Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults

Prognostic impact of adipose tissue loss at 1 month after surgery in patients with gastric cancer

BACKGROUND

The postoperative impact of short-term changes in skeletal muscle loss (SML) and adipose tissue loss (ATL) on treatment outcomes is unclear in patients with gastric cancer (GC). We investigate the role of SML and ATL at 1 month after surgery in determining postoperative survival and recurrence rates in patients with GC.

METHODS

We analyzed 540 patients with GC and assessed their skeletal muscle mass, visceral fat mass, and subcutaneous fat mass using computed tomography scans preoperatively and 1 month postoperatively. Patients were categorized into high and low groups based on their levels of SML, visceral ATL (v-ATL), and subcutaneous ATL (s-ATL). Additionally, they were classified into three groups (high ATL, intermediate ATL, and low ATL) based on their v-ATL and s-ATL measurements.

RESULTS

Patients with higher v-ATL and s-ATL had lower overall survival (OS) and recurrence-free survival (RFS) rates. High ATL was an independent prognostic factor for decreased OS (hazard ratio [HR] 2.27; 95% confidence interval [CI] 1.16-4.42; and P = 0.02) and RFS (HR 2.51; 95% CI 1.34-4.71; and P = 0.004) rates.

CONCLUSION

A reduction in adipose tissue volume shortly after surgery (1 month) could potentially indicate an increased risk of recurrence and mortality in patients with GC.

Prognostic value of computed tomography‑derived skeletal muscle index and radiodensity in patients with gastric cancer after curative gastrectomy

The association of computed tomography (CT)-derived skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD) with postoperative prognosis in patients with gastric cancer (GC) remains unknown. Therefore, the present study aimed to assess the association between SMI and SMD with 5-year overall survival (OS) and recurrence-free survival (RFS) in patients with GC. SMI and SMD were measured preoperatively in patients who underwent gastrectomy. Patients were categorized into Groups 1 (high SMI and SMD), 2 (high SMI or SMD) and 3 (low SMI and SMD). OS and RFS rates were assessed using Kaplan-Meier analysis and the log-rank test. Among 459 patients, OS and RFS rates were significantly lower in the low-SMD group than in the high-SMD group (OS, 83.4% vs. 88.8%, respectively; P=0.04 and RFS, 80.5% vs. 87.2%, respectively; P=0.02). OS and RFS rates were also significantly lower in Group 3 than in Groups 2 and 1 (P=0.006). Multivariate analysis revealed that a low SMI and SMD (Group 3) was a significant independent prognostic factor for OS [hazard ratio (HR), 2.32; 95% confidence interval (CI), 1.17-4.59; P=0.016] and RFS (HR, 2.28; 95% CI, 1.19-4.37; P=0.013). In summary, low SMI and SMD values may be useful postoperative prognostic indicators for patients with GC.

AI-based fully automatic image analysis: Optimal abdominal and thoracic segmentation volumes for estimating total muscle volume on computed tomography scans

Objectives

Evaluation of sarcopenia from computed tomography (CT) is often based on measuring skeletal muscle area on a single transverse slice. Automatic segmentation of muscle volume has a lower variance and may be a better proxy for the total muscle volume than single-slice areas. The aim of the study was to determine which abdominal and thoracic anatomical volumes were best at predicting the total muscle volume.

Methods

A cloud-based artificial intelligence tool (recomia.org) was used to segment all skeletal muscle of the torso of 994 patients who had performed whole-torso CT 2008-2020 for various clinical indications. Linear regression models for several anatomical volumes and single-slice areas were compared with regard to predicting the total torso muscle volume.

Results

The muscle volume from the tip of the coccyx and 25 cm cranially was the best of the abdominal volumes and was significantly better than the L3 slice muscle area (R2 0.935 vs 0.830, P < 0.0001). For thoracic volumes, the muscle volume between the top of the sternum to the lower bound of the Th12 vertebra showed the best correlation with the total volume, significantly better than the Th12 slice muscle area (R2 0.892 vs 0.775, P < 0.0001). Adjusting for body height improved the correlation slightly for all measurements but did not significantly change the ordering.

Conclusions

We identified muscle volumes that can be reliably segmented by automated image analysis which is superior to single slice areas in predicting total muscle volume.

