Correlation between findings on chest radiography and survival in neonates with congenital diaphragmatic hernia

OBJECTIVE: Predictors of survival are helpful when deciding on aggressiveness of care of neonates with congenital diaphragmatic hernia and respiratory failure. We evaluated findings on chest radiography as potential predictors of survival in these patients. MATERIALS AND METHODS: Findings on chest radiographs of neonates less than 24 hr old with congenital diaphragmatic hernia were evaluated. Radiographic findings analyzed included percentage of aerated ipsilateral lung, percentage of aerated contralateral lung, mediastinal shift, and hernia contents. Each finding was compared with survival (equated with hospital discharge) using a Mantel-Haenszel chi-square test. Survival was also determined using the total number of poor prognostic findings present in any one patient. RESULTS: In the 73 neonates with congenital diaphragmatic hernia in our study, the overall survival rate was 55%. There were statistically significant relationships between survival rate and percentage of ipsilateral aeration (p = 0.001), percentage of contralateral aeration (p = 0.016), and mediastinal shift (p = 0.026). The survival rate for multiple poor prognostic factors was 0% with four of four factors and 20% with three of four factors (p = 0.001). Survival rate was not influenced by prematurity (p = 0.102), sex (p = 0.104), or side of hernia (p = 0.895). CONCLUSION: Findings on initial chest radiography are helpful in predicting survival in neonates with congenital diaphragmatic hernia.     

The potential application of dual-energy subtraction radiography for COVID-19 pneumonia imaging

X-ray imaging plays a crucial role in the confirmation of COVID-19 pneumonia. Chest X-ray radiography and CT are two major imaging techniques that are currently adopted in the diagnosis of COVID-19 pneumonia. However, dual-energy subtraction radiography is hardly discussed as potential COVID-19 imaging application. More advanced X-ray radiography equipment often supports dual-energy subtraction X-ray radiography. Dual-energy subtraction radiography enables the calculation of pseudo-radiographs, in which bones are removed and only soft-tissues are highlighted. In this commentary, the author would like to draw the attention to the potential use of dual-energy subtraction X-ray radiography (i.e. soft-tissue pseudo-radiography) for the assessment and the longitudinal follow-up of COVID-19 pneumonia.     

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Emergency Radiology - Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes...     

Typical CT findings of COVID-19 pneumonia in patients presenting with repetitive negative RT-PCR

Multiple polymerase chain reaction (RT-PCR) is considered the gold standard diagnostic investigation for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19).However, false negative multiple polymerase chain reaction (RT-PCR) results can be diagnostically challenging. We report three patients with history of fever and different clinical signs. During the height of the pandemic in Italy (March to May 2020), these patients underwent chest computed tomography (CT) scans that showed lung alterations typical of COVID-19 with multiple negative RT-PCR tests and positive serology for SARS-CoV-2. Two of the three patients showed residual pneumonia on CT after the onset of the first clinical signs. One patient presented with diarrhoea without respiratory symptoms. These cases suggest that in the COVID-19 pandemic period, to provide an earlier specific treatment in patients with positive serology, a chest CT scan can be useful in those presenting with a fever or a history of fever associated with persistent mild respiratory symptoms or with abdominal complaints despite repeated negative RT-PCR results.     

Chest CT findings in a pregnant woman in the second trimester with COVID-19 pneumonia

Pregnant women with 2019 novel coronavirus disease (COVID-19) pneumonia are a special group of patients in the pandemic. We report a case of pregnant woman with COVID-19 pneumonia in the second trimester. Clinical and imaging features of the patient were similar to that reported in the literatures for both perinatal patients and non-pregnant patients.     

Systematic review and meta-analysis of chest radiograph (CXR) findings in COVID-19

Chest radiography (CXR) is most likely to be the utilized modality for diagnosing COVID-19 and following up on any lung-associated abnormalities. This review provides a meta-analysis of the current literature on CXR imaging findings to determine the most common appearances of lung abnormalities in COVID-19 patients in order to equip medical researchers and healthcare professionals in their efforts to combat this pandemic. Twelve studies met the inclusion criteria and were analyzed. The inclusion criteria consisted of: (1) published in English literature; (2) original research study; (3) sample size of at least 5 patients; (4) reporting clinical characteristics of COVID-19 patients as well as CXR imaging features; and (5) noting the number of patients with each corresponding imaging feature. A total of 1948 patients were included in this study. To perform the meta-analysis, a random-effects model calculated the pooled prevalence and 95% confidence intervals of abnormal CXR imaging findings. Seventy-four percent (74%) (95% CI: 51–92%) of patients with COVID-19 had an abnormal CXR at the initial time of diagnosis or sometime during the disease course. While there was no single feature on CXR that was diagnostic of COVID-19 viral pneumonia, a characteristic set of findings were obvious. The most common abnormalities were consolidation (28%, 95% CI: 8–54%) and ground-glass opacities (29%, 95% CI: 10–53%). The distribution was most frequently bilateral (43%, 95% CI: 27–60%), peripheral (51%, 95% CI: 36–66%), and basal zone (56%, 95% CI: 37–74%) predominant. Contrary to parenchymal abnormalities, pneumothorax (1%, 95% CI: 0–3%) and pleural effusions (6%, 95% CI: 1–16%) were rare.     

