CT-Derived Body Composition Assessment as a Prognostic Tool in Oncologic Patients: From Opportunistic Research to Artificial Intelligence-Based Clinical Implementation

Looking at or beyond the tumor - a systematic review and meta-analysis of quantitative imaging biomarkers predicting pancreatic cancer prognosis

OBJECTIVES

To evaluate the prognostic value of quantitative imaging biomarkers derived from computed tomography (CT) and magnetic resonance imaging (MRI) for pancreatic cancer (PC), with a particular focus on body composition parameters beyond the traditional intrinsic features of the tumor.

METHODS

PubMed, EMBASE, and Cochrane Library databases were searched for articles on quantitative imaging biomarkers obtained from CT or MRI in predicting PC prognosis published between January 2014 and August 2024. The Newcastle-Ottawa scale was used to assess the quality of the included studies. Survival outcomes, such as overall survival (OS) and recurrence-free survival (RFS), were evaluated. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a random-effects model. In case of high heterogeneity, subgroup analyses and sensitivity analyses were performed to identify potential sources of heterogeneity among the studies.

RESULTS

We performed a meta-analysis of ten imaging biomarkers investigated in 43 included studies. Larger tumor size, lower skeletal muscle radiodensity, lower skeletal muscle index (SMI), presence of sarcopenic obesity, lower psoas muscle index (PMI), higher visceral to subcutaneous adipose tissue area ratio, and lower visceral adipose tissue index were associated with significantly worse OS. In particular, lower SMI and lower PMI had relatively high HRs (1.65 for SMI, 95% CI 1.39-1.96, and 2.20 for PMI, 95% CI 1.74-2.78). Patients with lower SMI exhibited poorer RFS (HR 1.78, 95% CI 1.46-2.18). Subgroup analyses identified the origin region of the study and intervention type as potential factors of heterogeneity for SMI in predicting OS.

CONCLUSIONS

Imaging biomarkers indicating body composition at PC diagnosis may play an important role in predicting patient prognosis. Further prospective multi-center studies with large sample sizes are needed for validation and translation into clinical practice.

A Cloud-Based System for Automated AI Image Analysis and Reporting

Although numerous AI algorithms have been published, the relatively small number of algorithms used clinically is partly due to the difficulty of implementing AI seamlessly into the clinical workflow for radiologists and for their healthcare enterprise. The authors developed an AI orchestrator to facilitate the deployment and use of AI tools in a large multi-site university healthcare system and used it to conduct opportunistic screening for hepatic steatosis. During the 60-day study period, 991 abdominal CTs were processed at multiple different physical locations with an average turnaround time of 2.8 min. Quality control images and AI results were fully integrated into the existing clinical workflow. All input into and output from the server was in standardized data formats. The authors describe the methodology in detail; this framework can be adapted to integrate any clinical AI algorithm.

AI Applications for Thoracic Imaging: Considerations for Best Practice

Artificial intelligence (AI) technology is rapidly being introduced into thoracic radiology practice. Current representative use cases for AI in thoracic imaging show cumulative evidence of effectiveness. These include AI assistance for reading chest radiographs and low-dose (1.5-mSv) chest CT scans for lung cancer screening and triaging pulmonary embolism on chest CT scans. Other potential use cases are also under investigation, including filtering out normal chest radiographs, monitoring reading errors, and automated opportunistic screening of nontarget diseases. However, implementing AI tools in daily practice requires establishing practical strategies. Practical AI implementation will require objective on-site performance evaluation, institutional information technology infrastructure integration, and postdeployment monitoring. Meanwhile, the remaining challenges of adopting AI technology need to be addressed. These challenges include educating radiologists and radiology trainees, alleviating liability risk, and addressing potential disparities due to the uneven distribution of data and AI technology. Finally, next-generation AI technology represented by large language models (LLMs), including multimodal models, which can interpret both text and images, is expected to innovate the current landscape of AI in thoracic radiology practice. These LLMs offer opportunities ranging from generating text reports from images to explaining examination results to patients. However, these models require more research into their feasibility and efficacy.

Artificial intelligence measured 3D lumbosacral body composition and clinical outcomes in rectal cancer patients

INTRODUCTION

Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in patients using data from an entire three-dimensional (3D) region of the body. This study has utilized AI technology to measure BC from the entire lumbosacral (L1-S5) region and assessed the associations between BC and clinical outcomes in rectal cancer patients who have undergone neoadjuvant therapy followed by surgery.

