ISSN 1004-4140
CN 11-3017/P

人工智能定量分析新型冠状病毒感染不同病毒变异株CT特征

张凯, 柴军, 刘瑞, 赵建华, 王璟琛

张凯, 柴军, 刘瑞, 等. 人工智能定量分析新型冠状病毒感染不同病毒变异株CT特征[J]. CT理论与应用研究, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043.
引用本文: 张凯, 柴军, 刘瑞, 等. 人工智能定量分析新型冠状病毒感染不同病毒变异株CT特征[J]. CT理论与应用研究, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043.
ZHANG K, CHAI J, LIU R, et al. Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence[J]. CT Theory and Applications, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043. (in Chinese).
Citation: ZHANG K, CHAI J, LIU R, et al. Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence[J]. CT Theory and Applications, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043. (in Chinese).

人工智能定量分析新型冠状病毒感染不同病毒变异株CT特征

基金项目: 内蒙古自治区卫生健康科技计划项目(超高分辨率 CT 靶扫描技术联合低剂量对诊断亚实性肺结节的价值(202201015))。
详细信息
    作者简介:

    张凯: 男,硕士,内蒙古自治区人民医院影像医学科主治医师,主要研究方向胸部影像学,E-mail:28132311@qq.com

    通讯作者:

    柴军: 内蒙古自治区人民医院主任医师、硕士研究生导师,主要从事胸、腹部影像学研究及CT引导下穿刺术等,E-mail:amaschai@126.com

  • 中图分类号: O  242;R  814;R  563.1

Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence

  • 摘要: 目的:人工智能(AI)定量分析比较新型冠状病毒感染德尔塔(Delta)和奥密克戎(Omicron)变异株感染患者的胸部CT影像学特征。方法:回顾性分析2022年2月20日至2022年4月19日在内蒙古自治区第四医院确诊的294例新型冠状病毒Delta变异株感染患者及2022年12月1日至12月30日在内蒙古自治区人民医院确诊的222例Omicron变异株感染患者的临床资料及首次CT影像学资料进行分析,分为Delta组和Omicron组,应用推想预测肺部感染辅助诊断软件进行定量计算,比较分析组间CT影像学征象及CT定量数据。结果:磨玻璃斑片影、磨玻璃结节影、索条、实变、铺路石征、小叶间隔增厚及病灶内增粗血管影等影像学征象在两组之间比较无统计学意义。Omicron组病灶分布较Delta组更容易出现沿支气管血管束分布;Delta组的全肺病灶体积、体积占比、右肺中叶病灶体积、体积占比、右肺下叶病灶体积、体积占比均高于Omicron组;Delta组患者病灶分布于 -570~-470 HU体积、-470~-370 HU体积、-370~-270 HU体积、-270~-170 HU体积均高于Omicron组。结论:Delta变异株感染患者肺炎早期CT病灶体积及体积占比Omicron组高,Omicron组病灶分布较Delta组更容易出现不典型的沿支气管血管束分布,人工智能肺炎辅助诊断系统对COVID-19患者定量评估肺炎感染区域体积及体积占比,为患者病情评估提供客观的参考数据。
    Abstract: Objective: To quantitatively analyze and compare the chest computed tomography (CT) imaging features of patients infected with delta and omicron variants of COVID-19 using artificial intelligence (AI). Method: The clinical data of 294 patients infected with the novel coronavirus delta variant diagnosed at the Fourth Hospital of Inner Mongolia Autonomous Region from February 20, 2022 to April 19, 2022 and 222 patients infected with the omicron variant diagnosed at the People's Hospital of Inner Mongolia Autonomous Region from December 1, 2022 to December 30, 2022 were retrospectively analyzed. CT imaging data were analyzed and divided into delta and omicron groups. Quantitative calculation was performed using deductive predictive pulmonary infection auxiliary diagnostic software, and CT imaging signs and quantitative CT data between groups were compared and analyzed. Results: No statistical significance was noted between the delta and omicron groups in imaging signs, such as ground-glass opacity, ground-glass nodule, cord-like lesion, consolidation, paving stone sign, thickened interlobular septum, and thickened vessels in the lesion. The distribution of lesions along the bronchial vascular bundle was more likely in the omicron than in the delta group. The total lung lesion volume, volume proportion, right middle lobe lesion volume, volume proportion, right inferior lobe lesion volume, and volume proportion in the delta group were higher than those in the omicron group. The proportions of lesions in the delta group in −570 ~ −470 HU, −470 ~ −370 HU, −370 ~ −270 HU, and −270 ~ −170 HU volumes were higher than those in the omicron group. Conclusion: In the early stage of COVID-19, the volume of CT lesions in the patients infected with the delta variant was higher than that in the omicron group, and the distribution of lesions in the omicron group was more likely to have atypical distribution along the bronchial vascular bundle than that in the delta group. The volume and volume proportion of the pneumonia-infected area in patients with COVID-19 were quantitatively evaluated using the AI-assisted diagnosis system for COVID-19 to provide objective reference data for patients' condition assessment.
  • 骨质疏松症(osteoporosis,OP)是以骨密度减低和骨脆性增加为特征的代谢性骨病,易发生脆性骨折,具有较高的发病率和死亡率。近年来,随着我国人口老龄化,OP的流行率稳步增加。OP相关的脆性骨折具有高住院率、高护理费用和养老院依赖性,已经成为人们普遍关注的一个主要公共卫生问题[1]。尽管OP具有公共性和严重性,仍然存在诊断和治疗不足的问题。

