人工智能定量测量对新型冠状病毒肺炎患者胸部CT炎性病灶动态变化的评估价值

目的:探讨人工智能（AI）辅助定量测量评估新型冠状病毒肺炎（COVID-19）胸部CT动态变化的价值。方法:回顾性分析2020年1月15日至3月10日在华中科技大学同济医学院附属武汉市中心医院接受治疗的99例确诊COVID-19患者的临床和胸部CT动态影像资料。依据最终诊断,99例患者分为普通型（36例）、重型（33例...     

甘肃省SARS-CoV-2 Delta变异株感染的COVID-19流行病学和临床特征分析

目的 分析新型冠状病毒（SARS-CoV-2） Delta变异株感染的新型冠状病毒肺炎（COVID-19）的流行病学及临床特征。方法 回顾性分析2021年10月25日-11月19日兰州市第二人民医院雁滩分院收治的SARS-CoV-2 Delta变异株感染的本土138例COVID-19确诊患者的流行病学和临床特征,采集流行病学及人口学信息、临床症状、实验室检查、胸部CT、治疗和预后数据,最终随访日期为2021年11月27日。结果 截至2021年11月19日,甘肃省此次疫情累计报告COVID-19确诊病例144例,其中兰州市第二人民医院雁滩分院集中收治138例,男65例（47.1%）,女73例（52.9%）,男女比例为1:1.12,年龄2～87（42.7±21.0）岁,临床分型以普通型（48.6%,67/138）为主。SARS-CoV-2 Delta变异株的传播方式以密闭空间为主,具有明显的旅行团聚集性和家庭聚集性;密切接触者筛查和社区排查是发现感染者的主要途径;86.2%（119/138）的确诊患者已接种国产COVID-19灭活疫苗;临床症状最常见的为咳嗽（57.2%,79/138）,其...     

COVID-19胸部CT结构化报告临床应用价值评价

【摘要】目的：探究新型冠状病毒肺炎（COVID-19）胸部CT结构化报告临床诊断应用价值。方法：回顾性分析310例疑似COVID-19患者胸部CT及临床资料。根据纳入标准最终共纳入253例患者,阳性组203名,阴性组50名。对胸部CT进行诊断分型,并记录CT征象。以模式1（CT诊断分型）、模式2（CT诊断分型+CT征象）、模式3（CT诊断分型+基本信息+流行病学史+临床及实验室检查）三种模式进行二元逻辑回归分析,采用ROC曲线及曲线下面积（AUC）评价三种模型诊断准确性。结果：逻辑回归分析显示三种模式诊断敏感度分别为95.6%、96.0%和95.2%,特异度分别为34.0%、48.9%和32.7%,符合率分别为83.4%、87.0%、82.2%,AUC为0.768、0.895和0.812。结论：COVID-19胸部CT结构化报告能较为准确地诊断COVID-19肺炎,敏感度较高,但缺乏特异度,结合CT征象可进一步提高诊断准确性,特别是提高诊断特异度。     

胸部CT特征预测普通型新型冠状病毒肺炎患者转归的价值

【摘要】目的:探讨胸部CT特征评估普通型新型冠状病毒肺炎（COVID-19）患者转归情况的价值。方法：搜集入院时为普通型COVID-19的患者158例,根据入院后2~7天内是否发展为重症肺炎分为普通型组和转重症组;记录其临床资料、血清学指标以及胸部CT表现。利用多因素Logistic回归筛选普通型COVID-19转重症肺炎的独立影响因素。利用列线图预测普通型COVID-19患者的预后。结果：淋巴细胞计数减少（P=0.032）、病灶累及右肺中叶（P=0.020）、病灶累及肺叶数（P=0.021）以及病灶占整肺体积百分比（P=0.013）是影响普通型COVID-19转重症的独立影响因素。列线图模型拟合度为0.85,提示模型预测结果与实际一致性较好。结论：胸部CT的特征表现对普通型COVID-19患者临床分型的转变具有较好的预测能力,列线图可以方便地预测出每例普通型COVID-19患者转为重症肺炎的概率。     