METHODS

A retrospective, cross sectional analysis was performed on locally advanced rectal cancer (LARC) patients treated with neoadjuvant long-course chemoradiotherapy followed by curative resection with total mesorectal excision at a tertiary referral centre, Western Health, Melbourne, Australia. A pre-trained and validated in-house AI segmentation model was used to automatically segment and measure intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) and skeletal muscle (SM) from CT slices across the entire L1-S5 level of each patient. Multivariate analysis between patient BC and clinical outcomes was performed.

RESULTS

Two hundred and fourteen patients were included in the study. One hundred and fifty-one (70.6%) patients were male and 63 (29.4%) patients were female. The average age at diagnosis was 62.4 (±12.7) years. SM density, but not volume, was associated with better overall survival (OS) (HR 0.24, P = 0.029), recurrence-free survival (RFS) (HR 0.45, P = 0.048) and decreased length of stay (LoS) (HR 1.58, P = 0.036). Both IMAT volume (HR 0.13, P = 0.008) and density (HR 0.26, P = 0.006) were associated with better OS.

CONCLUSION

This study measured 3D BC from the entire lumbosacral region of rectal cancer patients. SM density was the most significant BC parameter, and was associated with improved OS, RFS and LoS. This adds to growing evidence that SM is a key component of BC in cancer patients and should be optimized prior to treatment. IMAT was also a prognostic factor, giving rise to avenues of future research into the role of adiposity on nutrition and tumour immunology.

Evaluation of a fully automated computed tomography image segmentation method for fast and accurate body composition measurements

INTRODUCTION

Body composition evaluation can be used to assess patients' nutritional status to predict clinical outcomes. To facilitate reliable and time-efficient body composition measurements eligible for clinical practice, fully automated computed tomography segmentation methods were developed. The aim of this study was to evaluate automated segmentation by Data Analysis Facilitation Suite in an independent dataset.

MATERIALS AND METHODS

Preoperative computed tomography images were used of 165 patients undergoing cytoreductive surgery with hyperthermic intraperitoneal chemotherapy from 2014 to 2019. Manual and automated measurements of skeletal muscle mass (SMM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and intramuscular adipose tissue (IMAT) were performed at the third lumbar vertebra. Segmentation accuracy of automated measurements was assessed using the Jaccard index and intra-class correlation coefficients.

RESULTS

Automatic segmentation provided accurate measurements compared to manual analysis, resulting in Jaccard score coefficients of 94.9 for SMM, 98.4 for VAT, 99.1 for SAT, and 79.4 for IMAT. Intra-class correlation coefficients ranged from 0.98 to 1.00. Automated measurements on average overestimated SMM and SAT areas compared to manual analysis, with mean differences (±2 standard deviations) of 1.10 (-1.91 to 4.11) and 1.61 (-2.26 to 5.48) respectively. For VAT and IMAT, automated measurements on average underestimated the areas with mean differences of -1.24 (-3.35 to 0.87) and -0.93 (-5.20 to 3.35), respectively.

CONCLUSIONS

Commercially available Data Analysis Facilitation Suite provides similar results compared to manual measurements of body composition at the level of third lumbar vertebra. This software provides accurate and time-efficient body composition measurements, which is necessary for implementation in clinical practice.

Automated chest CT three-dimensional quantification of body composition: adipose tissue and paravertebral muscle

This retrospective study developed an automated algorithm for 3D segmentation of adipose tissue and paravertebral muscle on chest CT using artificial intelligence (AI) and assessed its feasibility. The study included patients from the Boston Lung Cancer Study (2000-2011). For adipose tissue quantification, 77 patients were included, while 245 were used for muscle quantification. The data were split into training and test sets, with manual segmentation as the ground truth. Subcutaneous and visceral adipose tissues (SAT and VAT) were segmented separately. Muscle area, mean attenuation value, and intermuscular adipose tissue percentage (IMAT%) were calculated in the paravertebral muscle segmentation. The AI algorithm was trained on the training sets, and its performance was evaluated on the test sets. The AI achieved Dice scores above 0.87 and showed excellent correlations for VAT/SAT ratios, muscle attenuation value, and IMAT% (correlation coefficients > 0.98, p < 0.001). The mean differences between the AI and ground truth were minimal (VAT/SAT ratio: 0.7%; muscle attenuation value: 1 HU; IMAT%: <1%). In conclusion, we developed a feasible AI algorithm for automated 3D segmentation of adipose tissue and paravertebral muscle on chest CT.

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.