Association of computed tomography scan-assessed body composition with immune and PI3K/AKT pathway proteins in distinct breast cancer tumor components

This hypothesis-generating study aims to examine the extent to which computed tomography-assessed body composition phenotypes are associated with immune and PI3K/AKT signaling pathways in breast tumors. A total of 52 patients with newly diagnosed breast cancer were classified into four body composition types: adequate (lowest two tertiles of total adipose tissue [TAT]) and highest two tertiles of total skeletal muscle [TSM] areas); high adiposity (highest tertile of TAT and highest two tertiles of TSM); low muscle (lowest tertile of TSM and lowest two tertiles of TAT); and high adiposity with low muscle (highest tertile of TAT and lowest tertile of TSM). Immune and PI3K/AKT pathway proteins were profiled in tumor epithelium and the leukocyte-enriched stromal microenvironment using GeoMx (NanoString). Linear mixed models were used to compare log2-transformed protein levels. Compared with the normal type, the low muscle type was associated with higher expression of INPP4B (log2-fold change = 1.14, p = 0.0003, false discovery rate = 0.028). Other significant associations included low muscle type with increased CTLA4 and decreased pan-AKT expression in tumor epithelium, and high adiposity with increased CD3, CD8, CD20, and CD45RO expression in stroma (P<0.05; false discovery rate >0.2). With confirmation, body composition can be associated with signaling pathways in distinct components of breast tumors, highlighting the potential utility of body composition in informing tumor biology and therapy efficacies.

Effect of Sarcopenic Obesity on Weight Loss Outcomes and Quality of Life after Laparoscopic Sleeve Gastrectomy: A Retrospective Cohort Study

BACKGROUND

Sarcopenic obesity may affect the health outcome of people with obesity after laparoscopic sleeve gastrectomy (LSG). To assess the impact of sarcopenic obesity (SO) on weight loss outcomes and improvement of quality of life after LSG.

MATERIALS AND METHODS

This observational study included patients who underwent LSG with SO (99 patients) or without SO (146 patients) from a single center. The primary endpoint was weight loss and disease-specific quality of life in patients with or without SO after the operation. Fat-free mass (FFM) and fat mass (FM) were calculated based on the L3-level images of preoperative CT scans. SO was diagnosed if FM/FFM ≥ 0.80.

RESULTS

Operative time and postoperative hospital stay days were longer in the SO group (p < 0.001). After LSG, weight, BMI, and EBMI were significantly lower in the NSO group than in the SO group (all P < 0.05), while %EWL and the number of patients with %EWL ≥ 100% were significantly lower in the SO group (both p < 0.05). The total BAROS scores of patients in the NSO group were higher than those in the SO group (p < 0.05). Additionally, the MA II questionnaire assessment showed a lower percentage of "very good" and "good" outcomes in the SO group (p < 0.05).

CONCLUSIONS

Patients with SO take a slower rate, longer time to reach the ideal weight, and lower quality of life self-ratings than NSO patients after LSG. Thus, preoperative evaluation and tailoring rehabilitation guidance for people with SO should be accounted.

Metabolic score tool for personalized acute pancreatitis prognosis: A multicenter analysis

Increasing evidence suggests that body composition is associated with the development of acute pancreatitis (AP). This study aimed to investigate the applicability of body composition in predicting AP severity. Data of 213 patients with AP from Affiliated Hospital of Putian University (AHOPTU) were included in this study, whilst data of 173 patients with AP from Fujian Medical University Union Hospital (FMUUH) were used for external validation. Patients were classified into the non-severe and severe groups according to AP severity. After seven days of treatment, in patients from AHOPTU, the difference in skeletal muscle index before and after treatment (ΔSMI) was significantly higher (P = 0.002), while the skeletal muscle radiodensity before treatment (PreSMR) was significantly lower (P = 0.042) in the non-severe group than in the severe group. The multivariate logistic regression model also revealed that the ΔSMI and PreSMR were independent risk factors for AP severity. The optimal cut-off values of ΔSMI and PreSMR were 1.0 and 43.7, respectively. The following metabolic score (SMS) was established to predict AP severity: 0: ΔSMI < 1.0 and PreSMR < 43.7; 1: ΔSMI ≥ 1.0 and PreSMR < 43.7 or ΔSMI < 1.0 and PreSMR ≥ 43.7; 3: ΔSMI ≥ 1.0 and PreSMR ≥ 43.7. In patients from AHOPTU and FMUUH, the areas under the curves (AUC) for this model were 0.764 and 0.741, respectively. ΔSMI and PreSMR can accurately predict AP severity. It is recommended to routinely evaluate the statuses of patients with AP using the predictive model presented in this study for individualized treatment.