尿毒症患者无发热症状新冠肺炎与尿毒症肺炎CT鉴别诊断

【摘要】目的：通过对尿毒症患者无明显发热症状2019新冠肺炎(COVID-19)与尿毒症肺炎胸部CT影像对比分析,提高尿毒症患者无发热症状COVID-19与尿毒症肺炎鉴别诊断水平。方法：回顾性分析2019年12月15日-2020年2月22日武汉市第一医院13例尿毒症患者初次CT检查无明显发热症状COVID-19及2019年1月1日-2020年2月22日25例尿毒症肺炎患者CT影像表现。结果：COVID-19的磨玻璃影（10/13）、实变影（2/13）及空气支气管征（3/13）占比与尿毒性肺炎（11/25 、2/25、2/25）无明显区别（P＞0.05）;尿毒症肺炎小叶间隔增厚（10/25）及胸腔积液（9/25）明显多于COVID-19（0/13、0/13）（P＜0.01及P＜0.05）;COVID-19“铺路石征”或“晕征”（5/13）明显多于尿毒症肺炎（0/25）（P＜0.01）;COVID-19病灶仅肺外周分布（9/13）及单肺分布（3/13）明显多于尿毒症肺炎（0/25、0/25）（P＜0.01及P＜0.05）,而尿毒症肺炎病灶肺中心分布或中心及外周同时分布（22/25）、双肺分布（25/25）多于COVID-19（4/13、10/13）（P＜0.01及P＜0.05）。结论：CT检查在尿毒症患者中能够鉴别COVID-19与尿毒症肺炎,对此类人群中无发热症状COVID-19患者早发现、早隔离、减少交叉感染起到一定作用。     

CT影像组学联合征象鉴别新型冠状病毒肺炎与其他病毒性肺炎的价值

目的:探讨基于CT影像征象联合影像组学模型鉴别新型冠状病毒肺炎（COVID-19）和其他病毒性肺炎的临床价值。方法:回顾性分析2015年3月至2020年3月云南省15家医院实时逆转录聚合酶链反应检测为病毒性肺炎并接受胸部CT扫描的181例患者的临床和影像资料。根据患者的病毒类型分为COVID-19组（89例）和非COV...     

心脏MR纵向弛豫时间定量成像评估新型冠状病毒肺炎康复患者心肌损伤的价值

目的:探讨心脏MR（CMR）纵向弛豫时间定量成像（T 1 mapping）评估新型冠状病毒肺炎（COVID-19）康复者心肌损伤的价值。 方法:前瞻性收集阜阳市第二人民医院2020年5月至6月COVID-19患者康复出院3个月后接受CMR检查的15例患者（9例普通型、6例重型）的临床及影像资料。另...     

新型冠状病毒肺炎不同临床分型间CT和临床表现的相关性研究

目的 探讨新型冠状病毒肺炎(COVID-19)不同临床分型的CT和临床表现的相关性。方法 回顾性分析2020年1月华中科技大学同济医学院附属同济医院确诊的103例COVID-19患者的胸部CT平扫图像和临床资料,根据COVID-19诊疗方案(试行第五版),将所有患者分为普通型(58例)、重型(36例)和危重型(9例),并分析其临床表现、实验室检查和CT表现。CT特征参数包括病灶分布、位置、大小、形态、边缘、数目、密度、肺炎病灶占整肺百分比和肺外表现。不同临床分型的CT特征之间的比较采用χ2或Fisher确切概率法。年龄、从发病到CT检查时间及肺炎病灶占整肺体积百分比之间的比较采用方差分析(正态分布)或Kruskal-Wallis秩和检验(非正态分布)。结果 临床表现方面,危重型COVID-19患者更常见于老年男性,中位年龄65岁。58例普通型COVID-19患者有49例(84%)以发热为首发症状,重型及危重型COVID-19患者均以发热为首发症状,重型(25/36, 69%)及危重型(6/9, 67%)COVID-19患者的咳嗽发生率高于普通型(20/58, 34%)。所有危重型患者存在呼吸困难。CT表现中,普通型COVID-19表现为双肺(41/58, 71%)、多发(40/58, 69%)、磨玻璃(31/58, 52%)或混合型(25/58, 43%)、斑片(56/58, 97%)灶;重型及危重型COVID-19均表现为双肺病灶,重型COVID-19以多发(34/36, 96%)、斑片(33/36, 92%)、混合密度灶(26/36, 72%)为主;9例危重型COVID-19病灶均为大于3 cm的多发混合密度病灶。肺炎病灶占整肺体积的百分比:普通型(12.5%±6.1%)明显低于重型(25.9%±10.7%)及危重型(47.2%±19.2%) COVID-19,两者差异具有统计学意义(P值分别为<0.001、0.002),重型COVID-19也显著低于危重型,差异具有统计学意义(P=0.032)。结论 COVID-19不同临床分型的CT和临床表现存在差异,胸部CT表现具有特征性,不仅能早期诊断,还能对其临床病程及严重程度进行评估。     

CT肺功能成像在新型冠状病毒肺炎恢复期患者中的应用研究

目的:探讨CT肺功能成像在新型冠状病毒肺炎（COVID-19）恢复期患者肺部评估中的应用价值。方法:前瞻性收集2020年1月至4月华中科技大学同济医学院附属协和医院临床治愈出院的COVID-19患者。出院后3个月对患者进行临床肺功能检查及CT肺功能成像检查。使用Philips IntelliSpace Portal图像...     

