Improving risk stratification among veterans diagnosed with prostate cancer: impact of the 17-gene prostate score assay.

BACKGROUND: Active surveillance (AS) has been widely implemented within Veterans Affairs' medical centers (VAMCs) as a standard of care for low-risk prostate cancer (PCa). Patient characteristics such as age, race, and Agent Orange (AO) exposure may influence advisability of AS in veterans. The 17-gene assay may improve risk stratification and management selection. OBJECTIVES: To compare management strategies for PCa at 6 VAMCs before and after introduction of the Oncotype DX Genomic Prostate Score (GPS) assay. STUDY DESIGN: We reviewed records of patients diagnosed with PCa between 2013 and 2014 to identify management patterns in an untested cohort. From 2015 to 2016, these patients received GPS testing in a prospective study. Charts from 6 months post biopsy were reviewed for both cohorts to compare management received in the untested and tested cohorts. SUBJECTS: Men who just received their diagnosis and have National Comprehensive Cancer Network (NCCN) very low-, low-, and select cases of intermediate-risk PCa. RESULTS: Patient characteristics were generally similar in the untested and tested cohorts. AS utilization was 12% higher in the tested cohort compared with the untested cohort. In men younger than 60 years, utilization of AS in tested men was 33% higher than in untested men. AS in tested men was higher across all NCCN risk groups and races, particular in low-risk men (72% vs 90% for untested vs tested, respectively). Tested veterans exposed to AO received less AS than untested veterans. Tested nonexposed veterans received 19% more AS than untested veterans. Median GPS results did not significantly differ as a factor of race or AO exposure. CONCLUSIONS: Men who receive GPS testing are more likely to utilize AS within the year post diagnosis, regardless of age, race, and NCCN risk group. Median GPS was similar across racial groups and AO exposure groups, suggesting similar biology across these groups. The GPS assay may be a useful tool to refine risk assessment of PCa and increase rates of AS among clinically and biologically low-risk patients, which is in line with guideline-based care.

Does Visceral Fat Estimated by Dual-Energy X-ray Absorptiometry Independently Predict Cardiometabolic Risks in Adults?

BACKGROUND: Abdominal visceral fat, typically measured by computer tomography (CT) or magnetic resonance imaging (MRI), has been shown to correlate with cardiometabolic risks. The purpose of this study was to examine whether a newly developed and validated visceral fat measurement from dual-energy X-ray absorptiometry (DXA) provides added predictive value to the cross-sectional differences of cardiometabolic parameters beyond the traditional anthropometric and DXA adiposity parameters. METHOD: A heterogeneous cohort of 194 adults (81 males and 113 females) with a BMI of 19 to 54 kg/m(2) participated in this cross-sectional study. Body composition was measured with a DXA densitometer. Visceral fat was then computed with a proprietary algorithm. Insulin sensitivity index (SI, measured by intravenous glucose tolerance test), blood pressures, and lipid profiles, and peak oxygen uptake were also measured as cardiometabolic risk parameters. RESULTS: DXA-estimated visceral fat mass was associated with HDL cholesterol (regression coefficient [ß] = -5.15, P < .01, adjusted R(2) = .21), triglyceride (ß = 26.01, P < .01, adjusted R(2) = .14), and peak oxygen uptake (ß = -3.15, P < .01, adjusted R(2) = .57) after adjusting for age, gender, and ethnicity. A subanalysis stratifying gender-specific BMI tertiles showed visceral fat, together with ethnicity, was independently associated with SI in overweight men and moderately obese women (second tertile). CONCLUSIONS: Without requiring additional CT or MRI-based measurements, visceral fat detected by DXA might offer certain advantages over the traditional DXA adiposity parameters as means of assessing cardiometabolic risks.

The Impact of a Biopsy Based 17-Gene Genomic Prostate Score on Treatment Recommendations in Men with Newly Diagnosed Clinically Prostate Cancer Who are Candidates for Active Surveillance.

