Su Ningling, Hou Fan, Zheng Wen, Wu Zhifeng, E Linning
From the Department of Radiology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University.
Institute of Public-Safety and Big Data, College of Data Science, Taiyuan University of Technology, Taiyuan.
J Comput Assist Tomogr. 2023;47(5):738-745. doi: 10.1097/RCT.0000000000001484. Epub 2023 Jul 7.
This study aimed to develop a computed tomography (CT)-based deep learning model for assessing the severity of patients with connective tissue disease (CTD)-associated interstitial lung disease (ILD).
The retrospective study included 298 CTD-ILD patients between January 2018 and May 2022. A deep learning-based RDNet model was established (1610 fully annotated CT images for training and 402 images for validation). The model was used to automatically classify and quantify 3 radiologic features (ground glass opacities [GGOs], reticulation, and honeycombing), along with a volumetric sum of 3 areas (ILD%). As a control, we used 4 previously defined CT threshold methods to calculate the ILD assessment index. The Spearman rank correlation coefficient ( r ) evaluated the correlation between various indicators and the lung function index in the remaining 184 CTD-ILD patients who were staged according to the gender-age-physiology (GAP) system.
The RDNet model accurately identified GGOs, reticulation, and honeycombing, with corresponding Dice indexes of 0.784, 0.782, and 0.747, respectively. A total of 137 patients were at GAP1 (73.9%), 36 patients at GAP2 (19.6%), and 11 patients at GAP3 (6.0%). The percentages of reticulation and honeycombing at GAP2 and GAP3 were markedly elevated compared with those at GAP1 ( P < 0.001). The percentage of GGOs was not significantly different among the GAP stages ( P = 0.62). As the GAP stage increased, all lung function indicators tended to decrease, and the composite physiologic index (CPI) indicated an upward tendency. The percentage of honeycombs moderately correlated with the percentage of diffusing capacity of the lung for carbon monoxide (DLco%) ( r = -0.58, P < 0.001) and CPI ( r = 0.63, P < 0.001). The ILD assessment index calculated by the CT threshold method (-260 to -600 Hounsfield units) had a low correlation with DLco% and CPI (DLco%: r = -0.42, P < 0.001; CPI: r = 0.45, P < 0.001).
The RDNet model can quantify GGOs, reticulation, and honeycombing of chest CT images in CTD-ILD patients, among which honeycombing had the most significant effect on lung function indicators. In addition, this model provided good clinical utility for evaluating the severity of CTD-ILD.
本研究旨在开发一种基于计算机断层扫描（CT）的深度学习模型，用于评估结缔组织病（CTD）相关间质性肺疾病（ILD）患者的病情严重程度。
这项回顾性研究纳入了2018年1月至2022年5月期间的298例CTD-ILD患者。建立了基于深度学习的RDNet模型（1610张带有完整标注的CT图像用于训练，402张用于验证）。该模型用于自动分类和量化3种放射学特征（磨玻璃影[GGOs]、网状影和蜂窝状影），以及3个区域的体积总和（ILD%）。作为对照，我们使用4种先前定义的CT阈值方法来计算ILD评估指数。Spearman等级相关系数（r）评估了其余184例根据性别-年龄-生理学（GAP）系统进行分期的CTD-ILD患者中各种指标与肺功能指标之间的相关性。
RDNet模型能够准确识别GGOs、网状影和蜂窝状影，其相应的Dice指数分别为0.784、0.782和0.747。共有137例患者处于GAP1期（73.9%），36例处于GAP2期（19.6%），11例处于GAP3期（6.0%）。与GAP1期相比，GAP2期和GAP3期的网状影和蜂窝状影百分比显著升高（P<0.001）。GGOs百分比在GAP各期之间无显著差异（P=0.62）。随着GAP分期增加，所有肺功能指标均呈下降趋势，而综合生理指数（CPI）呈上升趋势。蜂窝状影百分比与一氧化碳弥散量百分比（DLco%）（r=-0.58，P<0.001）和CPI（r=0.63，P<0.001）呈中度相关。通过CT阈值方法（-260至-600亨氏单位）计算的ILD评估指数与DLco%和CPI的相关性较低（DLco%：r=-0.42，P<0.001；CPI：r=0.45，P<0.001）。
RDNet模型能够量化CTD-ILD患者胸部CT图像中的GGOs、网状影和蜂窝状影，其中蜂窝状影对肺功能指标的影响最为显著。此外，该模型在评估CTD-ILD病情严重程度方面具有良好的临床实用性。