Radiomic-based biomarkers: Transforming age and body composition metrics into personalized age-informed indices

Chronological age has been the standard for quantifying the aging process. While it is simple to quantify it cannot fully discern the biological variability of aging between individuals. The growing body of interest in this variability of human aging has led to the introduction of new biomarkers to operationalize biological age. The inclusion of body composition may provide additional value to biological aging as a prediction and estimation factor of individual health outcomes. Diagnostic images based on radiomic techniques such as Computed Tomography contain an untapped wealth of patient-specific data that remain inaccessible to healthcare providers. These images are beneficial for collecting information from body composition that adds precision and granularity when compared to traditional measures. This information can subsequently be aggregated to construct models for changes in the human body associated with aging. In addition, aging leads to a natural decline in the best parameter of drug dosing in older adults, glomerular filtration rate. Since the conventional models of kidney function are correlated with age and body composition, the radiomic biomarkers representing age-related changes in body composition may also serve as potential new imaging biomarkers of kidney function for personalized dosing. Our review introduces potential radiomic biomarkers as measures of body composition change targeting the aging processes. As a functional example, we have hypothesized an age-related model of radiomics as a covariate of kidney function to improve personalized dosing. Future research focusing on evaluating this hypothesis in human subject studies is acknowledged.

Bone mineral density as a prognostic marker in patients with biliary tract cancer undergoing surgery

Background

Biliary tract cancer (BTC) is one of the most aggressive malignancies and surgery represents the only curative treatment approach. However, even in patients with complete tumor resection 5-year survival rates are below 30%. So far, prognostic markers to assess the outcome of these patients are lacking. We therefore evaluated bone mineral density (BMD) as a prognostic tool in patients receiving surgery for BTC.

methods

76 BTC patients undergoing tumor resection in our clinic (Duesseldorf cohort) as well as an external validation cohort of 34 BTC patients (Cologne cohort) were included. BMD was analyzed at the first lumbar vertebra, using routine CT scans which has been proven comparable to DXA.

Results

Median overall survival (OS) of the Duesseldorf cohort after surgery was 527 days, one- and five-year survival probabilities were 62 and 18%. Patients with BMD above 156.5 HU had significantly improved OS (1435 days vs. 459 days; p = 0.002). The prognostic value for BMD was confirmed using Cox-regression analysis, as well as an external validation cohort. In subgroup analysis the prognostic effect of BMD was only present in female patients, suggesting sex specific differences.

Conclusion

BMD is a valuable, easily accessible and independent prognostic marker in patients receiving liver surgery for BTC.

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.

The correlation of CT-derived muscle density, skeletal muscle index, and visceral adipose tissue with nutritional status in severely injured patients

BACKGROUND

This study explored if computerized tomography-derived body composition parameters (CT-BCPs) are related to malnutrition in severely injured patients admitted to the Intensive Care Unit (ICU).

METHODS

This prospective cohort study included severely injured (Injury Severity Score ≥ 16) patients, admitted to the ICU of three level-1 trauma centers between 2018 and 2022. Abdominal CT scans were retrospectively analyzed to assess the CT-BCPs: muscle density (MD), skeletal muscle index (SMI), and visceral adipose tissue (VAT). The Subjective Global Assessment was used to diagnose malnutrition at ICU admission and on day 5 of admission, and the modified Nutrition Risk in Critically ill at admission was used to assess the nutritional risk.

RESULTS

Seven (11%) of the 65 analyzed patients had malnutrition at ICU admission, increasing to 23 patients (35%) on day 5. Thirteen (20%) patients had high nutritional risk. CT-BCPs were not related to malnutrition at ICU admission and on day 5. Patients with high nutritional risk at admission had lower MD (median (IQR) 32.1 HU (25.8-43.3) vs. 46.9 HU (37.7-53.3); p < 0.01) and higher VAT (median 166.5 cm2 (80.6-342.6) vs. 92.0 cm2 (40.6-148.2); p = 0.01) than patients with low nutritional risk.

CONCLUSION

CT-BCPs do not seem related to malnutrition, but low MD and high VAT may be associated with high nutritional risk. These findings may prove beneficial for clinical practice, as they suggest that CT-derived parameters may provide valuable information on nutritional risk in severely injured patients, in addition to conventional nutritional assessment and screening tools.

LEVEL OF EVIDENCE

Level III, Prognostic/Epidemiological.

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.

Sarcopenia is independently associated with poor preoperative physical fitness in patients undergoing colorectal cancer surgery

BACKGROUND

Accurate preoperative risk assessment for major colorectal cancer (CRC) surgery remains challenging. Body composition (BC) and cardiopulmonary exercise testing (CPET) can be used to evaluate risk. The relationship between BC and CPET in patients undergoing curative CRC surgery is unclear.

METHODS

Consecutive patients undergoing CPET prior to CRC surgery between 2010 and 2020 were identified between two different UK hospitals. Body composition phenotypes such as sarcopenia, myosteatosis, and visceral obesity were defined using widely accepted thresholds using preoperative single axial slice CT image at L3 vertebrae. Relationships between clinicopathological, BC, and CPET variables were investigated using linear regression analysis.