INTRODUCTION: The biopsy based 17-gene GPS was clinically validated to predict the likelihood of adverse surgical pathology in men with NCCN® very low, low or low-intermediate risk prostate cancer. We performed a prospective study to assess the impact of incorporating GPS into treatment recommendations in 3 high volume urology practices. METHODS: Men with newly diagnosed prostate cancer meeting specific NCCN criteria were prospectively enrolled in the trial. Biopsy tissue was analyzed. Urologists indicated treatment recommendations on questionnaires administered before and after GPS. The primary study objectives were to assess all changes in treatment modality and/or treatment intensity after GPS. RESULTS: A total of 158 men were included in analysis, including 35, 71 and 52 at NCCN very low, low and low-intermediate risk. Biological risk predicted by GPS differed from NCCN clinical risk alone in 61 men (39%). Overall 18% of recommendations between active surveillance and immediate treatment changed after GPS. The relative increase in recommendations for active surveillance was 24% (absolute change 41% to 51%). In 41 of 158 men (26%) modality and/or intensity recommendations changed after GPS, including 25, 14 and 2 in whom recommendation intensity decreased, increased and were equivocal, respectively. All changes were directionally consistent with GPS. The NCCN low risk group showed the greatest absolute recommendation change after GPS (37%). In 17 of 57 men (30%) the initial recommendation of radical prostatectomy was changed to active surveillance after GPS. Urologists indicated greater confidence and found that incorporating GPS was useful in 85% and 79% of cases, respectively, including when biological risk confirmed the clinical risk category. CONCLUSIONS: This study demonstrates that the 17-gene GPS influenced treatment recommendations among urologists and provided increased confidence in these recommendations in patients at NCCN very low to low-intermediate risk.

Topographical body fat distribution links to amino acid and lipid metabolism in healthy obese women [corrected].

Visceral adiposity is increasingly recognized as a key condition for the development of obesity related disorders, with the ratio between visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) reported as the best correlate of cardiometabolic risk. In this study, using a cohort of 40 obese females (age: 25-45 y, BMI: 28-40 kg/m(2)) under healthy clinical conditions and monitored over a 2 weeks period we examined the relationships between different body composition parameters, estimates of visceral adiposity and blood/urine metabolic profiles. Metabonomics and lipidomics analysis of blood plasma and urine were employed in combination with in vivo quantitation of body composition and abdominal fat distribution using iDXA and computerized tomography. Of the various visceral fat estimates, VAT/SAT and VAT/total abdominal fat ratios exhibited significant associations with regio-specific body lean and fat composition. The integration of these visceral fat estimates with metabolic profiles of blood and urine described a distinct amino acid, diacyl and ether phospholipid phenotype in women with higher visceral fat. Metabolites important in predicting visceral fat adiposity as assessed by Random forest analysis highlighted 7 most robust markers, including tyrosine, glutamine, PC-O 44∶6, PC-O 44∶4, PC-O 42∶4, PC-O 40∶4, and PC-O 40∶3 lipid species. Unexpectedly, the visceral fat associated inflammatory profiles were shown to be highly influenced by inter-days and between-subject variations. Nevertheless, the visceral fat associated amino acid and lipid signature is proposed to be further validated for future patient stratification and cardiometabolic health diagnostics.

Abdominal visceral fat measurement using dual-energy X-ray: association with cardiometabolic risk factors.

