Radiographic findings of adenoviral pneumonia in children

OBJECTIVE

Adenovirus pneumonia is a common cause of community-acquired pneumonia in children and can mimic bacterial pneumonia, but there are few publications on its radiographic features. This study has evaluated the chest radiography findings of community-acquired adenovirus pneumonia in children. The frequency of radiological findings mimicking bacterial pneumonia was investigated. The clinical features of patients with adenovirus pneumonia possessing radiological findings mimicking bacterial pneumonia were also evaluated.

MATERIALS AND METHODS

The chest radiographs of patients diagnosed with adenovirus pneumonia were retrospectively reviewed. The chest radiographs were interpreted independently by a pediatric infectious disease specialist and a pediatric radiologist. Chest radiography findings mimicking bacterial pneumonia (bacterial-like) were specified as consolidation +/- pleural effusion. Other findings on chest radiography or a completely normal chest X-ray were specified as findings that were compatible with "typical viral pneumonia".

RESULTS

A total of 1407 patients were positive for adenovirus with respiratory multiplex PCR. The 219 patients who met the study criteria were included in the study. Chest radiographs were normal in 58 (26.5 %) patients. The chest radiograph findings mimicked bacterial pneumonia in 41 (18.7 %) patients.

CONCLUSION

Adenovirus pneumonia occurs predominantly in children aged five years and younger, as with other viral pneumonias. The radiographic findings in adenovirus pneumonia are predominantly those seen in viral pneumonia. Increasing age and positivity for only adenovirus without other viruses on respiratory multiplex PCR were associated with the chest radiograph being more likely to be "bacterial-like". Adenovirus may lead to lobar/segmental consolidation at a rate that is not very rare.

[The diagnostic value of lung ultrasound in children with community-acquired pneumonia]

Objective: To investigate the diagnostic value of lung ultrasound in hospitalized children with community-acquired pneumonia (CAP). Methods: In the cross-sectional study, a total of 422 children with CAP who were hospitalized in the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, from February 2021 to August 2022 and completed lung ultrasound examination within 48 hours after admission were enrolled. The clinical characteristics, lung ultrasound and chest CT were collected. The patients were divided into two groups according to the signs of pneumonia indicated by chest CT, and the signs of lung ultrasound with diagnostic value were screened according to the signs of pneumonia indicated by chest CT by least absolute shrinkage and selection operator (Lasso) regression. According to severity of the disease, the children were divided into the severe group and the mild group, and the differences of lung ultrasound signs between the two groups were compared. Kruskal-Wallis test, Fisher's exact test was selected for comparison between groups. Random forest classifier wes used to evaluate the value of lung ultrasound in the diagnosis of CAP and prediction of severe pneumonia in children. The receiver operating characteristic curve was used to evaluate the prediction effect. Use DeLong test to compare the area under the curve. Results: Among the 422 cases of CAP, there were 258 males and 164 females, and the age of onset was 2.8 (1.3, 4.3) years. The confluent B-line, consolidation and pleural effusion detected by lung ultrasound were 309 cases (73.2%), 232 cases (55.0%) and 16 cases (3.8%), respectively, and the size of consolidation was 3.0 (0, 11.0) mm. One hundred and ten children (26.1%) with CAP completed chest CT. There were 90 cases with signs of pneumonia in chest CT and 20 cases without signs of pneumonia. Lasso was used for feature selection.Lung consolidation (OR=2.46), bilateral lung consolidation (OR=1.16) and confluent B-line (OR=1.34) were the main index. With random forest classifier, the accuracy of models using full variables and Lasso-selected variables were 0.79 (95%CI 0.70-0.86) and 0.79 (95%CI 0.70-0.86), the sensitivity were 0.81 and 0.81, and the specificity were 0.75 and 0.70, and the area under curve were 0.87 (95%CI 0.81-0.94, P<0.001) and 0.84 (95%CI 0.76-0.91, P<0.001), respectively. There were 97 cases in severe group and 325 cases in mild group. Compared with the mild group, the detection rate of consolidation, multiple consolidation, the size of consolidation and the size of consolidation was adjusted by body surface area (consolidation size/body surface area) in severe group were higher (66 cases (68.0%) vs. 166 cases (51.1%), 42 cases (43.3%) vs. 93 cases (28.6%), 8.0 (0, 17.0) vs. 1.0 (0, 9.0) mm, 12.5 (0, 24.6) vs. 2.1 (0, 17.6), χ2=8.59, 9.98, Z=14.40, 12.79, all P<0.05). Using lung ultrasound lung consolidation size and consolidation size/body surface area to predict the severe CAP, the optimal cut-off value were 6.7 mm and 10.2, the accuracy was 0.80 (95%CI 0.75-0.83) and 0.89 (95%CI 0.86-0.92), the sensitivity was 0.99 and 0.99, the specificity was 0.14 and 0.56, respectively, and the area under the curve was 0.66 (95%CI 0.60-0.72, P<0.001) and 0.76 (95%CI 0.70-0.83, P<0.001), respectively. The area under the curve of consolidation size/body surface area was higher than that of consolidation size (Z=5.50, P<0.001). Conclusions: Consolidation and confluent B-line, are important index for lung ultrasound diagnosis of CAP in children. The actual consolidation size adjusted by body surface area is superior to the size of consolidation in predicting severe CAP.