Development and application of the Cancer Cachexia Staging Index for the diagnosis and staging of cancer cachexia

OBJECTIVE

The current tools for evaluating cancer cachexia are either too simple to reflect the far-reaching effects of cachexia or too complicated to be used in daily practice. This study aimed to develop a cancer cachexia staging index (CCSI) that is both practical and comprehensive.

METHODS

Patients with gastrointestinal cancers were prospectively included in the study. Clinical data including weight change, body composition, systematic inflammation, nutrition, and function status were entered into regression models to determine the best variable combination as well as their respective cutoff values and score distribution in the CCSI. The CCSI's ability to predict outcomes and evaluate the consequences of cachexia for patients were then assessed.

RESULTS

Clinical information and test results from 10 568 patients were used to develop a CCSI composed of subjective and objective measures. Subjective measures included body mass index-adjusted weight loss grade, rate of weight loss, inflammation (neutrophil-to-lymphocyte ratio and C-reactive protein level), and prealbumin level. Objective measures included appetite status and physical status. Patients were diagnosed and stratified by the total CCSI score into 3 subgroups: no cachexia, mild or moderate cachexia, and severe cachexia. The CCSI grades showed good survival discrimination and were independently predictive of survival in multivariate analysis. Compared with the traditional Fearon criteria for diagnosing cancer cachexia, the CCSI was more accurate in predicting postoperative complications (net reclassification index [NRI], 2.8%; 95% CI, 0.0104-0.0456%), death (NRI, 10.68%; 95% CI, 0.0429-0.1708%), recurrence (NRI, 3.71%; 95% CI, 0.0082-0.0685%), and overall survival (NRI, 8.5%; 95% CI, 0.0219-0.1533%). The CCSI also had better discriminative ability than Fearon criteria in discriminating nutritional status, body composition, and systematic inflammation in patients with or without cachexia. A more detailed evaluation of a randomly selected subgroup (n = 1566) showed that CCSI grades had good discrimination of appetite and food intake status, physical function and muscle strength, symptom burden, and quality of life.

CONCLUSIONS

The CCSI is a comprehensive and practical evaluation tool for cancer cachexia. It can predict postoperative outcomes and survival. The CCSI stages showed good discrimination when evaluating patients with cancer in terms of nutritional status, physical function, systematic inflammation, body composition, symptom burden, and quality of life.

Factors related to trunk intramuscular adipose tissue content - A comparison of younger and older men

The present study investigated factors related to trunk intramuscular adipose tissue (IntraMAT) content in younger and older men. Twenty-three healthy younger (20 to 29 years) and 20 healthy older men (63 to 79 years) participated in this study. The trunk IntraMAT content was measured using magnetic resonance imaging at the height of the 3rd lumbar vertebra. In addition to blood properties and physical performance, dietary intake was assessed by a self-administered diet history questionnaire. The dietary intake status was quantified using the nutrient adequacy score for the intake of 10 selected nutrients by summing the number of items that met the criteria of dietary reference intakes for Japanese individuals. The results obtained revealed that the trunk IntraMAT content was significantly higher in the older group than in the younger group (p < 0.05). In the younger group, the trunk IntraMAT content significantly correlated with systolic and diastolic blood pressure and HbA1c (r_s = 0.443 to 0.464, p < 0.05). In the older group, significant and negative correlations were observed between the trunk IntraMAT content and 5-m usual walking speed, handgrip strength, and nutrient adequacy scores (r_s = -0.485 to -0.713, p < 0.05). These results indicate that factors associated with the trunk IntraMAT content differed in an age dependent manner. In the younger group, the trunk IntraMAT content correlated with the metabolic status such as blood pressure and HbA1c. In the older group, physical performance and the dietary intake status negatively correlated with the trunk IntraMAT content.