RESULTS

Two hundred eighteen patients with stage I-III CRC were included. The prevalence of sarcopenia, myosteatosis, and visceral obesity was 62%, 33%, and 64%, respectively. The median oxygen uptake at anaerobic threshold (VO2 at AT) was 12.2 mL/kg/min (IQR 10.6-14.2), and oxygen uptake at peak exercise (VO2 peak) was 18.8 mL/kg/min (IQR 15.4-23). On univariate linear regression analysis, male sex (P < 0.001) was positively associated with VO2 at AT. While ASA grade (P < 0.001) and BMI (P = 0.007) were negatively associated with VO2 at AT, on multivariate linear regression analysis, these variables remained significant (P < 0.05). On univariate linear regression analysis, male sex (P < 0.001) was positively associated with VO2 peak, whereas age (P < 0.001), ASA grade (P < 0.001), BMI (P = 0.003), sarcopenia (P = 0.015), and myosteatosis (P < 0.001) were negatively associated with VO2 peak. On multivariate linear regression analysis age (P < 0.001), ASA grade (P < 0.001), BMI (P < 0.001), and sarcopenia (P = 0.006) were independently and negatively associated with VO2 peak.

CONCLUSIONS

The novel finding that sarcopenia is independently associated with reduced VO2 peak performance in CPET supports the supposition that reduced muscle mass relates to poor physical function in CRC patients. Further work should be undertaken to assess whether sarcopenia diagnosed on CT can act as suitable surrogate for CPET to further enhance personalized risk stratification.

The effects of sarcopenic obesity on immediate postoperative outcomes after pancreatoduodenectomy: a retrospective cohort study

Purpose

Several studies have evaluated the impact of sarcopenic obesity (SO) on postoperative complications, including postoperative pancreatic fistula (POPF), in patients undergoing pancreatoduodenectomy (PD). Previous studies have shown that SO increases POPF, but it remains unclear whether SO increases postoperative complications. In this study, we aimed to determine the relationship between SO and immediate postoperative complications.

Methods

From January 2005 to December 2019, the medical records of patients who underwent PD for periampullary cancer were retrospectively reviewed. Skeletal muscle index (SMI) and visceral fat area (VFA) were calculated from preoperative computed tomography images. Patients with high VFA were classified as obese, while those with low SMI were classified as sarcopenic. Patients were divided into 4 groups: normal group, sarcopenia only group, obesity only group, and SO group. Postoperative outcomes were compared between groups, and factors affecting postoperative complications were analyzed by multivariate analysis.

Results

Normal group (n = 176), sarcopenia only group (n = 130), obesity only group (n = 207), and SO group (n = 117) were analyzed retrospectively. SO group had significantly more frequent major complications compared to the normal group (P = 0.006), as well as a significantly more frequent clinically relevant POPF compared to the other groups (P = 0.002). In multivariate analysis, SO was an independent risk factor for major complications (P = 0.008) and clinically relevant POPF (P = 0.003).

Conclusion

SO is a factor associated with poor immediate postoperative outcomes after PD for periampullary cancer.

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.

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients

BACKGROUND

The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuvant therapy and potential organ preservation. Of particular interest are predictors of pathological complete response (pCR) that can guide personalized treatment. There are currently no clinical biomarkers which can accurately predict neoadjuvant therapy (NAT) response but body composition (BC) measures present as an emerging contender. The primary aim of the study was to determine if artificial intelligence (AI) derived body composition variables can predict pCR in patients with LARC.

METHODS

LARC patients who underwent NAT followed by surgery from 2012 to 2023 were identified from the Australian Comprehensive Cancer Outcomes and Research Database registry (ACCORD). A validated in-house pre-trained 3D AI model was used to measure body composition via computed tomography images of the entire Lumbar-3 vertebral level to produce a volumetric measurement of visceral fat (VF), subcutaneous fat (SCF) and skeletal muscle (SM). Multivariate analysis between patient body composition and histological outcomes was performed.

RESULTS

Of 214 LARC patients treated with NAT, 22.4% of patients achieved pCR. SM volume (P = 0.015) and age (P = 0.03) were positively associated with pCR in both male and female patients. SCF volume was associated with decreased likelihood of pCR (P = 0.059).

CONCLUSION

This is the first study in the literature utilizing AI-measured 3D Body composition in LARC patients to assess their impact on pathological response. SM volume and age were positive predictors of pCR disease in both male and female patients following NAT for LARC. Future studies investigating the impact of body composition on clinical outcomes and patients on other neoadjuvant regimens such as TNT are potential avenues for further research.

