic processes underlying accelerated aging.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults

RATIONALE AND OBJECTIVES

Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty.

MATERIALS AND METHODS

Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters.

RESULTS

The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (β = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (β = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty.

CONCLUSION

We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

Dissecting the Contributing Role of Divergent Adipose Tissue to Multidimensional Frailty in Cirrhosis

BACKGROUND AND AIMS

Emerging evidence has demonstrated that abnormal body composition may potentiate the development of frailty, whereas little work focuses on the role of divergent adipose tissue. Therefore, we aimed to determine the potential contribution of adipose tissue distribution to multidimensional frailty in decompensated cirrhosis.

METHODS

We conducted a retrospective cohort study. Divergent adipose tissues were assessed by computed tomography-derived subcutaneous adipose tissue index (SATI), visceral adipose tissue index (VATI) and total adipose tissue index (TATI), respectively. Frailty was identified by our validated self-reported Frailty Index. Multiple binary logistic models incorporating different covariates were established to assess the relationship between adipose tissue distribution and frailty.

RESULTS

The study cohort comprised 245 cirrhotic patients with 45.3% being male. The median Frailty Index, body mass index (BMI) and model for end-stage liver disease (MELD) score were 0.11, 24.3 kg/m2 and 8.9 points, respectively. In both men and women, patients who were frail exhibited lower levels of SATI in comparison with nonfrail patients. SATI inversely correlated with Frailty Index in the entire cohort (rs=-0.1361, p=0.0332). Furthermore, SATI or TATI was independently associated with frail phenotype in several multiple logistic regression models adjusting for age, BMI, presence of ascites, sodium, Child-Pugh class or MELD score in isolation.

CONCLUSIONS

In the context of decompensated cirrhosis, low SATI and concomitant TATI were associated with higher risk of being frail. These findings highlight the importance to further apply tissue-specific tools of body composition in place of crude metric like BMI.

A role for artificial intelligence in molecular imaging of infection and inflammation

The detection of occult infections and low-grade inflammation in clinical practice remains challenging and much depending on readers’ expertise. Although molecular imaging, like [¹⁸F]FDG PET or radiolabeled leukocyte scintigraphy, offers quantitative and reproducible whole body data on inflammatory responses its interpretation is limited to visual analysis. This often leads to delayed diagnosis and treatment, as well as untapped areas of potential application. Artificial intelligence (AI) offers innovative approaches to mine the wealth of imaging data and has led to disruptive breakthroughs in other medical domains already. Here, we discuss how AI-based tools can improve the detection sensitivity of molecular imaging in infection and inflammation but also how AI might push the data analysis beyond current application toward predicting outcome and long-term risk assessment.