Altered Plasma Fatty Acid Abundance Is Associated with Cachexia in Treatment-Naïve Pancreatic Cancer

Cachexia occurs in up to 80% of pancreatic ductal adenocarcinoma (PDAC) patients and is characterized by unintentional weight loss and tissue wasting. To understand the metabolic changes that occur in PDAC-associated cachexia, we compared the abundance of plasma fatty acids (FAs), measured by gas chromatography, of subjects with treatment-naïve metastatic PDAC with or without cachexia, defined as a loss of > 2% weight and evidence of sarcopenia (n = 43). The abundance of saturated, monounsaturated, and polyunsaturated FAs was not different between subjects with cachexia and those without. Oleic acid was significantly higher in subjects with cachexia (p = 0.0007) and diabetes (p = 0.015). Lauric (r = 0.592, p = 0.0096) and eicosapentaenoic (r = 0.564, p = 0.015) acids were positively correlated with age in cachexia patients. Subjects with diabetes (p = 0.021) or both diabetes and cachexia (p = 0.092) had low palmitic:oleic acid ratios. Linoleic acid was lower in subjects with diabetes (p = 0.018) and correlated with hemoglobin (r = 0.519, p = 0.033) and albumin (r = 0.577, p = 0.015) in subjects with cachexia. Oleic or linoleic acid may be useful treatment targets or biomarkers of cachexia in patients with metastatic PDAC, particularly those with diabetes.

Population Pharmacokinetic Analysis from First-in-Human Data for HDAC Inhibitor, REC-2282 (AR-42), in Patients with Solid Tumors and Hematologic Malignancies: A Case Study for Evaluating Flat vs. Body Size Normalized Dosing

Background and Objectives

REC-2282 is a novel histone deacetylase inhibitor that has shown antitumor activity in in vitro and in vivo models of malignancy.  The aims of this study were to characterize the population pharmacokinetics of REC-2282 (AR-42) from the first-in-human (NCT01129193) and phase I acute myeloid leukemia trials (NCT01798901) and to evaluate potential sources of variability. Additionally, we sought to understand alternate body size descriptors as sources of inter-individual variability (IIV), which was significant for dose-normalized maximum observed concentration and area under the concentration-time curve (AUC).

Methods

Datasets from two clinical trials were combined, and population pharmacokinetic analysis was performed using NONMEM and R softwares; patient demographics were tested as covariates.

Results

A successful population pharmacokinetic model was constructed. The pharmacokinetics of REC-2282 were best described by a two-compartment model with one transit compartment for absorption, first-order elimination and a proportional error model. Fat-free mass (FFM) was retained as a single covariate on clearance (CL), though it explained < 3% of the observed variability on CL. Tumor type and formulation were retained as covariates on lag time, and a majority of variability, attributed to absorption, remained unexplained. Computed tomography (CT)-derived lean body weight estimates were lower than estimated lean body weight and fat-free mass measures in most patients. Analysis of dose-normalized AUC vs. body size descriptors suggests flat dosing is most appropriate for REC-2282.

Conclusions

FFM was identified as a significant covariate on CL; however, it explained only a very small portion of the IIV; major factors contributing significantly to REC-2282 pharmacokinetic variability remain unidentified.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13318-021-00722-z.

Validity of Extended-Field-of-View Ultrasound Imaging to Evaluate Quantity and Quality of Trunk Skeletal Muscles

This study examined the validity of extended-field-of-view (EFOV) ultrasound imaging for evaluating the quantity (cross-sectional area [CSA]) and quality (accumulation of intramuscular fat) of trunk skeletal muscles (rectus abdominis, abdominal oblique and erector spinae) using magnetic resonance imaging (MRI) as a reference. Thirty healthy young men participated in this study. Cross-sectional images of the trunk at the height of the third lumbar vertebra were acquired and compared by EFOV ultrasound imaging and MRI. No significant differences were observed in CSAs between the two methods (0.74 ≤ R² ≤ 0.85). Echo intensities significantly correlated with MRI-derived accumulation of intramuscular fat in each skeletal muscle group. However, the correlation coefficients were relatively low (0.37 ≤ r ≤0.47; p < 0.05). These results indicate that EFOV ultrasound imaging is a reliable method for assessing trunk skeletal muscle CSA. Further research is warranted to find the optimal ultrasound setting for evaluating muscle quality.

Muscle mass, not radiodensity, predicts physical function in cancer patients with or without cachexia

BACKGROUND

There is a need to better understand the relationship between functional impairment and muscle mass in cancer cachexia. This study aimed to establish the relationship between computed tomography (CT)-derived muscle cross-sectional area (CSA), radiodensity, and skeletal muscle index (SMI) and dual energy X-ray absorptiometry (DXA) parameters with functional performance in cancer patients.

