Effects of Contrast Phases on Automated Measurements of Muscle Quantity and Quality Using CT

Comparison of Muscle and Fat Measurements Between True Noncontrast and Virtual Noncontrast Images From Three Phases of Dual-Energy Dynamic Liver CT

RATIONALE AND OBJECTIVES

To determine whether virtual noncontrast (VNC) images derived from three-phase dynamic dual-energy CT (DECT) imaging can reliably substitute true noncontrast (TNC) images in assessing muscle and fat with automatic segmentation software.

MATERIALS AND METHODS

The data from 476 dynamic liver DECT examinations performed between April 2019 and December 2020 were retrospectively analyzed. VNC images were generated from arterial (VNCa), portal-venous (VNCp), and delayed (VNCd) phase images. Automated software measured muscle, visceral fat (VF), and subcutaneous fat (SF) areas. Sarcopenia was defined using muscle-related indices. Differences in muscle and fat measurements, as well as sarcopenia prevalence, between TNC and VNC images were assessed using paired t tests and McNemar tests, respectively.

RESULTS

The average age of the 476 patients (307 men) was 58.4±12.5years. Muscle density and area differed significantly between the TNC and VNC images; TNC images showed higher mean muscle density and smaller skeletal muscle area (SMA) than all VNC images (P<0.001). VF and SF attenuations were significantly lower on TNC images than on all VNC images (P<0.001). The proportions of sarcopenic patients did not differ significantly between TNC and VNCp or VNCd, regardless of the muscle index used.

CONCLUSION

Despite significant differences in muscle and fat attenuations between TNC and VNC images, VNCp and VNCd may be acceptable alternatives for measuring muscle and fat areas. However, TNC and VNC images should not be used interchangeably for assessing tissue attenuation or specific muscle components such as LAMA or NAMA.

Effect of Intravenous Contrast on CT Body Composition Measurements in Patients with Intraductal Papillary Mucinous Neoplasm

BACKGROUND

The effect of differing post-contrast phases on CT body composition measurements is not yet known.

METHODS

A fully automated AI-based body composition analysis using DAFS was performed on a retrospective cohort of 278 subjects undergoing pre-treatment triple-phase CT for pancreatic intraductal papillary mucinous neoplasm. The CT contrast phases included noncontrast (NON), arterial (ART), and venous (VEN) phases. The software selected a single axial CT image at mid-L3 on each phase for body compartment segmentation. The areas (cm2) were calculated for skeletal muscle (SM), intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). The mean Hounsfield units of skeletal muscle (SMHU) within the segmented regions were calculated. Bland-Altman and Chi Square analyses were performed.

RESULTS

SM-NON had a lower percentage of bias [LOA] than SM-ART, -0.7 [-7.6, 6.2], and SM-VEN, -0.3 [-7.6, 7.0]; VAT-NON had a higher percentage of bias than ART, 3.4 [-18.2, 25.0], and VEN, 5.8 [-15.0, 26.6]; and this value was lower for SAT-NON than ART, -0.4 [-14.9, 14.2], and VEN, -0.5 [-14.3, 13.4]; and higher for IMAT-NON than ART, 5.9 [-17.9, 29.7], and VEN, 9.5 [-17.0, 36.1]. The bias in SMHU NON [LOA] was lower than that in ART, -3.8 HU [-9.8, 2.1], and VEN, -7.8 HU [-14.8, -0.8].

CONCLUSIONS

IV contrast affects the voxel HU of fat and muscle, impacting CT analysis of body composition. We noted a relatively smaller bias in the SM, VAT, and SAT areas across the contrast phases. However, SMHU and IMAT experienced larger bias. During threshold risk stratification for CT-based measurements of SMHU and IMAT, the IV contrast phase should be taken into consideration.

Influence of weight-adjusted contrast enhancement on computed tomography-derived skeletal muscle measures: a retrospective proof-of-concept comparative study between Danish females and males

BACKGROUND

Computed tomography (CT) has an underutilized potential for evaluating body composition in clinical settings. Often conducted with intravenous contrast (IVC), CT scans yield unused body composition data due to unclear effects on skeletal muscle area (SMA), skeletal muscle index (SMI), and muscle density (SMD).

OBJECTIVES

This study investigates whether weight-adjusted IVC influences SMA, SMI, and SMD differently in females and males compared with noncontrast abdominal CT. In addition, the study explores associations between contrast and noncontrast-assessed SMA, SMI, SMD, and demographic factors.

METHODS

A comparative observational retrospective study was conducted on Danish patients who underwent consecutive 4-phased contrast-enhanced abdominal CT scans (noncontrast, arterial, venous, and late venous phases). Muscle measures were evaluated using validated semiautomated threshold-based software by 3 independent raters.

RESULTS

The study included 72 patients (51 males and 21 females) with a mean age of 59 (55 and 62) y. Weight-adjusted IVC increased SMA by ≤3.28 cm2 (95% confidence interval [CI]: 2.58, 3.98) corresponding to 2.4% (1.8, 2.9) in the late venous phase compared with noncontrast CT. Analysis between sexes showed no difference in the effects of IVC on SMA and SMI between females and males. However, females exhibited a higher increase in SMD during the venous by a mean of 1.7 HU (0.9; 2.5) and late venous phases with a mean HU of 1.80 (1.0; 2.6) compared with males. Multivariate regression analysis indicated an association between the differences in SMD and sex during venous (-1.38, 95% CI: -2.48, -0.48) and late venous phases (-1.23, 95% CI: -2.27, -0.19).

