Key result
Semiautomated body composition segmentation of abdominal CT images showed very low operator dependability, with all interobserver ICCs ≥ 0.938 and intraobserver ICCs ≥ 0.996.
Why the study?
CT-based body composition provides quantitative data relevant to diseases like type 2 diabetes mellitus and cancer, but whether multiple observers can interchangeably perform semiautomated segmentation was unclear.
Observational (n=132)
Blinded to other observers and previous results
No
Effect estimate: ICC ≥ 0.938
Semiautomated body composition segmentation of abdominal CT images has very low operator dependability and can be interchangeably performed by trained non-radiologists with highly reproducible results.
High reproducibility supports multi-observer use in research settings; leaves open clinical adoption for body composition monitoring.
BACKGROUND: Segmentation of computed tomography (CT) images provides quantitative data on body tissue composition, which may greatly impact the development and progression of diseases such as type 2 diabetes mellitus and cancer. We aimed to evaluate the inter- and intraobserver variation of semiautomated segmentation, to assess whether multiple observers may interchangeably perform this task. METHODS: Anonymised, unenhanced, single mid-abdominal CT images were acquired from 132 subjects from two previous studies. Semiautomated segmentation was performed using a proprietary software package. Abdominal muscle compartment (AMC), inter- and intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were identified according to pre-established attenuation ranges. The segmentation was performed by four observers: an oncology resident with extensive training and three radiographers with a 2-week training programme. To assess interobserver variation, segmentation of each CT image was performed individually by two or more observers. To assess intraobserver variation, three of the observers did repeated segmentations of the images. The distribution of variation between subjects, observers and random noise was estimated by a mixed effects model. Inter- and intraobserver correlation was assessed by intraclass correlation coefficient (ICC). RESULTS: For all four tissue compartments, the observer variations were far lower than random noise by factors ranging from 1.6 to 3.6 and those between subjects by factors ranging from 7.3 to 186.1. All interobserver ICC was ≥ 0.938, and all intraobserver ICC was ≥ 0.996. CONCLUSIONS: Body composition segmentation showed a very low level of operator dependability. Multiple observers may interchangeably perform this task with highly reproducible results.
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Kjønigsen et al. (2019) conducted an observational in Healthy and Type 2 Diabetes Mellitus (n=132). Semiautomated body composition segmentation vs. Between multiple observers was evaluated on Inter- and intraobserver variation assessed by intraclass correlation coefficient (ICC) (ICC ≥ 0.938). Semiautomated body composition segmentation of abdominal CT images showed very low operator dependability, with all interobserver ICCs ≥ 0.938 and intraobserver ICCs ≥ 0.996.
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