Graduate School of Health Sciences, Hokkaido University, Sapporo, Japan.
Department of Radiological Technology, Kurashiki Central Hospital, Kurashiki, Okayama, Japan.
J Appl Clin Med Phys. 2023 Aug;24(8):e14080. doi: 10.1002/acm2.14080. Epub 2023 Jun 19.
Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning-based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management.
Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients. A deep learning model was developed to predict body weight from scout images. The correlation between actual and predicted body weight was analyzed. To validate the use of predicted body weight in radiation dose management, the volume CT dose index (CTDI ) and the dose-length product (DLP) were compared between the body weight subgroups based on actual and predicted body weight. Surrogate size-specific dose estimates (SSDEs) acquired from actual and predicted body weight were compared to the reference standard.
The median actual and predicted body weight were 64.1 (interquartile range: 56.5-72.4) and 64.0 (56.3-72.2) kg, respectively. There was a strong correlation between actual and predicted body weight (ρ = 0.892, p < 0.001). The CTDI and DLP of the body weight subgroups were similar based on actual and predicted body weight (p < 0.001). Both surrogate SSDEs based on actual and predicted body weight were not significantly different from the reference standard (p = 0.447 and 0.410, respectively).
Predicted body weight can be an alternative to actual body weight in managing dose metrics and simplifying SSDE calculation. Our proposed method can be useful for CT radiation dose management in adult patients with unknown body weight.
准确的体重测量对于促进计算机断层扫描（CT）剂量优化至关重要；然而，在 CT 检查前，体重并非总是可以测量的，尤其是在急诊环境中。本研究旨在探讨基于胸部 CT 扫描图像的深度学习体重是否可以替代 CT 辐射剂量管理中的实际体重。
从 3601 名接受体检的患者中收集了胸部 CT 扫描图像和诊断图像。开发了一种深度学习模型，用于从扫描图像预测体重。分析了实际体重与预测体重之间的相关性。为了验证预测体重在辐射剂量管理中的应用，根据实际体重和预测体重将体重亚组的体积 CT 剂量指数（CTDI）和剂量长度乘积（DLP）进行了比较。比较了来自实际和预测体重的替代体型特异性剂量估计（SSDE）与参考标准。
实际体重和预测体重的中位数分别为 64.1（四分位距：56.5-72.4）和 64.0（56.3-72.2）kg，两者之间存在很强的相关性（ρ=0.892，p<0.001）。根据实际体重和预测体重，体重亚组的 CTDI 和 DLP 相似（p<0.001）。基于实际体重和预测体重的替代 SSDE 与参考标准无显著差异（p=0.447 和 0.410，分别）。
在管理剂量指标和简化 SSDE 计算方面，预测体重可以替代实际体重。我们提出的方法可用于管理未知体重的成年患者的 CT 辐射剂量。