OBJECTIVE: To examine the association between cardiometabolic risk factors and visceral adipose tissue (VAT) measurements using a dual-energy X-ray absorptiometry (DXA) based approach. DESIGN AND METHODS: An analysis of cross-sectional relationships between DXA VAT measured using CoreScan (GE Healthcare) and cardiometabolic indicators was conducted on a sample of 939 subjects (541 females and 398 males; average age, 56 years; average BMI, 26 kg/m2) who had previously undergone a total body DXA scan as well as measurements of key cardiometabolic risk factors. RESULTS: Sex-specific, age-adjusted multivariable regression analysis showed that for both men and women, DXA VAT was significantly associated with increased odds of hypertension, impaired fasting glucose, metabolic syndrome, and type 2 diabetes (P < 0.001). After additional model adjustment for BMI and waist circumference, the odds ratio (per SD change in VAT) for type 2 diabetes was 2.07 for women and 2.25 for men. Similarly, the odds ratio for metabolic syndrome for women was 3.46 and for men was 1.75. CONCLUSIONS: VAT measured using DXA showed a significant association with cardiometabolic risk factors and disease. These relationships persist after statistical adjustment for age, BMI, and waist circumference. DXA VAT may provide a new accessible option for quantifying VAT-related cardiometabolic risk.

Precision of a new tool to measure visceral adipose tissue (VAT) using dual-energy X-Ray absorptiometry (DXA).

OBJECTIVE: A new tool to quantify visceral adipose tissue (VAT) over the android region of a total body dual-energy x-ray absorptiometry (DXA) scan has recently been reported. The measurement, CoreScan, is currently available on Lunar iDXA densitometers. The purpose of the study was to determine the precision of the CoreScan VAT measurement, which is critical for understanding the utility of this measure in longitudinal trials. DESIGN AND METHODS: VAT precision was characterized in both an anthropomorphic imaging phantom (measured on 10 Lunar iDXA systems) and a clinical population consisting of obese women (n = 32). RESULTS: The intrascanner precision for the VAT phantom across 9 quantities of VAT mass (0-1,800 g) ranged from 28.4 to 38.0 g. The interscanner precision ranged from 24.7 to 38.4 g. There was no statistical dependence on the quantity of VAT for either the inter- or intrascanner precision result (p = 0.670). Combining inter- and intrascanner precision yielded a total phantom precision estimate of 47.6 g for VAT mass, which corresponds to a 4.8% coefficient of variance (CV) for a 1 kg VAT mass. Our clinical population, who completed replicate total body scans with repositioning between scans, showed a precision of 56.8 g on an average VAT mass of 1110.4 g. This corresponds to a 5.1% CV. Hence, the in vivo precision result was similar to the phantom precision result. CONCLUSIONS: The study suggests that CoreScan has a relatively low precision error in both phantoms and obese women and therefore may be a useful addition to clinical trials where interventions are targeted towards changes in visceral adiposity.

Quantification of visceral adipose tissue using lunar dual-energy X-ray absorptiometry in Asian Chinese.

OBJECTIVE: To evaluate the new DXA VAT method on an Asian Chinese population by comparing to a reference method, computed tomography (CT). DESIGN AND METHODS: In total, 145 adult men and women volunteers, representing a wide range of ages (19-83 years) and BMI values (18.5-39.3 kg/m(2) ) were studied with both DXA and CT. RESULTS: The coefficient of determination (r(2) ) for regression of CT on DXA values was 0.947 for females, 0.891 for males and 0.915 combined. The 95% confidence interval for r was 0.940-0.969 for the combined data. The Bland-Altman test showed a VAT bias (CT as standard method) of 143 cm(3) for females and 379 cm(3) for males. Combined, the bias was 262 cm(3) with 95% limits of agreement of -232 to 755 cm(3) . While the current DXA method moderately overestimates the VAT volume for the study subjects, a further analysis suggested that the overestimation could be largely contributed to VAT movement due to breath-holding status. CONCLUSIONS: For Asian Chinese, VAT measured with DXA is highly correlated to VAT measured with CT. Validation of the DXA VAT tool using a reference method (e.g., CT) needs to carefully control the breath-holding protocol.

Visceral adipose tissue quantification using Lunar Prodigy.