    双能X射线吸收仪(dual energy X-ray absorptiometry,DEXA)测量骨密度(bone mineral density,BMD)是目前公认的诊断OP参考标准,推荐所有65岁以上的女性和所有70岁以上的男性进行DEXA检查。目前只有不到33%的女性和5%的男性脆性骨折患者接受DEXA评估[2],而且,DEXA不能区分骨皮质与骨松质,检查结果易受到椎体骨质增生,椎体周围血管钙化的影响,导致骨密度假性增高。定量CT(quantitative computed tomography,QCT)检查也是BMD检查的常用方法,能够分别测量椎体骨松质及骨皮质的BMD,测量结果不受骨质增生及血管钙化的影响,缺点为需要专门的测量软件,单独进行BMD测量辐射剂量较高,使其在基层医院的应用受到限制。近年来,对由于其他适应症而进行CT扫描的图像进行BMD分析作为OP的机会筛查手段得到了人们的关注,CT检查提供的OP机会性筛查的优点是对患者没有额外的X线辐射及费用。以DEXA检查结果为标准,分析腰1椎体CT值预测骨质疏松的研究已有报道,但是,以QCT检查结果为标准,分析腰1椎体CT值预测骨质疏松的研究鲜见报道[3]

    本研究旨在分析腰1椎体CT值与QCT BMD的相关性并评价腰1椎体CT值对机会性筛查OP的可靠性及效能。

    回顾性分析2020年7月至2023年1月因其它临床适应症进行腹部CT平扫的1117例,患者年龄范围30~90岁,平均年龄(62.45±10.36)岁,其中,男583例,平均年龄(61.61±11.11)岁,女534例,平均年龄(63.37±9.39)岁。

    纳入标准:年龄范围30~90岁,采用120 kV的扫描条件。排除标准:包括椎体肿瘤,腰椎外伤、L1椎体骨折,椎体畸形,椎体内金属植入物,椎体成形术后,影响BMD的内分泌及代谢性疾病。本研究通过医院伦理委员会审查,因系回顾性研究,知情同意书被免除。

    使用飞利浦64排CT扫描仪(Incisive CT,飞利浦医疗系统)。采集参数如下:管电压120 kV,管电流260~300 mA,FOV 500×500 mm,矩阵512×512。CT图像重建采用标准重建算法,重建层厚为1 mm。

    将CT数据传输到PACS工作站,观察腰椎图像的窗宽1500,窗位300,测量L1、L2椎体CT值,在层厚为1 mm的轴位CT图像上选取椭圆形兴趣区(region of interest,ROI)分别位于椎体的中间平面、上部终板的下方层面和下部终板的上方层面的骨松质内,不包括骨岛、海绵状血管瘤及椎体后中央静脉走行区。取上述3个层面的CT值为椎体的平均CT值(图1)。为了评估测量方法的可重复性,自1117例患者中随机抽取30例,有2名医生在不同时间点(相隔2周)分别2次测量L1椎体CT值。

    图  1  L1椎体CT值测量
    Figure  1.  CT value measurements at L1 vertebra

    使用Mindways公司的QCT pro 5.0.3(QCT pro,Mindways Software,Inc.)非同步体模扫描技术进行QCT检查及测量,按QCT操作手册说明每天进行一次体模CT扫描并用图像分析系统进行指控分析。将CT数据传输到QCT工作站行BMD测量。椭圆形的ROI位于每个椎体(L1-L2)的中间平面的骨松质骨,不包括皮质骨、椎体后中央静脉走行区和任何硬化性病灶。

