Quantification of Chest Wall Asymmetry in Healthy Females Using Standardized Computed Tomography-Derived Curve Modeling: A Proof of Concept

利用标准化计算机断层扫描衍生曲线模型量化健康女性胸壁不对称性:概念验证

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Abstract

BACKGROUND: Anterior chest asymmetry is common, with potential implications for aesthetic and reconstructive breast surgery. However, current assessments rely on visual inspection or linear measurements, and few studies offer a reproducible, quantitative framework for analyzing skeletal asymmetry in healthy populations. OBJECTIVES: The aim of the study is to develop and evaluate a proof-of-concept methodology for quantifying anterior thoracic skeletal asymmetry using standardized, computed tomography (CT)-derived curve segmentation and polynomial modeling. METHODS: Chest CT scans from 50 female patients aged 18 to 45 with a BMI of 20 to 25 were evaluated using semi-automated segmentation. The right and left chest wall curvatures were extracted in both a horizontal and vertical plane at the level of the fourth rib insertion and one-fourth of the maximum thorax width, respectively. RESULTS: The left chest wall displayed significantly greater outward projection than the right chest in both planes (P < .001). The most pronounced differences were observed from 2 to 5 cm from the sternum in the horizontal plane and 0 to 1 cm from the manubrium in the vertical plane. Individual assessments showed that >65% of patients exhibited leftward prominence in the horizontal and vertical planes. The estimated mean volume discrepancy between the left and right chest walls was 19.3 cc. CONCLUSIONS: This study introduces a reproducible, curve-based methodology for quantifying skeletal chest wall asymmetry using CT images. In this healthy female cohort, a statistically significant leftward asymmetry was common. Although the volumetric difference may be small and not clinically significant in isolation, these findings support individualized skeletal assessment as a basis for surgical planning, warranting future studies with 3D modeling and more diverse populations.

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