小儿肺炎支原体肺炎数字X线胸片影像诊断

目的:探讨数字X线胸部摄影对小儿肺炎支原体肺炎的诊断价值.方法:对41例血清IgM抗体滴度升高4倍以上的患儿的初次胸部X线平片影像表现进行回顾性分析.结果:37例(90.3％)显示段、叶性分布实变影,8例(19.5％)显示结节样小叶气腔实变,19例(46.3 ％)显示支气管血管束周围浸润.4例(9.7％)病变累及双肺,30例(73.2％)累及下肺野.第2次抗体滴度≥1280的患儿肺内病变累及范围大于第2次抗体滴度≤640的患儿(x2 =8.789,P=0.003).结论:叶段分布气腔实变、支气管血管束周围浸润是小儿肺炎支原体肺炎最常见的胸部X线影像表现,结节样小叶中心气腔实变显示率较低.肺炎支原体抗体滴度升高程度可能与肺内病变的严重程度有关.     

Correlation of laboratory parameters and Chest CT findings in young adults with COVID-19 and comparison of imaging findings with children

PurposeWe aimed to compare COVID-19 imaging findings of young adults (19–35 years of age) with those of children (0–18 years) and to correlate imaging findings of young adults with their laboratory tests.Materials and methodsThis retrospective study included Real Time-Polymerase Chain Reaction (RT-PCR) confirmed 130 young adults (mean age: 28.39 ± 4.77; 65 male, 65 female) and 36 children (mean age: 12.41 ± 4.51; 17 male, 19 female), between March and June 2020. COVID-19 related imaging findings on chest CT were examined in young adults and compared with children by the Mann-Whitney U, and Chi-square or Fisher's exact test. Laboratory examinations of young adults were assessed in terms of correlation with radiological findings by the Spearman's correlation analysis.ResultsBilateral multiple distributions (p = 0.014), subpleural involvement, and pleural thickening (p = 0.004), GGOs with internal consolidations were more frequent in adults (p = 0.009). Infiltrations were significantly larger than 20 mm in young adults (p = 0.011). The rates of feeding vessel sign, vascular enlargement, and halo sign were significantly higher in young adults (p < 0.003). Highly significant positive correlations were found between radiological and biochemical parameters.ConclusionDistribution, size, and pattern of COVID-19 related imaging findings differed in children and young adults. Radiological findings were correlated with biochemical parameters but not with blood count results of young adults.     

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

PURPOSEChest X-ray plays a key role in diagnosis and management of COVID-19 patients and imaging features associated with clinical elements may assist with the development or validation of automated image analysis tools. We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.METHODSA retrospective study of X-rays, clinical, and laboratory data was performed from 48 SARS-CoV-2 RT-PCR positive patients (age 60±17 years, 15 women) between February 22 and March 6, 2020 from a tertiary care hospital in Milan, Italy. Sixty-five chest X-rays were reviewed by two radiologists for alveolar and interstitial opacities and classified by severity on a scale from 0 to 3. Clinical factors (age, symptoms, comorbidities) were investigated for association with opacity severity and also with placement of central line or endotracheal tube. Deep learning models were then trained for two tasks: lung segmentation and opacity detection. Imaging characteristics were compared to clinical datapoints using the unpaired student’s t-test or Mann-Whitney U test. Cohen’s kappa analysis was used to evaluate the concordance of deep learning to conventional radiologist interpretation.RESULTSFifty-six percent of patients presented with alveolar opacities, 73% had interstitial opacities, and 23% had normal X-rays. The presence of alveolar or interstitial opacities was statistically correlated with age (p = 0.008) and comorbidities (p = 0.005). The extent of alveolar or interstitial opacities on baseline X-ray was significantly associated with the presence of endotracheal tube (p = 0.0008 and p = 0.049) or central line (p = 0.003 and p = 0.007). In comparison to human interpretation, the deep learning model achieved a kappa concordance of 0.51 for alveolar opacities and 0.71 for interstitial opacities.CONCLUSIONChest X-ray analysis in an acute COVID-19 outbreak showed that the severity of opacities was associated with advanced age, comorbidities, as well as acuity of care. Artificial intelligence tools based upon deep learning of COVID-19 chest X-rays are feasible in the acute outbreak setting.