The Relationship between Clinical Frailty Score, CT-Derived Body Composition, Systemic Inflammation, and Survival in Patients with Chronic Limb-Threatening Ischemia

BACKGROUND

Frailty is a chronic condition with complex etiology and impaired functional performance that has been associated with altered body composition and chronic inflammation. Chronic limb-threatening ischemia (CLTI) carries significant morbidity and mortality and is associated with poor quality of life. The present study aims to examine these relationships and their prognostic value in patients with CLTI.

METHODS

Consecutive patients presenting as unscheduled admissions to a single tertiary center with CLTI were included over a 12-month period. Frailty was diagnosed using the Clinical Frailty Scale (CFS). Body composition was assessed using computerised tomography (CT) at the L3 vertebral level (CT-BC) to generate visceral and subcutaneous fat indices, skeletal muscle index, and skeletal muscle density. Skeletal muscle index and skeletal muscle density were combined to form the CT-sarcopenia score (CT-SS). Systemic inflammation was assessed by the modified Glasgow prognostic score (mGPS). The primary outcome was overall mortality.

RESULTS

There were 190 patients included with a median (interquartile range) follow-up of 22 (6) months (range 15-32 months) and 79 deaths during the follow-up period. One hundred patients (53%) had a CFS >4. CFS >4 (hazard ratio [HR] 2.14, 95% confidence interval [CI] 1.25-3.66, P < 0.01), CT-SS (HR 1.47, 95% CI 1.03-2.09, P < 0.05), and mGPS (HR 1.54, 95% CI 1.11-2.13, P < 0.01) were independently associated with increased mortality. CT-SS (odds ratio 1.88, 95% CI 1.09-3.24, P < 0.01) was independently associated with CFS >4. Patients with CT-SS 0 and CFS ≤4 had 90% (standard error [SE] 5%) 1-year survival, compared with 35% (SE 9%) in patients with CT-SS 2 and CFS >4 (P < 0.001). Patients with mGPS 0 and CFS ≤4 had 94% (SE 4%) 1-year survival compared with 44% (SE 6%) in the mGPS 2 and CFS >4 subgroup (P < 0.001).

CONCLUSIONS

Frailty assessed by CFS was associated with CT-BC. CFS, CT-SS, and mGPS were associated with poorer survival in patients presenting as unscheduled admissions with CLTI. CT-SS and mGPS may contribute to part of frailty and prognostic assessment in this patient cohort.

Body composition assessment with ultrasound muscle measurement: optimization through the use of semi-automated tools in colorectal cancer

Colorectal cancer (CRC) is a disease with a high prevalence and major impact on global health. Body composition (BC) data are of great importance in the assessment of nutritional status. Ultrasound (US) is an emerging, accessible and non-invasive technique that could be an alternative when it is not feasible to perform computed tomography (CT). The aim of this study is to evaluate the correlation between CT, as a reference technique, and US of the rectus femoris (RF) as a "proof of concept," in a cohort of patients with CRC and assess the optimisation of results obtained by US when performed by our new semi-automated tool. A single-centre cross-sectional study including 174 patients diagnosed with CRC and undergoing surgery was carried out at the Vall d'Hebron Hospital. We found a strong correlation between CT and US of the RF area (r = 0.67; p < 0.005). The latter, is able to discriminate patients with worse prognosis in terms of length of hospital stay and discharge destination (AUC-ROC = 0.64, p 0.015). These results improve when they are carried out with the automatic tool (area AUC-ROC = 0.73, p 0.023), especially when normalised by height and eliminating patients who associate overflow. According to our results, the US could be considered as a valuable alternative for the quantitative assessment of muscle mass when CT is not feasible. These measurements are improved when measuring software is applied, such as "Bat" software.

Body composition radiomic features as a predictor of survival in patients with non-small cellular lung carcinoma: A multicenter retrospective study

OBJECTIVES

This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

METHODS

The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms.

RESULTS

The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05).

CONCLUSION

Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.

Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach

PURPOSE

Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans. We developed a deep learning (DL) model for fully automatic BC quantification on routine staging CTs and determined its prognostic role in a clinical cohort of patients with GEAC.

MATERIALS AND METHODS

We developed and tested a DL model to quantify BC measures defined as subcutaneous and visceral adipose tissue (VAT) and skeletal muscle on routine CT and investigated their prognostic value in a cohort of patients with GEAC using baseline, 3-6-month, and 6-12-month postoperative CTs. Primary outcome was all-cause mortality, and secondary outcome was disease-free survival (DFS). Cox regression assessed the association between (1) BC at baseline and mortality and (2) the decrease in BC between baseline and follow-up scans and mortality/DFS.