Enhancing pediatric pneumonia diagnosis through masked autoencoders

Pneumonia, an inflammatory lung condition primarily triggered by bacteria, viruses, or fungi, presents distinctive challenges in pediatric cases due to the unique characteristics of the respiratory system and the potential for rapid deterioration. Timely diagnosis is crucial, particularly in children under 5, who have immature immune systems, making them more susceptible to pneumonia. While chest X-rays are indispensable for diagnosis, challenges arise from subtle radiographic findings, varied clinical presentations, and the subjectivity of interpretations, especially in pediatric cases. Deep learning, particularly transfer learning, has shown promise in improving pneumonia diagnosis by leveraging large labeled datasets. However, the scarcity of labeled data for pediatric chest X-rays presents a hurdle in effective model training. To address this challenge, we explore the potential of self-supervised learning, focusing on the Masked Autoencoder (MAE). By pretraining the MAE model on adult chest X-ray images and fine-tuning the pretrained model on a pediatric pneumonia chest X-ray dataset, we aim to overcome data scarcity issues and enhance diagnostic accuracy for pediatric pneumonia. The proposed approach demonstrated competitive performance an AUC of 0.996 and an accuracy of 95.89% in distinguishing between normal and pneumonia. Additionally, the approach exhibited high AUC values (normal: 0.997, bacterial pneumonia: 0.983, viral pneumonia: 0.956) and an accuracy of 93.86% in classifying normal, bacterial pneumonia, and viral pneumonia. This study also investigated the impact of different masking ratios during pretraining and explored the labeled data efficiency of the MAE model, presenting enhanced diagnostic capabilities for pediatric pneumonia.

A deep learning feature extraction-based hybrid approach for detecting pediatric pneumonia in chest X-ray images

Pneumonia is a disease caused by bacteria, viruses, and fungi that settle in the alveolar sacs of the lungs and can lead to serious health complications in humans. Early detection of pneumonia is necessary for early treatment to manage and cure the disease. Recently, machine learning-based pneumonia detection methods have focused on pneumonia in adults. Machine learning relies on manual feature engineering, whereas deep learning can automatically detect and extract features from data. This study proposes a deep learning feature extraction-based hybrid approach that combines deep learning and machine learning to detect pediatric pneumonia, which is difficult to standardize. The proposed hybrid approach enhances the accuracy of detecting pediatric pneumonia and simplifies the approach by eliminating the requirement for advanced feature extraction. The experiments indicate that the hybrid approach using a Medium Neural Network based on AlexNet feature extraction achieved a 97.9% accuracy rate and 98.0% sensitivity rate. The results show that the proposed approach achieved higher accuracy rates than state-of-the-art approaches.

Lung ultrasound: A potential tool in the diagnosis of ventilator-associated pneumonia in pediatric intensive care units

PURPOSE

Ventilator-associated pneumonia (VAP) is a common healthcare-associated infection in pediatric intensive care unit (PICU), increasing mortality, antibiotics use and duration of ventilation and hospitalization. VAP diagnosis is based on clinical and chest X-ray (CXR) signs defined by the 2018 Center for Disease Control (gold standard). However, CXR induces repetitive patients' irradiation and technical limitations. This study aimed to investigate if lung ultrasound (LUS) can substitute CXR in the VAP diagnosis.

METHODS

A monocentric and prospective study was conducted in a French tertiary care hospital. Patients under 18-year-old admitted to PICU between November 2018 and July 2020 with invasive mechanical ventilation for more than 48 h were included. The studied LUS signs were consolidations, dynamic air bronchogram, subpleural consolidations (SPC), B-lines, and pleural effusion. The diagnostic values of each sign associated with clinical signs (cCDC) were compared to the gold standard approach. LUS, chest X-ray, and clinical score were performed daily.