双相CT空气潴留征与半定量评分预测新型冠状病毒肺炎患者血气指标异常及进展为重症的价值

目的:探讨低剂量双相（吸气相与呼气相）CT空气潴留征及病灶范围半定量评分预测新型冠状病毒肺炎（COVID-19）患者重症及血气指标异常的价值。方法:前瞻性连续性纳入2020年1月23日至2月29日无锡市定点收治医院经核酸检测确诊并住院治疗的非重症COVID-19患者。所有患者入院时均接受低剂量双相CT检查且在病程中按时...     

Typical chest CT features can determine the severity of COVID-19: A systematic review and meta-analysis of the observational studies

BackgroundIt remains unclear whether a specific chest CT characteristic is associated with the clinical severity of COVID-19. This meta-analysis was performed to assess the relationship between different chest CT features and severity of clinical presentation in COVID-19.MethodsPubMed, Embase, Scopus, web of science databases (WOS), Cochrane library, and Google scholar were searched up to May 19, 2020 for observational studies that assessed the relationship of different chest CT manifestations and the severity of clinical presentation in COVID-19 infection. Risk of bias assessment was evaluated applying the Newcastle-Ottawa Scale. A random-effects model or fixed-effects model, as appropriately, were used to pool results. Heterogeneity was assessed using Forest plot, Cochran's Q test, and I2. Publication bias was assessed applying Egger's test.ResultsA total of 18 studies involving 3323 patients were included. Bronchial wall thickening (OR 11.64, 95% CI 1.81–74.66) was more likely to be associated with severe cases of COVID-19 infection, followed by crazy paving (OR 7.60, 95% CI 3.82–15.14), linear opacity (OR 3.27, 95% CI 1.10–9.70), and GGO (OR 1.37, 95% CI 1.08–1.73). However, there was no significant association between the presence of consolidation and severity of clinical presentation (OR 2.33, 95% CI 0.85–6.36). Considering the lesion distribution bilateral lung involvement was more frequently associated with severe clinical presentation (OR 3.44, 95% CI 1.74–6.79).ConclusionsOur meta-analysis of observational studies indicates some specific chest CT features are associated with clinical severity of COVID-19.     

Chest CT features and progression of patients with coronavirus disease 2019

Objectives:Coronavirus disease 2019 (COVID-19) is a major public health emergency. It poses a grave threat to human life and health. The purpose of the study is to investigate the chest CT findings and progression of the disease observed in COVID-19 patients.Methods:Forty-nine confirmed cases of adult COVID-19 patients with common type, severe and critically severe type were included in this retrospective single-center study. The thin-section chest CT features and progress of the disease were evaluated. The clinical and chest imaging findings of COVID-19 patients with different severity types were compared. The CT severity score and MuLBSTA score (a prediction of mortality risk) were calculated in those patients.Results:Among the 49 patients, 35 patients (71%) were common type and 14 patients (28%) were severe and critically severe type. Nearly all patients (98%) had pure ground-glass opacities (GGO) in CT imaging. Of the severe and critically severe type patients, 86% exhibited GGO with consolidation, in comparison with 54% of the patients with common type. Fibrosis presented in 79% of the severe and critically severe type patients and 43% of the common type patients. The severe and critically severe type patients were significantly more prone to experience five-lobe involvement compared to the common type patients (p = 0.002). The severe and critically severe type patients also had higher CT severity and MuLBSTA scores than the common type patients (5.43 ± 2.38 vs 3.37 ± 2.40, p < 0.001;and 10.21 ± 3.83 vs 4.63 ± 3.43, p < 0.001, respectively). MuLBSTA score was positively correlated with admittance to the intensive care unit (p = 0.005, r = 0.351). Nineteen patients underwent three times CT scan. The interval between first and second CT scan was 4[4,8] days, second and third was 3[2,4] days. There were greater improvements in the third CT follow-up findings compared to the second (p = 0.002).Conclusions:The severe and critically severe type patients often experienced more severe lung lesions, including GGO with consolidation. The CT severity score and MuLBSTA score may be helpful for the assessment of COVID-19 severity and progression.Advances in knowledge:Chest CT has the value of evaluated radiographical features of COVID-19 and allow for dynamic observation of the disease progression. Considering coagulation disorder of COVID-19, MuLBSTA score may need to be updated to increase new understanding of COVID-19.     