The imaging features of novel coronavirus (SARS-CoV-2) and coronavirus disease 2019 (COVID-19) pandemic are still being fully characterized and understood (1, 2). The estimated mortality rate is reported between 1.4% and 7% (3). Health services and intensive care units (ICUs) are facing critical saturation in this pandemic (4), where early and wise resource allocation decisions may impact population outcomes.Radiology departments play a key role in this pandemic (5–8), with imaging data potentially contributing towards detection (9–14), characterization (9), monitoring (15–18), triage (19–22), resource allocation, early intervention, and isolation (8). Although speculative, models that correlate imaging findings to outcomes could be helpful or predictive in the management and triage of the 20% of SARS-CoV-2 positive patients who develop more serious manifestations of COVID-19 pneumonia. Epidemiology standards require a waiting period in between patients with airborne viral diseases, which may practically limit computed tomography (CT) use. To date, radiology and thoracic professional societies have pointed to the efficiency, ease of access, field availability, and repeatability of chest X-ray as well as its ease of cleaning and decontamination. These strengths are balanced against the higher sensitivity and specificity of CT. Moreover, when patients are encouraged to present early in the course of their disease, as was the case in Hubei Province, China, chest X-ray may have less value than CT.Typical and characteristic CT features for COVID-19 related pneumonia have been recently defined (23–28). Chest X-ray findings might help address clinical decision-making in screening, management and prioritization that may unfortunately arise in the care of COVID-19 patients. Resource allocation may be most critical during peak prevalence, when imaging equipment may also be stretched thin, or not accessible to intensive care or “medical surge facility” settings.Deep learning uses convolutional neural networks that are like a “black box” in that they may or may not use conventional imaging features to function and classify the outputs. Machine learning on the other hand, would use specific features, and generally requires less data points to ensure clinically relevant accuracy or validity. This study uses tools for explanatory purposes, not for producing a refined or usable model at this early stage. There are currently limited reports of the role of chest X-ray in COVID-19 patients with scarce details of the application and role of deep learning of chest X-rays in patients with COVID-19. Previous papers stated that the main findings were bilateral reticular nodular opacities, ground-glass opacities and peripheral consolidations (29–31). Deep learning and artificial intelligence (AI) applications in chest radiography are in their infancy, but there are multiple commercial platforms for computer-aided detection for pulmonary nodule detection, characterization and quantification of interstitial lung disease (32–34). We aimed to identify associations between clinical and radiographic features as well as to assess the feasibility of deep learning applied to chest X-rays in the setting of an acute COVID-19 outbreak.     

Improved detection of focal pneumonia by chest radiography with bone suppression imaging

Objective

To evaluate radiologists’ ability to detect focal pneumonia by use of standard chest radiographs alone compared with standard plus bone-suppressed chest radiographs.

Methods

Standard chest radiographs in 36 patients with 46 focal airspace opacities due to pneumonia (10 patients had bilateral opacities) and 20 patients without focal opacities were included in an observer study. A bone suppression image processing system was applied to the 56 radiographs to create corresponding bone suppression images. In the observer study, eight observers, including six attending radiologists and two radiology residents, indicated their confidence level regarding the presence of a focal opacity compatible with pneumonia for each lung, first by use of standard images, then with the addition of bone suppression images. Receiver operating characteristic (ROC) analysis was used to evaluate the observers’ performance.

Results

The mean value of the area under the ROC curve (AUC) for eight observers was significantly improved from 0.844 with use of standard images alone to 0.880 with standard plus bone suppression images (P?<?0.001) based on 46 positive lungs and 66 negative lungs.

Conclusion

Use of bone suppression images improved radiologists’ performance for detection of focal pneumonia on chest radiographs.

Key Points

? Bone suppression image processing can be applied to conventional digital radiography systems. ? Bone suppression imaging (BSI) produces images that appear similar to dual-energy soft tissue images. ? BSI improves the conspicuity of focal lung disease by minimizing bone opacity. ? BSI can improve the accuracy of radiologists in detecting focal pneumonia.     

骨抑制像胸片提高局灶性肺炎的检出率

目的评价放射医师仅用常规胸片与联合使用常规胸片和骨抑制像胸片对于检出局灶性肺炎的能力。方法本观察研究包括36例病人的常规胸片,共有肺炎所致的46个局灶性斑片影(10例为双侧斑片),还有20张没有局灶性斑片的胸片。采用骨抑制图像处理系统对56张胸片进行后处理,生成骨抑制影像。共有8位放射医师,6位主治医师,2位住院医师,先对常规胸片阅片,然后再增加骨抑制像,分别对每侧肺内是否存在肺炎所致的局灶性斑片进行评价。采用ROC曲线分析评价各观察者的诊断。结果对于这46侧阳性肺和66侧阴性肺,8位医师的ROC曲线下面积的平均值从仅用常规胸片的0.844提高到常规胸片联合骨抑制像的0.880(P<0.001)。结论使用骨抑制像可以提高放射医师在胸片中检出局灶性肺炎的能力。