RESULTS

Model performance was high with Dice coefficients ≥0.94 ± 0.06. Among 299 patients with GEAC (age 63.0 ± 10.7 years; 19.4% female), 140 deaths (47%) occurred over a median follow-up of 31.3 months. At baseline, no BC measure was associated with DFS. Only a substantial decrease in VAT >70% after a 6- to 12-month follow-up was associated with mortality (hazard ratio [HR], 1.99 [95% CI, 1.18 to 3.34]; P = .009) and DFS (HR, 1.73 [95% CI, 1.01 to 2.95]; P = .045) independent of age, sex, BMI, Union for International Cancer Control stage, histologic grading, resection status, neoadjuvant therapy, and time between surgery and follow-up CT.

CONCLUSION

DL enables opportunistic estimation of BC from routine staging CT to quantify prognostic information. In patients with GEAC, only a substantial decrease of VAT 6-12 months postsurgery was an independent predictor for DFS beyond traditional risk factors, which may help to identify individuals at high risk who go otherwise unnoticed.

Bridging gaps in cancer cachexia Care: Current insights and future perspectives

Cachexia is characterized by severe weight loss and skeletal muscle depletion, and is a threat to cancer patients by worsening their prognosis. International guidelines set indications for the screening and diagnosis of cancer cachexia and suggest interventions (nutritional support, physical exercise, and pharmacological treatments). Nevertheless, real-life experience not always aligns with such indications. We aimed to review the current state of the field and the main advancements, with a focus on real-life clinical practice from the perspectives of oncologists, nutrition professionals, and radiologists. Pragmatic solutions are proposed to improve the current management of the disease, emphasizing the importance of increasing awareness of clinical nutrition's benefits, fostering multidisciplinary collaboration, promoting early identification of at-risk patients, and leveraging available resources. Given the distinct needs of patients who are receiving oncologic anti-cancer treatments and those in the follow-up phase, the use of tailored approaches is encouraged. The pivotal role of healthcare professionals in managing patients in active treatment is highlighted, while patient and caregiver empowerment should be strengthened in the follow-up phase. Telemedicine and web-based applications represent valuable tools for continuous monitoring of patients, facilitating timely and personalized intervention through effective communication between patients and healthcare providers. These actions can potentially improve the outcomes, well-being, and survival of cancer patients with cachexia.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients

OBJECTIVE

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS).

METHODS

Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated.

RESULTS

Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms.

CONCLUSION

CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Prognostic Value of Body Composition and Systemic Inflammatory Markers in Patients with Locally Advanced Cervical Cancer Following Chemoradiotherapy

Background

Abnormal changes in body composition and systemic inflammation response have been associated with poor survival of cancer patients. Our study was to explore the prognostic value of the association between body composition indicators and systemic inflammation markers among patients with locally advanced cervical cancer (LACC) who underwent concurrent chemoradiotherapy (CCRT).

Methods

We retrospectively reviewed medical records of LACC patients treated between 2016 and 2019. Subcutaneous, visceral and intra-muscular adipose index (SAI, VAI and IMAI) and skeletal muscle index (SMI) were derived from computed tomography (CT). Kaplan-Meier analysis and Univariate and multivariate Cox analyses were used to evaluate the survival. A nomogram was constructed to assess the prognostic value.

Results

The study included 196 patients treated with CCRT. According to multivariable Cox analyses, IIIC1r (P = 0.045), high systemic immune-inflammation index (SII) (P = 0.004), sarcopenia (P = 0.008), high SAI (P = 0.016) and high VAI (P = 0.001) were significantly risk factors for overall survival (OS). Kaplan-Meier analysis showed that patients with low lymphocyte-to-monocyte ratio (LMR) and sarcopenia had longer OS than those with high LMR and sarcopenia (P = 0.023). The high neutrophil-to-lymphocyte ratio (NLR) in non-sarcopenic patients showed better survival (P = 0.022). Low VAI (P = 0.019) or low IMAI (P = 0.019) combined with low SII had a favorable OS. Low LMR combined with low SAI was associated with longer OS (P = 0.022). The calibration plots of nomogram predicting the 3-year and 5-year OS rates were close to the ideal models.

Conclusion

Inflammation factors were closely associated with abnormal muscle and fat distribution. The combined prognostic value of body composition indicators and systemic inflammation markers was reliable in predicting survival for LACC patients.

