Park Hyungin, Hwang Eui Jin, Goo Jin Mo
From the Department of Radiology, Seoul National University Hospital, Seoul, South Korea (H.P., E.J.H., J.M.G.); and Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea (J.M.G.).
Invest Radiol. 2024 Mar 1;59(3):278-286. doi: 10.1097/RLI.0000000000001003.
The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.
This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models.
The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status.
The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.
本研究旨在确定基于深度学习的内核适配后的低剂量计算机断层扫描（LDCT）定量肺气肿对长期死亡率的预测价值。
这项回顾性研究调查了2009年2月至2016年12月期间在健康检查中从60岁及以上无症状个体获得的LDCT。这些LDCT使用1或1.25毫米的切片厚度以及高频内核进行重建。一种能够生成类似于标准剂量和低频内核图像的CT图像的深度学习算法被应用于这些LDCT。为了量化肺气肿，在进行内核适配前后测量衰减值小于或等于-950亨氏单位（LAA-950）的肺体积百分比。根据 Fleischner 学会声明，LAA-950超过6%的低剂量胸部CT被视为肺气肿阳性。生存数据来自2021年底的国家登记数据库。使用多变量Cox比例风险模型根据肺气肿量化结果探讨非意外死亡风险，不包括受伤或中毒等原因。
该研究包括5178名参与者（平均年龄±标准差，66±3岁；3110名男性）。在内核适配后，LAA-950的中位数（18.2%对2.6%）和LAA-950超过6%的LDCT比例（96.3%对39.3%）显著下降。内核适配前的肺气肿量化与非意外死亡风险之间没有关联。然而，在内核适配后，在调整年龄、性别和吸烟状况后，较高的LAA-950（每增加1%的风险比，1.01；P = 0.045）和LAA-950超过6%（风险比，1.36；P = 0.008）成为非意外死亡的独立预测因素。
深度学习在内核适配中的应用被证明有助于在LDCT上量化肺气肿，成为无症状个体长期非意外死亡率的潜在预测工具。