    根据骨质疏松的影像学与骨密度诊断专家共识[4],腰椎QCT诊断标准如下:取2个腰椎椎体(第1和第2腰椎)松质骨骨密度平均值,≥120 mg/cm3的骨密度为正常,介于80 mg/cm3和120 mg/cm3之间的骨密度为骨量减少,≤80 mg/cm3的骨密度为骨质疏松。

    使用SPSS 19.0统计软件进行分析。结果以$ (\bar x \pm s)$表示,采用单因素方差分析比较组间检测结果,满足方差齐性应用LSD多重比较,方差不齐应用Tamhance比较。应用Pearson相关性检验分析椎体CT值与腰椎BMD的相关性。绘制受试者工作特征(receiver operating characteristic,ROC)曲线,采用Youden指数确定预测骨质疏松和骨量正常的阈值,计算灵敏度、特异度及曲线下面积(area under the curve,AUC)评估椎体CT值对OP的预测性能。应用组内相关系数(intraclass correlation coefficient,ICC)分析CT测量的测量者内一致性和测量者间一致性,ICC值>0.8表示一致性可靠。应用Bland-Altman图分析CT值测量于QCT两者测量方法的一致性,以两者方法测量的BMD的平均值为横坐标,以二者的差值为纵坐标,并以差值的均数±1.96倍差值的标准差(SD)为一致性界限。P<0.05为差异有统计学意义。

    QCT腰椎BMD测量结果示,1117例患者中,骨量正常组267例,骨量减少组472例,骨质疏松组378例。L1和L2椎体平均CT值分别为(126.03±43.75)HU和(120.42±45.28)HU,OP组平均CT值分别为(80.40±20.99)HU和(75.25±30.42)HU,骨量减少组为(131.73±18.86)HU和(126.40±19.94)HU,骨量正常组为(180.24±28.85)HU和(173.82±27.61)HU,3组间CT值差异有统计学意义(表1)。

    表  1  3组患者腰椎CT值及BMD值比较
    Table  1.  Comparison of lumbar CT and BMD values among three groups of patients
    分组 OP组(378例) 骨量减少(472例) 正常组(267例) F p
    L1 CT值/HU 80.54±21.16 131.76±18.87 180.29±28.45 1598.663 0.000abc
    L1 BMD值/(mg/cm3 62.53±16.41 103.07±12.42 144.03±19.36 2136.773 0.000abc
    L2 CT值/HU 75.25±30.42 126.40±19.94 173.82±27.61 1167.846 0.000abc
    L2 BMD值/(mg/cm3 57.42±17.13 97.84±11.93 138.96±19.69 2088.040 0.000abc
    L1、L2平均CT值/HU 77.89±22.72 129.08±18.06 177.05±27.38 1593.007 0.000abc
    L1、L2平均BMD值/(mg/cm3 59.89±16.02 100.45±10.89 141.49±19.02 2360.898 0.000abc
    注:a为OP组与骨量减少组,b为骨量减少组与骨量正常组,c为OP组与骨量正常组。
    下载: 导出CSV 
    | 显示表格

    L1及L2椎体CT值与QCT测量的BMD值呈明显的正相关,相关系数分别为(r=0.956,r =0.902,P均<0.01)。L1椎体CT值与L1和L2的平均BMD值呈明显的正相关(r=0.954,P<0.01)(图2)。

    图  2  L1椎体CT值与L1及L2椎体平均BMD的相关性
    Figure  2.  Correlation between the L1 vertebral CT value and mean BMD of the L1 and L2 vertebral bodies

    L1椎体CT值测量具有较高的可靠性,同一测量者不同时间点测量L1椎体CT值的组内相关系数ICC=0.995(P<0.01;95% CI:0.990~0.998),两名测量者间的组间相关系数ICC=0.985(P<0.01;95% CI:0.968~0.993)。

    以QCT测量的L1椎体BMD结果为因变量,L1椎体CT值(HU)为变量,得出根据CT值预测BMD的线性回归方程为:BMD(QCT)=0.75 X(HU)+4.26。

    Pearson相关分析显示QCT测量的L1椎体BMD与由L1椎体CT值预测的BMD明显相关(r=0.956,P<0.01)。Bland-Altman图显示两种方法测量结果的平均差值非常接近于0,两种方法测量值的差异无统计学意义(图3)。

    图  3  QCT测量BMD与预测BMD的Bland-Altman图
    Figure  3.  Bland–Altman plot for the predicted and measured QCT values

