Tandon Damini, Sletten Arthur, Ha Austin, Skolnick Gary B, Commean Paul, Myckatyn Terence
From the Division of Plastic and Reconstructive Surgery, Washington University School of Medicine in Saint Louis, St. Louis, MO.
Department of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Plast Reconstr Surg Glob Open. 2025 Jan 10;13(1):e6413. doi: 10.1097/GOX.0000000000006413. eCollection 2025 Jan.
We present an approach for evaluating abdominal computed tomography (CT) scans that generates reproducible measures relevant to donor site morbidity after abdominally based breast reconstruction. Seventeen preoperative CT metrics were measured in 20 patients with software: interanterior superior iliac spine distance; abdominal wall protrusion; interrectus distance; rectus abdominis width, thickness, and width-to-thickness ratio; abdominal wall thickness; subcutaneous fat volume; visceral fat volume; right/left psoas volumes and densities; and right/left rectus abdominis volumes and densities. Two operators performed measures to determine interrater reliability (n = 10). Interclass coefficients (ICCs) were calculated, and Bland-Altman plots were fashioned. Intrarater reliability was excellent (ICC > 0.9, 0.958-1) for 15 measures, and good (0.75 < ICC < 0.9, 0.815-0.853) for 2 measures. Interrater reliability was excellent (ICC > 0.9, 0.912-0.995) for 12 measures and good (0.75 < ICC < 0.9, 0.78-0.896) for 5 measures. Bland-Altman plots confirmed intra/interrater agreement. Our study meets its objective of establishing a protocol for obtaining abdominal CT measurements with high reproducibility and intrarater and interrater reliability. Although this study is not meant to weigh the particular influences of various CT measurements on clinical outcomes, we are now actively studying this with the intention of reporting our findings in the near future. Larger patient cohorts must be leveraged to determine correlations between abdominal CT scan findings and donor site outcomes using machine learning algorithms that generate models for predicting abdominal donor site complications.
我们提出了一种评估腹部计算机断层扫描（CT）的方法，该方法可生成与腹部乳房重建术后供区并发症相关的可重复测量指标。使用软件对20例患者的17项术前CT指标进行了测量：髂前上棘间距；腹壁突出度；腹直肌间距；腹直肌宽度、厚度及宽厚比；腹壁厚度；皮下脂肪体积；内脏脂肪体积；左右腰大肌体积和密度；以及左右腹直肌体积和密度。两名操作人员进行测量以确定评分者间信度（n = 10）。计算组内相关系数（ICC）并绘制Bland-Altman图。15项测量的评分者内信度极佳（ICC > 0.9，0.958 - 1），2项测量的评分者内信度良好（0.75 < ICC < 0.9，0.815 - 0.853）。12项测量的评分者间信度极佳（ICC > 0.9，0.912 - 0.995），5项测量的评分者间信度良好（0.75 < ICC < 0.9，0.78 - 0.896）。Bland-Altman图证实了评分者内/间的一致性。我们的研究达到了其目标，即建立一种具有高重复性以及评分者内和评分者间信度的腹部CT测量方案。尽管本研究并非旨在衡量各种CT测量对临床结果的具体影响，但我们目前正在积极研究这一问题，打算在不久的将来报告我们的研究结果。必须利用更大的患者队列，使用生成预测腹部供区并发症模型的机器学习算法来确定腹部CT扫描结果与供区结果之间的相关性。
