Background & aims: Low skeletal muscle mass and density have recently been discovered as prognostic and predictive parameters to guide interventions in various populations, including cancer patients. The gold standard for body composition analysis in cancer patients is computed tomography (CT). To date, the effect of contrast-enhancement on muscle composition measurements has not been established. The aim of this study was to determine the effect of contrast-enhancement on skeletal muscle mass and density measurements on four-phase CT studies. Design: In this observational study, two observers measured cross-sectional skeletal muscle area corrected for patients' height (skeletal muscle index [SMI]) and density (SMD) at the level of the third lumbar vertebra on 50 randomly selected CT examinations with unenhanced, arterial, and portal-venous phases. The levels of agreement between enhancement phases for SMI and SMD were calculated using intra-class correlation coefficients (ICCs). Results: Mean SMI was 42.5 (±9.9) cm²/m² on the unenhanced phase, compared with 42.8 (±9.9) and 43.6 (±9.9) cm²/m² for the arterial and portal-venous phase, respectively (both p < 0.01). Mean SMD was lower for the unenhanced phase (30.9 ± 8.0 Hounsfield units [HU]) compared with the arterial (38.0 ± 9.9 HU) and portal-venous (38.7 ± 9.2 HU) phase (both p < 0.001). No significant difference was found between SMD in the portal-venous and arterial phase (p = 0.161). The ICCs were excellent (≥0.992) for all SMIs and for SMD between the contrast-enhanced phases (0.949). The ICCs for the unenhanced phase compared with the arterial (0.676) and portal-venous (0.665) phase were considered fair to good. Conclusions: Statistically significant differences in SMI were observed between different enhancement phases. However, further work is needed to assess the clinical relevance of these small differences. Contrast-enhancement strongly influenced SMD values. Studies using this measure should therefore use the portal-venous phase of contrast-enhanced CT examinations.

Objectives: To assesses whether automated brain image analysis with quantification of structural brain changes improves diagnostic accuracy in a memory clinic setting. Methods: In 42 memory clinic patients, we evaluated whether automated quantification of brain tissue volumes, hippocampal volume and white matter lesion volume improves diagnostic accuracy for Alzheimer's disease (AD) and frontotemporal dementia (FTD), compared to visual interpretation. Reference data were derived from a dementia-free aging population (n = 4915, aged >45 years), and were expressed as age- and sex-specific percentiles. Experienced radiologists determined the most likely imaging-based diagnosis based on structural brain MRI using three strategies (visual assessment of MRI only, quantitative normative information only, or a combination of both). Diagnostic accuracy of each strategy was calculated with the clinical diagnosis as the reference standard. Results: Providing radiologists with only quantitative data decreased diagnostic accuracy both for AD and FTD compared to conventional visual rating. The combination of quantitative with visual information, however, led to better diagnostic accuracy compared to only visual ratings for AD. This was not the case for FTD. Conclusion: Quantitative assessment of structural brain MRI combined with a reference standard in addition to standard visual assessment may improve diagnostic accuracy in a memory clinic setting.

Background: The association between body composition (e.g. sarcopenia or visceral obesity) and treatment outcomes, such as survival, using single-slice computed tomography (CT)-based measurements has recently been studied in various patient groups. These studies have been conducted with different software programmes, each with their specific characteristics, of which the inter-observer, intra-observer, and inter-software correlation are unknown. Therefore, a comparative study was performed. Methods: Fifty abdominal CT scans were randomly selected from 50 different patients and independently assessed by two observers. Cross-sectional muscle area (CSMA, i.e. rectus abdominis, oblique and transverse abdominal muscles, paraspinal muscles, and the psoas muscle), visceral adipose tissue area (VAT), and subcutaneous adipose tissue area (SAT) were segmented by using standard Hounsfield unit ranges and computed for regions of interest. The inter-software, intra-observer, and inter-observer agreement for CSMA, VAT, and SAT measurements using FatSeg, OsiriX, ImageJ, and sliceOmatic were calculated using intra-class correlation coefficients (ICCs) and Bland–Altman analyses. Cohen's κ was calculated for the agreement of sarcopenia and visceral obesity assessment. The Jaccard similarity coefficient was used to compare the similarity and diversity of measurements. Results: Bland–Altman analyses and ICC indicated that the CSMA, VAT, and SAT measurements between the different software programmes were highly comparable (ICC 0.979–1.000, P < 0.001). All programmes adequately distinguished between the presence or absence of sarcopenia (κ = 0.88–0.96 for one observer and all κ = 1.00 for all comparisons of the other observer) and visceral obesity (all κ = 1.00). Furthermore, excellent intra-observer (ICC 0.999–1.000, P < 0.001) and inter-observer (ICC 0.998–0.999, P < 0.001) agreement for all software programmes were found. Accordingly, excellent Jaccard similarity coefficients were found for all comparisons (mean ≥ 0.964). Conclusions: FatSeg, OsiriX, ImageJ, and sliceOmatic showed an excellent agreement for CSMA, VAT, and SAT measurements on abdominal CT scans. Furthermore, excellent inter-observer and intra-observer agreement were achieved. Therefore, results of studies using these different software programmes can reliably be compared.