Growth rate of small lung cancers detected on mass CT screening

CT has recently been used in mass screening for lung cancer. Small cancers have been identified but the growth characteristics of these lesions are not fully understood. We identified 82 primary cancers in our 3-year mass CT screening programme, of which 61 were examined in the present study. The volume doubling time (VDT) was calculated based on the exponential model using successive annual CT images or follow-up CT images. All cases were also examined in the hospital by high resolution CT (HRCT). Lesions were divided into three types based on HRCT characteristics: type G (n = 19), ground glass opacity (GGO); type GS (n = 19), focal GGO with a solid central component; and type S (n = 23), solid nodule. 18 (95%) lesions of type G, 18 (95%) of type GS and 7 (30%) of type S were invisible on conventional chest radiographs. The mean size of the tumour was 10 mm, 11 mm and 16 mm for type G, type GS and type S, respectively. Most tumours (80%) were adenocarcinomas; 78% of these were GGO (type G and GS). Mean VDT values were 813 days, 457 days and 149 days for type G, type GS and type S, respectively; these are significantly different from each other (p < 0.05). Our results show that annual mass screening CT for 3 successive years resulted in the identification of a large number of slowly growing adenocarcinomas that were not visible on chest radiographs.     

Características en tomografía computarizada de la neumonía por coronavirus-2019 (COVID-19) en tres grupos de pacientes iraníes: estudio de un solo centro

Introduction and ObjectivesThe pivotal role of chest computed tomographic (CT) to diagnosis and prognosis coronavirus disease-2019 (COVID-19) is still an open field to be explored. This study was conducted to assess the CT features in confirmed cases with COVID-19.Materials and MethodsRetrospectively, initial chest CT data of 363 confirmed cases with COVID-19 were reviewed. All subjects were stratified into three groups based on patients’ clinical outcomes; non-critical group (n=194), critical group (n=65), and death group (n=104). The detailed of CT findings were collected from patients’ medical records and then evaluated for each group. In addition, multinomial logistic regression was used to analyze risk factors according to CT findings in three groups of patients with COVID-19.ResultsCompared with the non-critical group, mixed ground-glass opacities (GGO) and consolidation lesion, pleural effusion lesion, presence of diffuse opacity in cases, more than 2 lobes involved and opacity scores were significantly higher in the critical and death groups (P<0.05). Having more mixed GGO with consolidation, pleural effusion, lack of pure GGO, more diffuse opacity, involvement of more than 2 lobes and high opacity score identified as independent risk factors of critical and death groups.ConclusionCT images of non-critical, critical and death groups with COVID-19 had definite characteristics. CT examination plays a vital role in managing the current COVID-19 outbreak, for early detection of COVID-19 pneumonia. In addition, initial CT findings may be useful to stratify patients, which have a potentially important utility in the current global medical situation.     

基于深度学习的新型冠状病毒肺炎转归胸部CT评价

目的 分析基于深度学习的新型冠状病毒肺炎（COVID-19）不同临床转归患者胸部CT的差异,以提高对COVID-19转归的影像认识。 方法 回顾性分析2020年1月25日至3月29日来自内蒙古自治区COVID-19病例库的42例COVID-19患者（临床分型为普通型）的胸部CT资料,其中,男性20例、女性22例,年龄17个月~86岁[（48.74±17.18）岁]。根据是否转为重症（重型或危重型）将患者分为未转为重症的A组（n=29）和转为重症的B组（n=13）,比较2组患者年龄、性别及基于深度学习的胸部CT表现,评价感染肺叶病灶分布,累及肺叶侧别、数目,感染肺叶病灶体积,密度（CT值）和感染肺叶病灶体积占比等资料的差异。计量资料的比较采用两独立样本t检验、Mann-Whitney U非参数检验;计数资料的比较采用卡方检验或Fisher's确切概率法。 结果 2组患者的性别差异无统计学意义（χ2=0.016,P=1.000）。B组患者的年龄高于A组[（65.62±11.24）岁对（41.17±13.66）岁 ],且差异有统计学意义（t=5.64,P<0.001）。B组患者感染肺叶数以及各肺叶感染体积占比、总感染体积占比均高于A组,且差异有统计学意义（Z=2.505~3.605,均P<0.05）。2组患者肺部总体积差异无统计学意义（Z=1.456,P=0.146）,B组患者各肺叶感染体积及双肺总感染体积均高于A组,且差异有统计学意义（Z=2.301~3.254,均P<0.05）;B组患者在各CT阈值范围内的肺部感染体积占比均高于A组,且差异有统计学意义（Z=3.115~3.578,均P<0.05）。胸部CT和人工智能病灶识别图的图像结果显示,病灶均以磨玻璃密度影、实变为主,双肺下叶感染较多,右肺中叶较少。 结论 转为重症的COVID-19患者的胸部CT明显有别于未转为重症的患者。基于深度学习的人工智能可尽早评估有重症转归倾向的患者,有助于COVID-19重症率的控制。     