A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis

Automated computed tomography (CT) scan segmentation (labelling of pixels according to tissue type) is now possible. This technique is being adapted to achieve three-dimensional (3D) segmentation of CT scans, opposed to single L3-slice alone. This systematic review evaluates feasibility and accuracy of automated segmentation of 3D CT scans for volumetric body composition (BC) analysis, as well as current limitations and pitfalls clinicians and researchers should be aware of. OVID Medline, Embase and grey literature databases up to October 2021 were searched. Original studies investigating automated skeletal muscle, visceral and subcutaneous AT segmentation from CT were included. Seven of the 92 studies met inclusion criteria. Variation existed in expertise and numbers of humans performing ground-truth segmentations used to train algorithms. There was heterogeneity in patient characteristics, pathology and CT phases that segmentation algorithms were developed upon. Reporting of anatomical CT coverage varied, with confusing terminology. Six studies covered volumetric regional slabs rather than the whole body. One study stated the use of whole-body CT, but it was not clear whether this truly meant head-to-fingertip-to-toe. Two studies used conventional computer algorithms. The latter five used deep learning (DL), an artificial intelligence technique where algorithms are similarly organized to brain neuronal pathways. Six of seven reported excellent segmentation performance (Dice similarity coefficients > 0.9 per tissue). Internal testing on unseen scans was performed for only four of seven algorithms, whilst only three were tested externally. Trained DL algorithms achieved full CT segmentation in 12 to 75 s versus 25 min for non-DL techniques. DL enables opportunistic, rapid and automated volumetric BC analysis of CT performed for clinical indications. However, most CT scans do not cover head-to-fingertip-to-toe; further research must validate using common CT regions to estimate true whole-body BC, with direct comparison to single lumbar slice. Due to successes of DL, we expect progressive numbers of algorithms to materialize in addition to the seven discussed in this paper. Researchers and clinicians in the field of BC must therefore be aware of pitfalls. High Dice similarity coefficients do not inform the degree to which BC tissues may be under- or overestimated and nor does it inform on algorithm precision. Consensus is needed to define accuracy and precision standards for ground-truth labelling. Creation of a large international, multicentre common CT dataset with BC ground-truth labels from multiple experts could be a robust solution.

Management Algorithm for Osseous Metastatic Disease: What the Treatment Teams Want to Know

Radiologists play a primary role in identifying, characterizing, and classifying spinal metastases and can play a lifesaving role in the care of these patients by triaging those with instability to urgent spine surgery consultation. For this reason, an understanding of current treatment algorithms and principles of spinal stability in patients with cancer is vital for all who interpret spine studies. In addition, advances in imaging allow radiologists to provide more accurate diagnoses and characterize pathology, thereby improving patient safety.

The Impact of NUTRItional Status at First Medical Oncology Visit on Clinical Outcomes: The NUTRIONCO Study

Malnutrition affects up to 75% of cancer patients and results from a combination of anorexia and metabolic dysregulation. Metabolic and nutritional abnormalities in cancer patients can lead to cachexia, a multifactorial syndrome characterized by involuntary loss of skeletal muscle mass, systemic inflammation and increased protein catabolism. Cancer cachexia negatively affects patients' outcomes, response to anticancer treatments, quality of life, and survival. However, risk of malnutrition, and cachexia are still under-recognized in cancer patients. The Prevalence of Malnutrition in Oncology (PreMiO) study revealed that 51% of patients already had nutritional deficiencies at their first medical oncology visit. Here, we report the results of the subsequent retrospective, observational NUTRItional status at first medical oncology visit ON Clinical Outcomes (NUTRIONCO) study, aimed at assessing the impact of baseline nutritional and non-nutritional variables collected in the PreMiO study on the clinical outcomes of the same patients followed up from August 2019 to October 2021. We have highlighted a statistically significant association between baseline variables and patient death, rehospitalization, treatment toxicity, and disease progression at follow-up. We found a higher overall survival probability in the well-nourished general study population vs. malnourished patients (p < 0.001). Of major interest is the fact that patient stratification revealed that malnutrition decreased survival probability in non-metastatic patients but not in metastatic patients (p < 0.001). Multivariate analysis confirmed that baseline malnutrition (p = 0.004) and VAS score for appetite loss (p = 0.0104), in addition to albumin < 35 g/L (p < 0.0001) and neutrophil/lymphocyte ratio > 3 (p = 0.0007), were independently associated with the death of non-metastatic patients at follow-up. These findings highlight the importance of proactive, early management of malnutrition and cachexia in cancer patients, and in particular, in non-metastatic patients, from the perspective of a substantial improvement of their clinical outcomes.

Imaging Techniques to Determine Degree of Sarcopenia and Systemic Inflammation in Advanced Renal Cell Carcinoma

PURPOSE OF REVIEW

The purpose of this review is to provide an up-to-date understanding regarding the literature on sarcopenia and inflammation as prognostic factors in the context of renal cell carcinoma (RCC).

