Objective comparison of commonly used computed tomography body composition analysis software

Postoperative skeletal muscle loss as a prognostic indicator of clinical outcomes in patients with gastric cancer: a systematic review and meta-analysis

BACKGROUND

A decrease in skeletal muscle mass is frequently observed during the perioperative period in patients with gastric cancer (GC) and is strongly associated with postoperative complications and poor long-term survival outcomes. Relevant research indicates that a certain proportion of patients with GC experience skeletal muscle mass loss after surgery. Therefore, a systematic review and meta-analysis was conducted to assess postoperative skeletal muscle loss as a prognostic marker for clinical outcomes in patients with GC.

METHODS

PubMed, Web of Science, and Embase were used to systematically screen and retrieve relevant studies according to strictly established inclusion and exclusion criteria. Further analysis of the effect of postoperative skeletal muscle loss on long-term survival metrics (including overall survival [OS], recurrence-free survival [RFS], disease-free survival [DFS], and disease-specific survival [DSS]) in patients with GC was performed.

RESULTS'

From 10 studies and 11 related publications, more than 3764 patients with GC were identified. Severe postoperative skeletal muscle loss occurred in 25.7% of patients with GC and was significantly correlated with poorer OS (hazard ratio [HR], 2.27; 95% CI, 1.58-3.25; P <.00001), RFS (HR, 2.96; 95% CI, 1.47-5.97; P =.002), DFS (HR, 2.41; 95% CI, 1.17-4.97; P =.02), and DSS (HR, 4.12; 95% CI, 2.44-6.94; P <.00001). When postoperative skeletal muscle loss advanced to sarcopenia, patients had worse OS as well (HR, 2.22; 95% CI, 1.49-3.30; P <.0001).

CONCLUSION

Patients who undergo radical surgery for GC often experience skeletal muscle mass loss, and significant skeletal muscle mass loss is associated with poorer survival outcomes. Identifying patients with significant skeletal muscle mass loss during follow-up and promptly providing tailored interventions, such as nutritional and exercise support, are essential.

CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans

BACKGROUND

Body composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.

METHODS

A retrospective dataset of 205 contrast-enhanced thoraco-abdominal CT examinations was used for training, while 54 scans from a publicly available dataset were used for independent testing. Two radiology residents performed manual segmentation, identifying the centers of the L1 and L3 vertebrae and segmenting the corresponding axial slices. MultiResUNet was used to identify CT slices intersecting the L1 and L3 vertebrae, and its performance was evaluated using the mean absolute error (MAE). Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA's performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland-Altman analyses.

RESULTS

On the independent dataset, CompositIA achieved a MAE of about 5 mm in detecting slices intersecting the L1 and L3 vertebrae, with a MAE < 10 mm in at least 85% of cases and a vDSC greater than 0.85 in segmenting axial slices. Regression and Bland-Altman analyses demonstrated a strong linear relationship and good agreement between automated and manual scores (p values < 0.001 for all indices), with mean PREs ranging from 5.13% to 15.18%.

CONCLUSION

CompositIA facilitated the automated quantification of body composition scores, achieving high precision in independent testing.

RELEVANCE STATEMENT

CompositIA is an automated, open-source pipeline for quantifying body composition indices from CT scans, simplifying clinical assessments, and expanding their applicability.

KEY POINTS

Manual body composition assessment from CTs is time-consuming and prone to errors. CompositIA was trained on 205 CT scans and tested on 54 scans. CompositIA demonstrated mean percentage relative errors under 15% compared to manual indices. CompositIA simplifies body composition assessment through an artificial intelligence-driven and open-source pipeline.

A Cross-Sectional Validation of Horos and CoreSlicer Software Programs for Body Composition Analysis in Abdominal Computed Tomography Scans in Colorectal Cancer Patients

BACKGROUND

Body composition assessment using computed tomography (CT) scans may be hampered by software costs. To facilitate its implementation in resource-limited settings, two open-source segmentation programs (Horos and CoreSlicer) were transversally validated in colorectal cancer patients.

METHODS

Contrast-enhanced abdominal CT scans were analyzed following the Alberta protocol. The Cross-Sectional Area (CSA) and intensities of skeletal muscle tissue (MT), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intramuscular adipose tissue (IMAT) were measured. The Skeletal Muscle Index (SMI) was calculated. Cutoff points were applied to the SMI, MT intensity, and VAT CSA to define muscle atrophy, myosteatosis, and abdominal obesity. The inter-software agreement was evaluated using different statistical tools.

RESULTS

A total of 68 participants were measured. The MT CSA and SMI displayed no differences. The MT CSA agreement was excellent, and both programs provided equal muscle atrophy prevalences. CoreSlicer underestimated the MT intensity, with a non-significant myosteatosis prevalence increase (+5.88% and +8.82%) using two different operative definitions. CoreSlicer overestimated the CSA and intensity in both VAT and SAT, with a non-significant increase (+2.94%) in the abdominal obesity prevalence.

CONCLUSIONS

Both software programs were feasible tools in the study group. The MT CSA showed great inter-software agreement and no muscle atrophy misdiagnosis. Segmentation differences in the MT intensity and VAT CSA caused limited diagnostic misclassification in the study sample.