CONCLUSIONS

Weight-adjusted IVC leads to increased SMA, SMI, and SMD. Although SMA and SMI differences were consistent across the sexes, females exhibited a significantly higher SMD increase than males in the venous and late venous phases. Further investigations are necessary to determine the applicability of SMD as a muscle quality proxy in IVC CT scans.

Skeletal muscle density performance for screening frailty in older adults with cancer and the impact of diabetes: The CARE Registry

INTRODUCTION

Skeletal muscle density (SMD) measurements from imaging scans identify myosteatosis and could screen patients for geriatric assessment. We assessed SMD performance as a screening tool to identify older adults with cancer likely to be frail and who could benefit from in-depth assessment; we compared performance by sex and diabetes status.

MATERIALS AND METHODS

We analyzed patients in the Cancer & Aging Resilience Evaluation (CARE) Registry. Frailty and diabetes were captured using a patient-reported geriatric assessment (CARE tool). Frailty was defined using CARE frailty index (CARE-FI) based on principles of deficit accumulation. SMD was calculated from computed tomography scans (L3 vertebrae). Analyses were conducted by sex and diabetes status. Scatterplots and linear regression described crude associations between SMD and frailty score. Classification performance (frail vs. non-frail) was analyzed with (1) area under the receiver operating characteristic curves (AUC) and confidence intervals (CIs); and (2) sensitivity/specificity for sex-specific SMD quartile cut-offs (Q1, median, Q3). Performance was compared between patients with and without diabetes using differences and estimated CIs (2000 bootstrap replicates). We additionally calculated positive and negative likelihood ratios (LR+, LR-).

RESULTS

The analytic cohort included 872 patients (39% female, median age 68 years, 27% with diabetes) with predominately stage III/IV gastrointestinal cancer; >60% planning to initiate first-line chemotherapy. SMD was negatively associated with frailty score; models were best fit in male patients with diabetes. AUC estimates for female (range: 0.58-0.62) and male (0.58-0.68) patients were low. Q3 cut-offs had high sensitivity (range: 0.76-0.89), but poor specificity (0.25-0.34). Diabetes did not impact estimates for female patients. Male patients with diabetes had greater sensitivity estimates compared to those without (sensitivity differences: 0.23 [0.07, 0.38], 0.08 [-0.07, 0.24], and 0.11 [0.00, 0.22] for Q1, median, Q3, respectively). LR estimates were most notable for male patients with diabetes (LR+ = 2.92, Q1 cut-off; LR- = 0.46, Q3 cut-off).

DISCUSSION

Using SMD alone to screen older patients for geriatric assessment requires improvement. High-sensitivity cut-off points could miss 11-24% of patients with frailty, and many non-frail patients may be flagged. Screening with SMD is practical but work is needed to understand clinical andresource impacts of different cut-off points. Future research should evaluate performance with additional clinical data and in subgroups.

Prognostic Value of Artificial Intelligence-Driven, Computed Tomography-Based, Volumetric Assessment of the Volume and Density of Muscle in Patients With Colon Cancer

OBJECTIVE

The prognostic value of the volume and density of skeletal muscles in the abdominal waist of patients with colon cancer remains unclear. This study aimed to investigate the association between the automated computed tomography (CT)-based volume and density of the muscle in the abdominal waist and survival outcomes in patients with colon cancer.

MATERIALS AND METHODS

We retrospectively evaluated 474 patients with colon cancer who underwent surgery with curative intent between January 2010 and October 2017. Volumetric skeletal muscle index and muscular density were measured at the abdominal waist using artificial intelligence (AI)-based volumetric segmentation of body composition on preoperative pre-contrast CT images. Patients were grouped based on their skeletal muscle index (sarcopenia vs. not) and muscular density (myosteatosis vs. not) values and combinations (normal, sarcopenia alone, myosteatosis alone, and combined sarcopenia and myosteatosis). Postsurgical disease-free survival (DFS) and overall survival (OS) were analyzed using univariable and multivariable analyses, including multivariable Cox proportional hazard regression.

RESULTS

Univariable analysis showed that DFS and OS were significantly worse for the sarcopenia group than for the non-sarcopenia group (P = 0.044 and P = 0.003, respectively, by log-rank test) and for the myosteatosis group than for the non-myosteatosis group (P < 0.001 by log-rank test for all). In the multivariable analysis, the myosteatotic muscle type was associated with worse DFS (adjusted hazard ratio [aHR], 1.89 [95% confidence interval, 1.25-2.86]; P = 0.003) and OS (aHR, 1.90 [95% confidence interval, 1.84-3.04]; P = 0.008) than the normal muscle type. The combined muscle type showed worse OS than the normal muscle type (aHR, 1.95 [95% confidence interval, 1.08-3.54]; P = 0.027).

CONCLUSION

Preoperative volumetric sarcopenia and myosteatosis, automatically assessed from pre-contrast CT scans using AI-based software, adversely affect survival outcomes in patients with colon cancer.