A dual-energy X-ray absorptiometry (DXA) application to measure visceral adipose tissue (VAT) in the android region of a total body DXA scan has recently been developed. This new application, CoreScan, has been validated on the Lunar iDXA (GE Healthcare, Madison, WI) densitometer against volumetric computed tomography. The geometric assumptions underlying the CoreScan model are the same on the Prodigy (GE Healthcare, Madison, WI) densitometer. However, differences between the peak X-ray voltage and detector array configurations may lead to differences in VAT quantification. The purpose of this study was to evaluate the agreement of Prodigy and iDXA CoreScan values and to characterize differences in VAT precision between the instruments. Data from volunteers with paired Prodigy and iDXA measurements were used to define empirical adjustments to the VAT algorithm parameters (n=59) and validate performance on Prodigy (n=62). Prodigy VAT measurements were highly correlated to iDXA (r=0.984). The mean of the Prodigy-iDXA VAT volume differences was -13.8cm³ with a 95% confidence interval of -45 to +17cm³. The Bland-Altman 95% limits of agreement for the 2 methods were -252 to +224cm³. Measurement of short-term precision showed that measurement error variance on iDXA was smaller (p<0.01) than Prodigy (coefficient of variance: 7.3% vs 9.8%). Precision results are in agreement with previous reports on the differences between Prodigy and iDXA for body composition measures. Prodigy and iDXA measures of VAT are similar, but the lower precision of the Prodigy may require investigators to target larger changes in VAT.

Precision of GE Lunar iDXA for the measurement of total and regional body composition in nonobese adults.

Dual-energy X-ray absorptiometry (DXA) is a well-accepted technique for measuring body composition. Knowledge of measurement precision is critical for monitoring of changes in bone mineral content (BMC), and fat and lean masses. The purpose of this study was to characterize in vivo precision of total body and regional body composition parameters using the GE Lunar iDXA (GE Healthcare Lunar, Madison, WI) system in a sample of nonobese subjects. We also evaluated the difference between expert and automatic region-of-interest (ROI) analysis on body composition precision. To this end, 2 total body scans were performed on each subject with repositioning between scans. Total body precision for BMC, fat and lean mass were 0.5%, 1.0%, and 0.5% coefficient of variation (CV), respectively. Regional body composition precision error was less than 2.5% CV for all regions except arms. Precision error was higher for the arms (CV: BMC 1.5%; fat mass 2.8%; lean mass 1.6%), likely owing to the placement of arms relative to torso leading to differences in ROI. There was a significant correlation between auto ROI and expert ROI (r>0.99). Small, but statistically significant differences were found between auto and manual ROI. Differences were small in total body, leg, trunk, and android and gynoid regions (0.004-2.8%), but larger in arm region (3.0-6.3%). Total body and regional precision for iDXA are small and it is suggested that iDXA may be useful for monitoring changes in body composition during longitudinal trials.

Dual-energy X-ray absorptiometry for quantification of visceral fat.

Obesity is the major risk factor for metabolic syndrome and through it diabetes as well as cardiovascular disease. Visceral fat (VF) rather than subcutaneous fat (SF) is the major predictor of adverse events. Currently, the reference standard for measuring VF is abdominal X-ray computed tomography (CT) or magnetic resonance imaging (MRI), requiring highly used clinical equipment. Dual-energy X-ray absorptiometry (DXA) can accurately measure body composition with high-precision, low X-ray exposure, and short-scanning time. The purpose of this study was to validate a new fully automated method whereby abdominal VF can be measured by DXA. Furthermore, we explored the association between DXA-derived abdominal VF and several other indices for obesity: BMI, waist circumference, waist-to-hip ratio, and DXA-derived total abdominal fat (AF), and SF. We studied 124 adult men and women, aged 18-90 years, representing a wide range of BMI values (18.5-40 kg/m(2)) measured with both DXA and CT in a fasting state within a one hour interval. The coefficient of determination (r(2)) for regression of CT on DXA values was 0.959 for females, 0.949 for males, and 0.957 combined. The 95% confidence interval for r was 0.968 to 0.985 for the combined data. The 95% confidence interval for the mean of the differences between CT and DXA VF volume was -96.0 to -16.3 cm(3). Bland-Altman bias was +67 cm(3) for females and +43 cm(3) for males. The 95% limits of agreement were -339 to +472 cm(3) for females and -379 to +465 cm(3) for males. Combined, the bias was +56 cm(3) with 95% limits of agreement of -355 to +468 cm(3). The correlations between DXA-derived VF and BMI, waist circumference, waist-to-hip ratio, and DXA-derived AF and SF ranged from poor to modest. We conclude that DXA can measure abdominal VF precisely in both men and women. This simple noninvasive method with virtually no radiation can therefore be used to measure VF in individual patients and help define diabetes and cardiovascular risk.