    ROC曲线分析显示L1、L2椎体CT值及L1、L2椎体平均CT值区分骨质疏松与非骨质疏松(骨量正常+骨量减少)的阈值分别为106.33 HU、106.33 HU和105.83 HU,AUC值分别为0.982、0.977和0.984(表2图4)。L1、L2椎体CT值及L1、L2椎体平均CT值区分骨量正常和低BMD(骨量减少+骨质疏松)的阈值分别为149.33 HU,144.00 HU和148.33 HU,AUC值分别为0.969、0.964和0.970,敏感度为90.70%,89.9%和91.5%,特异度为92.5%、88.80%和91.5%。

    表  2  L1、L2及L1和L2 椎体平均CT值预测骨质疏松的效能
    Table  2.  Efficacy of predicting osteoporosis based on the L1CT, L2CT, and mean CT values of L1 and L2
    分类敏感度特异度阈值AUCP
    L1CT值/HU91.394.7106.330.981<0.001
    L2CT值/HU95.290.9106.330.977<0.001
    L1、L2平均
    CT值/HU
    94.493.9105.830.984<0.001
    下载: 导出CSV 
    | 显示表格
    图  4  L1CT值、L2CT值和L1、L2平均CT值预测骨质疏松的ROC曲线
    Figure  4.  Fig.4 ROC curves for predicting osteoporosis based on the L1CT, L2CT, and mean CT values of L1 and L2, respectively

    有学者研究表明,L1椎体CT值与DEXA检查的T值呈中等程度相关性,相关系数为0.35~0.703[56],本研究表明,L1椎体CT值与QCT检查的BMD呈明显相关,相关系数为0.954。有研究显示QCT检查发现骨质疏松的敏感度和特异度明显高于DEXA检查,其主要原因是DEXA检查是二维检查,检查结果易受腰椎退行性变、腹部血管钙化等因素的影响,QCT检查测量的是椎体松质骨的体积BMD,其测量结果不受腰椎骨质增生、腹主动脉钙化、腰椎侧弯和体重等因素的影响[5,7]。因此,L1椎体CT值与QCT检查的BMD相关性高于与DEXA检查T值的相关性。

    本研究显示在轴位图像上L1椎体CT值测量具有良好的组间一致性和组内一致性,与文献报道基本一致[3]。本研究中Bland-Altman图显示L1椎体QCT测量的BMD与由L1椎体CT值预测的BMD的差异无统计学意义,说明L1椎体CT值测量具有高度可靠性。Buenger等[8]研究显示横轴位、矢状位及冠状位测量L1椎体CT值无显著性差异。不同CT设备测量的腰椎CT值及QCT骨密度值无显著性差异[910]。Mooney等[11]研究显示不同CT设备(飞利浦CT扫描仪和GE CT扫描仪)间L1椎体CT值具有很高的组间一致性,ICC为0.91。胸部CT或腹部CT扫描均包含有L1椎体,一项meta分析[12]显示使用胸部CT和腹部CT两种不同的检查参数,所测L1椎体CT值对于诊断骨质疏松的敏感度和特异度无显著性差异。L1椎体紧邻与肋骨相关节的胸12椎体,容易识别,而且,测量L1椎体CT值具有方法简单,不需要模体校正,所需的测量时间短,方便易行,可重复性高等优点,因此,L1椎体可以作为测量CT值筛查骨质疏松的理想部位[8,1113]

    Chia等[6]对一组50例增强CT扫描患者研究显示L1椎体CT值区分OP和非OP的AUC值为0.93,敏感度和特异度分别为80%和100%。Yao等[14]研究结果显示L1椎体CT值区分骨量正常和低骨量的AUC值0.83,敏感度为73%,特异度为86%。本组1117例腹部CT平扫患者中有378例为OP,其L1椎体CT值区分OP和非OP的AUC值为0.982,敏感度和特异度分别为91.3%和94.7%,区分骨量正常和低骨量的AUC值0.969。本组研究AUC值稍高或明显高于文献[6,14]报道的结果,究其原因可能是OP诊断标准的不同,本研究使用QCT测量BMD为诊断标准,而文献[6,14]均采用DEXA测量BMD为标准,此外,是否增强CT扫描、不同种族及研究人群的性别与年龄等也是影响椎体CT值及诊断效能的因素[7]。国内外关于L1椎体诊断OP的CT阈值也有一些报道,Wang等[15]对一组482例肺癌筛查的受试者研究显示,以QCT检查BMD为标准L1椎体CT值诊断OP的阈值为102.4 HU,章玲惠等[7]以QCT检查为标准对261例胸部低剂量CT扫描的受试者的研究显示,L1椎体诊断OP的阈值为93.36 HU,而王力平等[16]的阈值为98 HU,本组研究中OP阈值为106.33 HU,与Wang等[15]的研究结果基本一致。