Differential diagnosis between the coronavirus disease 2019 and Streptococcus pneumoniae pneumonia by thin-slice CT features

ObjectiveThe chest computed tomography (CT) features of coronavirus disease 2019 (COVID-19) and Streptococcus pneumoniae pneumonia (S. pneumoniae pneumonia) were compared to provide further evidence for the differential imaging diagnosis of patients with these two types of pneumonia.MethodsClinical information and chest CT data of 149 COVID-19 patients between January 9, 2020 and March 15, 2020 and 97 patients with S. pneumoniae pneumonia between January 23, 2011 and March 18, 2020 in Zhongnan Hospital of Wuhan University were retrospectively analyzed. In addition, CT features were comparatively analyzed.ResultsAccording to the chest CT images, the probability of lung segmental and lobar pneumonia in S. pneumoniae pneumonia was higher than that in COVID-19(P<0.001); the probabilities of ground-glass opacity (GGO), the “crazy paving” sign, and abnormally thickened interlobular septa in COVID-19 were higher than those in S. pneumoniae pneumonia(P = 0.005, P<0.001, P<0.001, respectively); and the probabilities of consolidation lesions, bronchial wall thickening, centrilobular nodules, and pleural effusion in S. pneumoniae pneumonia were higher than those in COVID-19 (P<0.001, P = 0.001, P = 0.003, P = 0.001, respectively).ConclusionThe findings of GGO, the crazy paving sign, and abnormally thickened interlobular septa on chest CT were significantly higher in COVID-19 than S. pneumoniae pneumonia. The most important differential points on chest CT signs between COVID-19 and S. pneumoniae pneumonia were whether disease lesions were distributed in entire lung lobes and segments and whether the crazy paving sign, interlobular septal thickening, and consolidation lesions were found.     

Complications in COVID-19 patients: Characteristics of pulmonary embolism

ObjectiveThe purpose of this study is to evaluate chest CT imaging features, clinical characteristics, laboratory values of COVID-19 patients who underwent CTA for suspected pulmonary embolism. We also examined whether clinical, laboratory or radiological characteristics could be associated with a higher rate of PE.Materials and methodsThis retrospective study included 84 consecutive patients with laboratory-confirmed SARS-CoV-2 who underwent CTA for suspected PE. The presence and localization of PE as well as the type and extent of pulmonary opacities on chest CT exams were examined and correlated with the information on comorbidities and laboratory values for all patients.ResultsOf the 84 patients, pulmonary embolism was discovered in 24 patients. We observed that 87% of PE was found to be in lung parenchyma affected by COVID-19 pneumonia. Compared with no-PE patients, PE patients showed an overall greater lung involvement by consolidation (p = 0.02) and GGO (p < 0.01) and a higher level of D-Dimer (p < 0,01). Moreover, the PE group showed a lower level of saturation (p = 0,01) and required more hospitalization (p < 0,01).ConclusionOur study showed a high incidence of PE in COVID-19 pneumonia. In 87% of patients, PE was found in lung parenchyma affected by COVID-19 pneumonia with a worse CT severity score and a greater number of lung lobar involvement compared with non-PE patients. CT severity, lower level of saturation, and a rise in D-dimer levels could be an indication for a CTPA.Advances in knowledgeCertain findings of non-contrast chest CT could be an indication for a CTPA.     

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data

ObjectiveTo develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.Materials and MethodsClinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.ResultsAmong 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively.ConclusionCT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.