MATERIALS AND METHODS

Handgrip strength, stair climb power (SCP), one-repetition maximum (1RM) strength, and body composition (CT and DXA) were compared across cancer patients with cachexia (CAC; N = 28), without cachexia (CNC; N = 28), and non-cancer patients (CON; N = 19). Multivariate regression was performed to find predictors of function.

RESULTS

CAC had lower CT muscle CSA and SMI and lower DXA appendicular lean mass (ALM) than CNC or CON (p ≤ 0.011). Muscle radiodensity was not different across groups despite larger proportion of low CT SMI in CAC, and CAC performed worse in SCP than CON (p = 0.018). In cancer patients, DXA ALM and CT muscle CSA each predicted physical function (p ≤ 0.05); muscle radiodensity did not, and DXA ALM explained more variability in SCP and 1RM than CT muscle CSA.

CONCLUSIONS

Stair climb power was reduced in cancer cachexia; muscle radiodensity was not. Muscle mass by CT or DXA, but not radiodensity, predicted functional performance in cancer patients.

A guide for using NIH Image J for single slice cross-sectional area and composition analysis of the thigh from computed tomography

Reports using computed tomography (CT) to estimate thigh skeletal muscle cross-sectional area and mean muscle attenuation are often difficult to evaluate due to inconsistent methods of quantification and/or poorly described analysis methods. This CT tutorial provides step-by-step instructions in using free, NIH Image J software to quantify both muscle size and composition in the mid-thigh, which was validated against a robust commercially available software, SliceOmatic. CT scans of the mid-thigh were analyzed from 101 healthy individuals aged 65 and older. Mean cross-sectional area and mean attenuation values are presented across seven defined Hounsfield unit (HU) ranges along with the percent contribution of each region to the total mid-thigh area. Inter-software correlation coefficients ranged from R² = 0.92–0.99 for all specific area comparisons measured using the Image J method compared to SliceOmatic. We recommend reporting individual HU ranges for all areas measured. Although HU range 0–100 includes the majority of skeletal muscle area, HU range -29 to 150 appears to be the most inclusive for quantifying total thigh muscle. Reporting all HU ranges is necessary to determine the relative contribution of each, as they may be differentially affected by age, obesity, disease, and exercise. This standardized operating procedure will facilitate consistency among investigators reporting computed tomography characteristics of the thigh on single slice images.

Trial Registration: ClinicalTrials.gov NCT02308228.

Tutorial for using SliceOmatic to calculate thigh area and composition from computed tomography images from older adults

OBJECTIVE

Area of muscle, fat, and bone is often measured in thigh CT scans when tissue composition is a key outcome. SliceOmatic software is commonly referenced for such analysis but published methods may be insufficient for new users. Thus, a quick start guide to calculating thigh composition using SliceOmatic has been developed.

METHODS

CT images of the thigh were collected from older (69 ± 4 yrs, N = 24) adults before and after 12-weeks of resistance training. SliceOmatic was used to segment images into seven density regions encompassing fat, muscle, and bone from -190 to +2000 Hounsfield Units [HU]. The relative contributions to thigh area and the effects of tissue density overlap for skin and marrow with muscle and fat were determined.

RESULTS

The largest contributors to the thigh were normal fat (-190 to -30 HU, 29.1 ± 7.4%) and muscle (35 to 100 HU, 48.9 ± 8.2%) while the smallest were high density (101 to 150 HU, 0.79 ± 0.50%) and very high density muscle (151 to 200 HU, 0.07 ± 0.02%). Training significantly (P<0.05) increased area for muscle in the very low (-29 to -1 HU, 5.5 ± 7.9%), low (0 to 34 HU, 9.6 ± 16.8%), normal (35 to 100 HU, 4.2 ± 7.9%), and high (100 to 150 HU, 70.9 ± 80.6%) density ranges for muscle. Normal fat, very high density muscle and bone did not change (P>0.05). Contributions to area were altered by ~1% or less and the results of training were not affected by accounting for skin and marrow.

CONCLUSIONS

When using SliceOmatic to calculate thigh composition, accounting for skin and marrow may not be necessary. We recommend defining muscle as -29 to +200 HU but that smaller ranges (e.g. low density muscle, 0 to 34 HU) can easily be examined for relationships with the health condition and intervention of interest.

TRIAL REGISTRATION

Clinicaltrials.gov NCT02261961.