Modeling physical activity outcomes from wearable monitors.

Although the measurement of physical activity with wearable monitors may be considered objective, consensus guidelines for collecting and processing these objective data are lacking. This article presents an algorithm embodying best practice recommendations for collecting, processing, and reporting physical activity data routinely collected with accelerometry-based activity monitors. This algorithm is proposed as a linear series of seven steps within three successive phases. The Precollection Phase includes two steps. Step 1 defines the population of interest, the type and intensity of physical activity behaviors to be targeted, and the preferred outcome variables, and identifies the epoch duration. In Step 2, the activity monitor is selected, and decisions about how long and where on the body the monitor is to be worn are made. The Data Collection Phase, Step 3, consists of collecting and processing activity monitor data and is dependent on decisions made previously. The Postcollection Phase consists of four steps. Step 4 involves quality and quantity control checks of the activity monitor data. In Step 5, the raw data are transformed into physiologically meaningful units using a calibration algorithm. Step 6 involves summarizing these data according to the target behavior. In Step 7, physical activity outcome variables based on time, energy expenditure, or movement type are generated. Best practice recommendations include the full disclosure of each step within the algorithm when reporting monitor-derived physical activity outcome variables in the research literature. As such, those reading and publishing within the research literature, as well as future users, will have the best chance for understanding the interactions between study methodology and activity monitor selection, as well as the best possibility for relating their own monitor-derived physical activity outcome variables to the research literature.

Validation of the ActiGraph two-regression model for predicting energy expenditure.

PURPOSE: The purpose of this study was to validate a two-regression model for predicting energy expenditure (EE) from ActiGraph GT1M accelerometer-generated activity counts using a whole-room indirect calorimeter and the doubly labeled water (DLW) technique. We also investigated if a low-pass filter (LPF) approach would improve the model's accuracy in the minute-to-minute EE prediction. METHODS: Thirty-four healthy volunteers (age = 20-67 yr, body mass index = 19.3-52.1 kg.m) spent approximately 24 h in a room calorimeter while wearing a GT1M monitor and performed structured and self-selected activities followed by overnight sleep. The EE predicted by the models and expressed in metabolic equivalents (MET-minutes) during waking times was compared with the room calorimeter-measured EE. A subset of volunteers (n = 22) completed a 14-d DLW protocol in free living while wearing an ActiGraph. The average daily EE predicted by the models (MET-minutes) was compared with the DLW. RESULTS: Compared with the room calorimeter, the two-regression model overpredicted EE by 10.2% +/- 11.4% (1282 +/- 125 and 1174 +/- 152 MET.min, P < 0.001) and time spent in moderate physical activity (PA) by 36.9 +/- 46.0 min while underestimating the time spent in light PA by -48.3 +/- 55.0 min (P < 0.05). The LPF reduced the squared and mean absolute error in the EE prediction (P < 0.05) but not the prediction error in time spent in moderate or light PA (both P > 0.05). The EE measured by DLW (2108 +/- 358 MET.min.d) and predicted by both filtered and unfiltered models (2104 +/- 218 and 2192 +/- 228 MET.min.d, respectively) were similar (P > 0.05). CONCLUSIONS: The two-regression model with LPF showed good agreement with total EE measured using room calorimeter and DLW. However, the individual variability in assessing time spent in sedentary, low, and moderate PA intensities and related EE remains significant.

