Ying Zhoumeng, Zhu Zhenchen, Hu Ge, Pan Zhengsong, Tan Weixiong, Han Wei, Wu Zifeng, Zhou Zhen, Wang Jinhua, Song Wei, Song Lan, Jin Zhengyu
Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Front Med (Lausanne). 2024 Sep 30;11:1435337. doi: 10.3389/fmed.2024.1435337. eCollection 2024.
Given the high prevalence of fibrotic interstitial lung abnormalities (ILAs) post-COVID-19, this study aims to evaluate the effectiveness of quantitative CT features in predicting fibrotic ILAs at 3-month follow-up.
This retrospective study utilized cohorts from distinct clinical settings: the training dataset comprised individuals presenting at the fever clinic and emergency department, while the validation dataset included patients hospitalized with COVID-19 pneumonia. They were classified into fibrotic group and nonfibrotic group based on whether the fibrotic ILAs were present at follow-up. A U-Net-based AI tool was used for quantification of both pneumonia lesions and pulmonary blood volumes. Receiver operating characteristic (ROC) curve analysis and multivariate analysis were used to assess their predictive abilities for fibrotic ILAs.
Among the training dataset, 122 patients (mean age of 68 years ±16 [standard deviation], 73 men), 55.74% showed fibrotic ILAs at 3-month follow-up. The multivariate analysis identified the pneumonia volume [PV, odd ratio (OR) 3.28, 95% confidence interval (CI): 1.20-9.31, = 0.02], consolidation volume (CV, OR 3.77, 95% CI: 1.37-10.75, = 0.01), ground-glass opacity volume (GV, OR 3.38, 95% CI: 1.26-9.38, = 0.02), pneumonia mass (PM, OR 3.58, 95% CI: 1.28-10.46, = 0.02), and the CT score (OR 12.06, 95% CI: 3.15-58.89, < 0.001) as independent predictors of fibrotic ILAs, and all quantitative parameters were as effective as CT score (all > 0.05). And the area under the curve (AUC) values were PV (0.79), GV (0.78), PM (0.79), CV (0.80), and the CT score (0.77). The validation dataset, comprising 45 patients (mean age 67.29 ± 14.29 years, 25 males) with 57.78% showing fibrotic ILAs at follow-up, confirmed the predictive validity of these parameters with AUC values for PV (0.86), CV (0.90), GV (0.83), PM (0.88), and the CT score (0.85). Additionally, the percentage of blood volume in vessels <5mm relative to the total pulmonary blood volume (BV5%) was significantly lower in patients with fibrotic ILAs ( = 0.048) compared to those without.
U-Net based quantification of pneumonia lesion and BV5% on baseline CT scan has the potential to predict fibrotic ILAs at follow-up in COVID-19 patients.
鉴于新冠病毒感染后纤维化间质性肺异常(ILA)的高发病率,本研究旨在评估定量CT特征在预测3个月随访时纤维化ILA的有效性。
这项回顾性研究使用了来自不同临床环境的队列:训练数据集包括在发热门诊和急诊科就诊的个体,而验证数据集包括因新冠病毒肺炎住院的患者。根据随访时是否存在纤维化ILA,将他们分为纤维化组和非纤维化组。基于U-Net的人工智能工具用于量化肺炎病变和肺血容量。采用受试者操作特征(ROC)曲线分析和多变量分析来评估它们对纤维化ILA的预测能力。
在训练数据集中,122例患者(平均年龄68岁±16[标准差],73例男性),55.74%在3个月随访时出现纤维化ILA。多变量分析确定肺炎体积[PV,比值比(OR)3.28,95%置信区间(CI):1.20-9.31,P=0.02]、实变体积(CV,OR 3.77,95%CI:1.37-10.75,P=0.01)、磨玻璃影体积(GV,OR 3.38,95%CI:1.26-9.38,P=0.02)、肺炎肿块(PM,OR 3.58,95%CI:1.28-10.46,P=0.02)和CT评分(OR 12.06,95%CI:3.15-58.89,P<0.001)为纤维化ILA的独立预测因素,所有定量参数与CT评分一样有效(所有P>0.05)。曲线下面积(AUC)值分别为PV(0.79)、GV(0.78)、PM(0.79)、CV(0.80)和CT评分(0.77)。验证数据集包括45例患者(平均年龄67.29±14.29岁,25例男性),57.78%在随访时出现纤维化ILA,证实了这些参数的预测有效性,PV、CV、GV、PM和CT评分的AUC值分别为0.86、0.90、0.83、0.88和0.85。此外,与无纤维化ILA的患者相比,纤维化ILA患者中直径<5mm血管内血容量占总肺血容量的百分比(BV5%)显著降低(P=0.048)。
基于U-Net对基线CT扫描的肺炎病变和BV5%进行量化,有可能预测新冠病毒感染患者随访时的纤维化ILA。