RECENT FINDINGS

Sarcopenia is increasingly recognized as a prognostic factor in RCC. Emerging literature suggests monitoring quantity of muscle on successive imaging and examining muscle density may be additionally informative. Inflammation has prognostic ability in RCC and is also considered a key contributor to development and progression of both RCC and sarcopenia. Recent studies suggest these two prognostic factors together may provide additional prognostic ability when used in combination. Ongoing developments include quality control regarding sarcopenia research and imaging, improving understanding of muscle loss mechanisms, and enhancing clinical incorporation of sarcopenia via improving imaging analysis practicality (i.e., artificial intelligence) and feasible biomarkers. Sarcopenia and systemic inflammation are complementary prognostic factors for adverse outcomes in patients with RCC. Further study on high-quality sarcopenia assessment standardization and expedited sarcopenia assessment is desired for eventual routine clinical incorporation of these prognostic factors.

CT-Derived Body Composition Is a Predictor of Survival after Esophagectomy

BACKGROUND

Body composition can be accurately quantified based on computed tomography (CT) and typically reflects an individual's overall health status. However, there is a dearth of research examining the relationship between body composition and survival following esophagectomy.

METHODS

We created a cohort consisting of 183 patients who underwent esophagectomy for esophageal cancer without neoadjuvant therapy. The cohort included preoperative PET-CT scans, along with pathologic and clinical data, which were collected prospectively. Radiomic, tumor, PET, and body composition features were automatically extracted from the images. Cox regression models were utilized to identify variables associated with survival. Logistic regression and machine learning models were developed to predict one-, three-, and five-year survival rates. Model performance was evaluated based on the area under the receiver operating characteristics curve (ROC/AUC). To test for the statistical significance of the impact of body composition on survival, body composition features were excluded for the best-performing models, and the DeLong test was used.

RESULTS

The one-year survival model contained 10 variables, including three body composition variables (bone mass, bone density, and visceral adipose tissue (VAT) density), and demonstrated an AUC of 0.817 (95% CI: 0.738-0.897). The three-year survival model incorporated 14 variables, including three body composition variables (intermuscular adipose tissue (IMAT) volume, IMAT mass, and bone mass), with an AUC of 0.693 (95% CI: 0.594-0.792). For the five-year survival model, 10 variables were included, of which two were body composition variables (intramuscular adipose tissue (IMAT) volume and visceral adipose tissue (VAT) mass), with an AUC of 0.861 (95% CI: 0.783-0.938). The one- and five-year survival models exhibited significantly inferior performance when body composition features were not incorporated.

CONCLUSIONS

Body composition features derived from preoperative CT scans should be considered when predicting survival following esophagectomy.

Fully automated CT-based adiposity assessment: comparison of the L1 and L3 vertebral levels for opportunistic prediction

PURPOSE

The purpose of this study is to compare fully automated CT-based measures of adipose tissue at the L1 level versus the standard L3 level for predicting mortality, which would allow for use at both chest (L1) and abdominal (L3) CT.

METHODS

This retrospective study of 9066 asymptomatic adults (mean age, 57.1 ± 7.8 [SD] years; 4020 men, 5046 women) undergoing unenhanced low-dose abdominal CT for colorectal cancer screening. A previously validated artificial intelligence (AI) tool was used to assess cross-sectional visceral and subcutaneous adipose tissue areas (SAT and VAT), as well as their ratio (VSR) at the L1 and L3 levels. Post-CT survival prediction was compared using area under the ROC curve (ROC AUC) and hazard ratios (HRs).

RESULTS

Median clinical follow-up interval after CT was 8.8 years (interquartile range, 5.2-11.6 years), during which 5.9% died (532/9066). No significant difference (p > 0.05) for mortality was observed between L1 and L3 VAT and SAT at 10-year ROC AUC. However, L3 measures were significantly better for VSR at 10-year AUC (p < 0.001). HRs comparing worst-to-best quartiles for mortality at L1 vs. L3 were 2.12 (95% CI, 1.65-2.72) and 2.22 (1.74-2.83) for VAT; 1.20 (0.95-1.52) and 1.16 (0.92-1.46) for SAT; and 2.26 (1.7-2.93) and 3.05 (2.32-4.01) for VSR. In women, the corresponding HRs for VSR were 2.58 (1.80-3.69) (L1) and 4.49 (2.98-6.78) (L3).

CONCLUSION

Automated CT-based measures of visceral fat (VAT and VSR) at L1 are predictive of survival, although overall measures of adiposity at L1 level are somewhat inferior to the standard L3-level measures. Utilizing predictive L1-level fat measures could expand opportunistic screening to chest CT imaging.