Optimizing energy expenditure detection in human metabolic chambers.

Whole-room indirect calorimeters are capable of measuring human metabolic rate in conditions representative of quasi-free-living state through measurement of oxygen consumption (VO2) and carbon dioxide production (VCO2). However, the relatively large room size required for patient comfort creates low signal-to-noise ratio for the VO2 and VCO2 signals. We proposed a wavelet-based approach to efficiently remove noise while retaining important dynamic changes in the VO2 and VCO2. We used correlated noise modeled from gasinfusion experiments superimposed on theoretical VO2 sequences to test the accuracy of a wavelet based processing method. The wavelet filtering is demonstrated to improve the accuracy and sensitivity of minute-to-minute changes in VO2, while maintaining stability during steady-state periods. The wavelet method is shown to have a lower mean absolute error and reduced total error when compared to standard methods of processing calorimeter signals.

Body composition measured by dual-energy X-ray absorptiometry half-body scans in obese adults.

Dual-energy X-ray absorptiometry (DXA) has become a common measurement of human body composition. However, obese subjects have been understudied largely due to weight and scan area restrictions. Newer DXA instruments allow for heavier subjects to be supported by the DXA scanner, but the imaging area is still smaller than the body size of some obese subjects. In this study, we determined the validity of an automated half-scan methodology by comparing to the standard whole-body scans in a cohort of obese volunteers. Fifty-two subjects whose BMI>30 kg/m2 completed whole-body iDXA (GE Lunar) scans. The resulting scans were analyzed in three ways: the standard whole-body scan, total body estimated from the left side, and from the right side. Fat mass, nonbone lean mass, bone mineral content (BMC), and percent fat derived from each half scan were compared to the whole-body scans. Total fat mass, nonbone lean mass, or percent fat was comparable for the whole-body scans, left, and right side scans (>97% within individuals and >99.9% for the group). The BMC estimate using the right side scan was slightly but statistically higher than the whole-body BMC (approximately 30 g or 1%, P<0.001), while the left side scan BMC estimate was lower than the whole-body BMC by the same magnitude. No significant magnitude bias was found for any of the composition variables. We conclude that the new iDXA half-body analysis in obese subjects appears to be closely comparable to whole-body analysis for fat mass, nonbone lean mass, and percent fat.

Comparing the performance of three generations of ActiGraph accelerometers.

ActiGraph accelerometers are a useful tool for objective assessment of physical activity in clinical and epidemiological studies. Several generations of ActiGraph are being used; however, little work has been done to verify that measurements are consistent across generations. This study employed mechanical oscillations to characterize the dynamic response and intermonitor variability of three generations of ActiGraph monitors, from the oldest 7164 (n = 13), 71256 (n = 12), to the newest GT1M (n = 12). The response due to independent radius (22.1-60.4 mm) and frequency (25-250 rpm) changes were measured, as well as intermonitor variability within each generation. The 7164 and 71256 have similar relationships between activity counts and radius (P = 0.229) but were significantly different from the GT1M (P < 0.001). The frequency responses were nonlinear in all three generations. Although the response curve shapes were similar, the differences between generations at various frequencies were significant (P < 0.017), especially in the extremes of the measurement range. Intermonitor variability was markedly reduced in the GT1M compared with the 7164 and 71256. Other measurement differences between generations include decreased peak counts and decreased sensitivity in low-frequency detection in the GT1M. The results of this study revealed an improvement of the intermonitor variability by the GT1M monitor. However, the reduced sensitivity in low-count ranges in the GT1M may not be well suited for monitoring sedentary or light-intensity movements. Furthermore, the algorithms for energy expenditure predictions developed using older 7164 monitors may need to be modified for the GT1M.

Validity of physical activity intensity predictions by ActiGraph, Actical, and RT3 accelerometers.