    CT扫描kV对骨小梁CT值的影响明显高于对软组织CT值的影响,扫描kV增加,椎体CT值降低。Garner等[17]使用双能CT扫描研究显示使用80 kV和140 kV扫描时L1椎体CT值分别是194.9 HU和118.5 HU,多数学者推荐使用120 kV进行胸腹部CT扫描[6,18],而且QCT检查诊断OP的标准中也要求采用120 kV的扫描条件。因此,本研究均采用120 kV进行腹部CT扫描。测量椎体CT值所用的图像重建层厚目前尚无统一的标准,采用1~5 mm重建层厚均有报道,多数采用1~1.25 mm重建层厚[5,7,1415]。王鹏勇等[19]对90例腰椎BMD测量者的原始CT图像进行回顾性分析,分别以0.625、1.25和2.5 mm重建层厚完成QCT椎体骨密度测量,结果显示不同重建层厚间腰椎骨密度均值差异无统计学意义。

    本研究发现在轴位L1椎体CT值测量时,平行于终板的椎体中部水平层面骨小梁较其上下层面更致密,CT值更高,尤其是在低BMD的患者更加明显,与文献[10]报道一致,基于此,我们选择在轴位椎体图像上的椎体中部层面、上部终板的下方层面及下部终板的上方层面选取ROI,取上述3个平面的平均CT值作为L1椎体的CT值。由于腰椎QCT骨密度诊断OP标准中取2个腰椎椎体(第1和第2腰椎)松质骨骨密度平均值,小于80 mg/cm3的骨密度为骨质疏松[4],本研究对L1椎体CT值与L1、L2平均CT值在预测OP性能进行了比较,结果显示AUC值分别为0.982和0.984,两者均有非常高的诊断效能。

    本研究存在一定的限度。①本研究为单中心的回顾性研究,存在地域上的选择偏倚,代表性可能不足;②没有对不同性别及种族椎体CT值诊断骨质疏松的效能进行分析;③选择人群的年龄偏倚,本组患者平均年龄为62.45岁。

    综上所述,常规腹部CT扫描L1椎体CT值测量具有较高的组间及组内可靠性,L1椎体CT值与QCT检查的BMD呈明显的相关性,L1椎体CT值预测OP准确性高,区分OP与非OP的CT阈值是106.33 HU,AUC值0.982。对于缺少DEXA及QCT设备的基层医院,应用常规腹部CT测量L1椎体CT值进行OP机会性筛查是可行的。鉴于目前仍然普遍存在OP诊断和治疗不足的问题,我们建议在常规腹部CT的报告中增加L1椎体CT值的内容,对于CT值达到OP阈值的患者,建议进一步行DEXA或QCT检查确诊有无OP,达到早期诊断早期治疗的目的。

  • 图  1   (a)女,24岁,Omicron组患者,新型冠状病毒感染核酸检测阳性1天,入院体温37.3°,右肺上叶后段磨玻璃密度结节。(b)男,55岁,Delta组患者,周身酸痛伴气短1周+新冠病毒核酸检测阳性 1天,入院体温38.3°,双肺下叶磨玻璃结节影

    Figure  1.   (a) A 24-year-old female patient in the Omicron group had a positive nucleic acid test result for novel coronavirus pneumonia for 1 day. She was admitted to the hospital with a temperature of 37.3° and a nodule of ground glass density in the posterior segment of the upper lobe of the right lung. (b) Male, 55-year-old patient in the delta group, with body pain and shortness of breath for 1 week + positive COVID-19 nucleic acid test for 1 day; admission temperature: 38.3°, ground glass nodules in the lower lobes of both lungs

    图  2   女,41岁,Omicron组患者,新型冠状病毒核酸检测阳性1天,咳嗽2天。双肺下叶斑片状磨玻璃影,胸膜下分布,病灶内可见增粗血管影

    Figure  2.   Female patient, 41-year-old, in the Omicron group, tested positive for novel coronavirus nucleic acid for 1 day and coughed for 2 days. Patchy ground-glass opacity was observed in the lower lobes of both lungs, subpleura distributed, and thickened vascular shadows were observed in the lesions