OBJECTIVE: Accelerometers are promising tools for characterizing physical activity (PA) patterns in free-living persons. To date, validation of energy expenditure (EE) predictions from accelerometers has been restricted to short laboratory or simulated free-living protocols. This study seeks to determine the capabilities of eight previously published regression equations for three commercially available accelerometers to predict summary measures of daily EE. METHODS AND PROCEDURES: Study participants were outfitted with ActiGraph, Actical, and RT3 accelerometers, while measurements were simultaneously made during overnight stays in a room calorimeter, which provided minute-by-minute EE measurements, in a diverse subject population (n = 85). Regression equations for each device were used to predict the minute-by-minute metabolic equivalents (METs) along with the daily PA level (PAL). RESULTS: Two RT3 regressions and one ActiGraph regression were not significantly different from calorimeter measured PAL. When data from the entire visit were divided into four intensity categories-sedentary, light, moderate, and vigorous-significant (P < 0.001) over- and underpredictions were detected in numerous regression equations and intensity categories. DISCUSSION: Most EE prediction equations showed differences of <2% in the moderate and vigorous intensity categories. These differences, though small in magnitude, may limit the ability of these regressions to accurately characterize whether specific PA goals have been met in the field setting. New regression equations should be developed if more accurate prediction of the daily PAL or higher precision in determining the time spent in specific PA intensity categories is desired.

An artificial neural network model of energy expenditure using nonintegrated acceleration signals.

Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that we felt had the potential to characterize EE intensity. Using these data, we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12 x 20 x 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 +/- 0.10 kcal/min), mean squared errors (0.23 +/- 0.14 kcal(2)/min(2)), and difference in total EE (21 +/- 115 kcal/day), compared with both the IDEEA (P < 0.01) and a regression model for the ActiGraph accelerometer (P < 0.001). Thus ANN combined with raw acceleration signals is a promising approach to link body accelerations to EE. Further validation is needed to understand the performance of the model for different physical activity types under free-living conditions.

Physical activity monitors: do more sensors mean better precision?

Physical activity is essential to health. Accelerometry-based activity monitors are widely used in clinical and epidemiological research settings; however, only measuring body movement may prohibit accurate prediction of energy expenditure. Recent technological advancements allow synchronous measurements of heart rate, body temperature, acceleration, and other physiological responses and record them in detail (every minute or finer precision). Current multisensor devices are small, wireless, and capable of continuously recording data over several days or weeks, making them readily applicable in the free-living environment. Future studies should focus on developing strategies to optimize sensor data for accurate and robust predictions of clinically pertinent outcome parameters, such as total daily energy expenditure and physical activity energy expenditure. There is also a need for calibration instruments to allow users to standardize devices in their own laboratory or clinic. We also call for more transparency in publishing sensor properties and modeling algorithms, rather than proprietary or "black-box" prediction approaches.

Modality independent elastography (MIE): potential applications in dermoscopy.

The use of palpation information for skin disease characterization is not as commonly used as in other soft tissues, although mechanical differences within lesions have been noted. For example, regions of hyperkeratosis have the potential to transform into cancerous lesions and likely feature different material properties from those of surrounding normal tissue due to varying cytoarchitecture. As a result, the spatial distribution of lesion mechanical properties may serve to assist a diagnosis or enhance visualization of the complete extent of a cancerous region, i.e., accurate information regarding the margins of disease for surgical therapy. In this work, a multiresolution extension to a novel elastographic imaging method called Modality Independent Elastography (MIE) is used to characterize the mechanical properties of a skin-like phantom embedded with a mock stiff lesion. Simulation studies were also performed to investigate the potential for characterizing realistic melanoma lesions. Elasticity image reconstructions from the phantom experiments localized the stiff inclusion and had good correlation between the Young's modulus contrast ratio and experimental measurements from material testing. In addition, multiresolution MIE was shown to be a more robust framework than its single-resolution version. Results from the melanoma simulation demonstrate the potential for using multiresolution MIE with dermoscopic images.