    图  3   (a)男,42岁,Omicron组患者,咽痛2天,新冠病毒核酸阳性1天。既往体建,右肺下叶胸膜下“铺路石征”。(b)女,65岁,新冠核酸阳性1天,发热、咳嗽半日。双肺多发斑片状磨玻璃影及实变影,胸膜下分布为著,病灶内增粗血管影

    Figure  3.   (a) A 42-year-old male patient in the Omicron group had sore throat for 2 days and tested positive for COVID-19 nucleic acid for 1 day. Previous body construction, subpleural "paving stone sign" in the inferior lobe of the right lung. (b) A 65-year-old female who tested positive for COVID-19 for 1 day experienced fever and cough for half a day. Multiple patchy ground glass shadows and consolidation shadows in both lungs, with subpleural distribution and thickened vascular shadows in the lesions

    图  4   37岁女性,Omicron组患者,新型冠状病毒感染核酸检测阳性1天,咳嗽1天,CT MPR图像显示左肺下叶沿支气管血管束分布的斑片状磨玻璃密度及实变影

    Figure  4.   A 37-year-old woman in the omicron group tested positive for COVID-19 nucleic acid for 1 day and coughed for 1 day. CT images showed patchy ground glass, and consolidation is peribronchovascular bundle distribution in the lower lobe of the left lung

    图  5   46岁女性,Delta组患者,应用肺部感染辅助诊断软件进行处理获取左肺上叶、下叶肺炎病灶体积及体积占比

    Figure  5.   A 46-year-old female patient in the delta group was treated with pulmonary infection auxiliary diagnostic software to obtain the volume and proportion of pneumonia lesions in the upper and lower lobes of the left lung

    表  1   影像学征象比较

    Table  1   Comparison of imaging findings

    特征组别统计检验
    Delta组(n=293)Omicron组(n=222)$\chi^2$P
    磨玻璃斑片影 164(56.0) 115(51.8) 0.375 0.879
    磨玻璃结节影 132(45.1) 99(45.0) 0.001 0.991
    索条     2(0.6) 3(1.4) 0.078#
    实变     130(44.4) 104(47.3) 0.427 0.513
    铺路石征   47(16.0) 38(17.1) 7.014 0.408
    小叶间隔增厚 40(13.6) 34(15.3) 0.533#
    增粗血管影  29(9.9) 25(11.4) 0.287 0.592
     注:#-采用Fisher确切概率法比较。
    下载: 导出CSV

    表  2   病灶分布比较

    Table  2   Comparison of focal distribution

    分布组别P
    Delta组(n=293)Omicron组(n=222)
    支气管血管束分布17(5.8) 41(18.5)0.029
    胸膜下分布   276(94.2)181(81.5)
    下载: 导出CSV

    表  3   Delta组与Omicron组肺炎体积及体积占比比较

    Table  3   Comparison of pneumonia volume and volume proportion between the delta and omicron groups

    指标组别统计检验
    Delta组(n=293)Omicron组(n=222)ZP
     全肺病灶体积占比/%3.39(0.56~8.78)1.69(0.44~6.93)0.9170.025
     全肺病灶体积/cm3113.93(23.60~320.39)66.80(15.83~234.35)2.1740.030
     右肺上叶病灶体积占比/%0.63(0.00~3.63)0.40(0.00~2.82)1.3610.173
     右肺上叶病灶体积/cm35.63(0.00~28.83)3.23(0.00~27.00)1.3250.185
     右肺中叶病灶体积占比/%0.84(0.00~6.22)0.31(0.00~3.475)2.3800.017
     右肺中叶病灶体积/cm33.07(0.00~18.56)1.30(0.00~10.21)2.4470.014
     右肺下叶病灶体积占比/%4.61(0.62~17.34)2.18(0.24~12.69)2.9400.003
     右肺下叶病灶体积/cm337.68(5.58~117.28)17.02(1.90~82.04)2.9420.003
     左肺上叶病灶体积占比/%0.28(0.00~3.76)0.36(0.00~3.06)0.4680.639
     左肺上叶病灶体积/cm32.51(0.002~28.70)3.45(0.00~26.27)0.5480.584
     左肺下叶病灶体积占比/%3.27(0.35~14.18)1.63(0.15~12.67)1.9170.055
     左肺下叶病灶体积/cm323.21(2.45~86.92)11.49(0.95~69.64)2.0030.065
    下载: 导出CSV

    表  4   不同CT值分段病变体积及体积占比

    Table  4   Volume and volume proportion of different CT values

    指标组别统计检验
    Delta组(n=293)Omicron组(n=222)ZP
      -570~-470体积/cm314.66(2.85~44.29)9.01(2.12~31.59)1.9560.050
      -570~-470体积占比/%13.82(11.39~15.86)13.79(11.16~16.28)0.3370.736
      -470~-370体积/cm312.14(2.15~35.79)7.65(1.86~25.26)2.0830.037
      -470~-370体积占比/%11.31(8.80~13.77)11.05(8.56~13.28)0.5800.562
      -370~-270体积/cm39.43(1.81~27.9)6.11(1.34~18.07)2.3360.019
      -370~-270体积占比/%9.00(6.24~12.00)8.44(5.81~10.81)1.3950.163
      -270~-170体积/cm36.74(1.24~22.73)4.60(0.88~13.49)2.2120.027
      -270~-170体积占比/%6.63(4.39~10.00)6.29(4.17~8.67)1.2480.212
      -170~-70体积/cm34.65(0.97~16.92)3.17(0.61~10.73)1.9150.055
      -170~-70体积占比/%5.03(2.99~8.32)4.76(2.72~7.36)0.7410.458
      -70~-30体积/cm33.47(0.59~11.89)2.23(0.43~8.73)1.6340.102
      -70~-30体积占比/%3.49(1.60~5.78)3.25(1.49~6.34)0.0850.932
      30~70体积/cm30.58(0.06~2.85)0.38(0.03~2.33)1.6360.102
      30~70体积占比/%0.67(0.20~1.52)0.54(0.16~1.39)0.6730.501
    下载: 导出CSV
  • [1]

    DAVIES N G, ABBOTT S, BARNARD R C, et al. Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England[J]. Nature Communications, 2021, 372(6538): eabg3055. DOI: 10.1126/science.abg3055.

    [2]

    RAMAN R, PATEL K J, RANJAN K. COVID-19: Unmasking emerging SARS-CoV-2 variants, vaccines and therapeutic strategies[J]. Biomolecules, 2021, 11(7): 993. doi: 10.3390/biom11070993

    [3] 张影, 李晓鹤, 陈凤, 等. 新型冠状病毒德尔塔和奥密克戎变异株感染患者的临床特征分析[J]. 新发传染病电子杂志, 2022,7(3): 22−26. DOI: 10.19871/j.cnki.xfcrbzz.2022.03.005.

    ZHANG Y, LI X H, CHEN F, et al. Clinical characteristics of patients infected with SARS-CoV-2 Delta and Omicron variants[J]. Electronic Journal of Emerging Infectious Diseases, 2022, 7(3): 22−26. DOI: 10.19871/j.cnki.xfcrbzz.2022.03.005. (in Chinese).

    [4]

    RUBIN G D, RYERSON C J, HARAMATI L B, et al. The role of chest imaging in patient management during the COVID-19 pandemic: A multinational consensus statement from the Fleischner Society[J]. Radiology, 2020, 296(1): 172−180. doi: 10.1148/radiol.2020201365

    [5]

    BERNHEIM A, MEI X, HUANG M, et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection[J]. Radiology, 2020, 295(3): 685−691.

    [6] 车宏伟, 张晓琴, 柴军, 等. 新型冠状病毒肺炎临床表现及CT影像学分析[J]. CT理论与应用研究, 2021,30(4): 525−532. DOI: 10.15953/j.1004-4140.2021.30.04.14.

    CHE H W, ZHANG X Q, CHAI J, et al. Clinical manifestations and CT imaging analysis of corona virus disease 2019[J]. CT Theory and Applications, 2021, 30(4): 525−532. DOI: 10.15953/j.1004-4140.2021.30.04.14. (in Chinese).

    [7]

    AWULACHEW E, DIRIBA K, ANJA A, et al. Computed tomography (CT) imaging features of patients with COVID-19: Systematic review and meta-analysis[J]. Radiology Research and Practice, 2020: 1023506.

    [8]

    PAN F, YE T, SUN P, et al. Time course of lung changes on chest CT during recovery from 2019 novel coronavirus (COVID-19) pneumonia[J]. Radiology, 2020, 295(3): 715−721.

    [9] 胡元楠, 邓明, 胡金香, 等. 多参数定量CT评估新型冠状病毒肺炎预后的价值[J]. 武汉大学学报(医学版), 2021,42(2): 237−241. DOI: 10.14188/j.1671-8852.2020.0296.

    HU Y N, DENG M, HU J X, et al. Value of multiparameter quantitative CT in evaluating the prognosis of COVID-19[J]. Medical Journal of Wuhan University, 2021, 42(2): 237−241. DOI: 10.14188/j.1671-8852.2020.0296. (in Chinese).

    [10]

    GUAN C S, LV Z B, LI J, et al. CT appearances, patterns of progression, and follow-up of COVID-19: Evaluation on thin-section CT[J]. Insights into Imaging, 2021, 12(1): 73. doi: 10.1186/s13244-021-01019-0

    [11]

    LI B, DENG A, LI K, et al. Viral infection and transmission in a large, well-traced outbreak caused by the SARS-CoV-2 Delta variant[J]. Nature Communications, 2022, 13(1): 460. doi: 10.1038/s41467-022-28089-y

    [12]

    IULIANO A D, BRUNKARD J M, BOEHMER T K, et al. Trends in disease severity and health care utilization during the early Omicron variant period compared with previous SARS-CoV-2 high transmission periods—United States, December 2020–January 2022[J]. Morbidity and Mortality Weekly Report, 2022, 71(4): 146−152. doi: 10.15585/mmwr.mm7104e4

    [13]

    HU T, ZHANG M, DENG A, et al. Comparison of Omicron and Delta variant infection COVID-19 cases—Guangdong Province, China, 2022[J]. China CDC Weekly, 2022, 4(18): 385.

    [14]

    YOON S H, LEE J H, KIM B N. Chest CT findings in hospitalized patients with SARS-CoV-2: Delta versus Omicron variants[J]. Radiology, 2023, 306(1): 252−260. doi: 10.1148/radiol.220676

    [15]

    HUI K P Y, HO J C W, CHEUNG M, et al. SARS-CoV-2 Omicron variant replication in human bronchus and lung ex vivo[J]. Nature, 2022, 603(7902): 715−720. doi: 10.1038/s41586-022-04479-6

    [16]

    ULLOA A C, BUCHAN S A, DANEMAN N, et al. Estimates of SARS-CoV-2 Omicron variant severity in Ontario, Canada[J]. JAMA, 2022, 327(13): 1286−1288. doi: 10.1001/jama.2022.2274

    [17]

    ULLOA A C, BUCHAN S A, DANEMAN N, et al. Early estimates of SARS-CoV-2 Omicron variant severity based on a matched cohort study, Ontario, Canada[J]. MedRxiv, 2021: 2021.12. 24.21268382.

    [18]

    WOLTER N, JASSAT W, WALAZA S, et al. Early assessment of the clinical severity of the SARS-CoV-2 Omicron variant in South Africa: A Data linkage study[J]. The Lancet, 2022, 399(10323): 437−446. doi: 10.1016/S0140-6736(22)00017-4

    [19] 龚晓明, 李航, 宋璐, 等. 新型冠状病毒肺炎(COVID-19)CT表现初步探讨[J]. 放射学实践, 2020,35(3): 261−265. DOI: 10.13609/j.cnki.1000-0313.2020.03.003.

    GONG X M, LI H, SONG L, et al. Preliminary study on CT characteristics of corona virus disease 2019[J]. Radiologic Practice, 2020, 35(3): 261−265. DOI: 10.13609/j.cnki.1000-0313.2020.03.003. (in Chinese).

    [20] 张振华, 吉祥, 张劲松, 等. 基于AI技术的新型冠状病毒肺炎CT影像特点分析[J]. 医疗卫生装备, 2020,41(5): 6−8, 27. DOI: 10.19745/j.1003-8868.2020099.

    ZHANG Z H, JI X, ZHANG J S, et al. Analysis of COVID-19 CT features based on AI technology[J]. Chinese Medical Equipment Journal, 2020, 41(5): 6−8, 27. DOI: 10.19745/j.1003-8868.2020099. (in Chinese).

    [21]

    TSAKOK M T, WATSON R A, SAUJANI S J, et al. Chest CT and hospital outcomes in patients with Omicron compared with Delta variant SARS-CoV-2 infection[J]. Radiology, 2022, 306(1): 261−269. DOI: 10.1148/radiol.220533.

  • 期刊类型引用(1)

    1. 张凯杰,丁婷,桂志国,陈平,刘祎,张鹏程,汤豪威. 基于自适应加权增强总变差的CT偏置扫描重建算法. 中国体视学与图像分析. 2024(02): 126-137 . 百度学术

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  • 收稿日期:  2023-03-09
  • 修回日期:  2023-03-20
  • 录用日期:  2023-04-11
  • 网络出版日期:  2023-04-26
  • 发布日期:  2023-09-21

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