RESULTS

Fifty-seven patients were included. The median age was 8 [3-34] months. Nineteen (33%) children developed a VAP. In patients with VAP, B-Lines, and consolidations were highly frequent (100 and 68.8%) and, associated with cCDC, were highly sensitive (100 [79-100] % and 88 [62-98] %, respectively) and specific (95.5 [92-98] % and 98 [95-99] %, respectively). Other studied signs, including SPC, showed high specificity (>97%) but low sensibility (<50%).

CONCLUSION

LUS seems to be a powerful tool for VAP diagnosis in children with a clinical suspicion, efficiently substituting CXR, and limiting children's exposure to ionizing radiations.

Accuracy and inter-rater agreement among practitioners using quick thoracic ultrasonography to diagnose calf pneumonia

BACKGROUND

Thoracic ultrasonography (TUS) is a commonly used tool for on-farm detection of pneumonia in calves. Different scanning methods have been described, but the performance of novice practitioners after training has not been documented.

METHODS

In this study, 38 practitioners performed quick TUS (qTUS) on 18-23 calves each. Pneumonia was defined as lung consolidation 1 cm or more in depth. Diagnostic parameters (accuracy [Acc], sensitivity [Se] and specificity [Sp]) were compared to those of an experienced operator. Cohen's kappa and Krippendorff's alpha (Kalpha) were determined. The potential effects of training and exam sessions on performance were evaluated.

RESULTS

The average relative Se and Sp were 0.66 (standard deviation [SD] = 0.26; minimum [Min.]-Maximum [Max.] = 0-1) and 0.71 (SD = 0.19; Min.-Max. = 0.25-1), respectively. The average relative Acc was 0.73 (SD = 0.11; Min.-Max. = 0.52-0.96). Over all sessions, Cohen's kappa averaged 0.40 (SD = 0.24; Min.-Max. = 0.014-0.90) and Kalpha was 0.24 (95% confidence interval [CI]: 0.20-0.27), indicating 'fair' agreement. Calf age and housing influenced Se and Sp. Supervised practical training improved Se by 17.5% (95% CI: 0.01-0.34).

LIMITATIONS

The separate effects of calf age and housing could not be determined.

CONCLUSION

This study showed that qTUS, like any other clinical skill, has a learning curve, and variability in performance can be substantial. Adequate training and certification of one's skill are recommended to assure good diagnostic accuracy.

Radiological assessment of immunotherapy effects and immune checkpoint-related pneumonitis for lung cancer

Immune checkpoint inhibitors (ICIs) therapy have revolutionized advanced lung cancer care. Interestingly, the host responses for patients received ICIs therapy are distinguishing from those with cytotoxic drugs, showing potential initial transient worsening of disease burden, pseudoprogression and delayed time to treatment response. Thus, a new imaging criterion to evaluate the response for immunotherapy should be developed. ICIs treatment is associated with unique adverse events, including potential life-threatening immune checkpoint inhibitor-related pneumonitis (ICI-pneumonitis) if treated patients are not managed promptly. Currently, the diagnosis and clinical management of ICI-pneumonitis remain challenging. As the clinical manifestation is often nonspecific, computed tomography (CT) scan and X-ray films play important roles in diagnosis and triage. This article reviews the complications of immunotherapy in lung cancer and illustrates various radiologic patterns of ICI-pneumonitis. Additionally, it is tried to differentiate ICI-pneumonitis from other pulmonary pathologies common to lung cancer such as radiation pneumonitis, bacterial pneumonia and coronavirus disease of 2019 (COVID-19) infection in recent months. Maybe it is challenging to distinguish radiologically but clinical presentation may help.

Radiographic Imaging of Community-Acquired Pneumonia: A Case-Based Review

The chest radiograph is the most common imaging examination performed in most radiology departments, and one of the more common indications for these studies is suspected infection. Radiologists must therefore be aware of less common radiographic patterns of pulmonary infection if they are to add value in the interpretation of chest radiographs for this indication. This review uses a case-based format to illustrate a range of imaging findings that can be associated with acute pulmonary infection and highlight findings that should prompt investigation for diseases other than community-acquired pneumonia to prevent misdiagnosis and delays in appropriate management.

Evaluation of the diagnostic performance of physician lung ultrasound versus chest radiography for pneumonia diagnosis in a peri-urban South African cohort

BACKGROUND

Lung ultrasound (US), which is radiation-free and cheaper than chest radiography (CXR), may be a useful modality for the diagnosis of pediatric pneumonia, but there are limited data from low- and middle-income countries.

OBJECTIVES

The aim of this study was to evaluate the diagnostic performance of non-radiologist, physician-performed lung US compared to CXR for pneumonia in children in a resource-constrained, African setting.

MATERIALS AND METHODS

Children under 5 years of age enrolled in a South African birth cohort study, the Drakenstein Child Health Study, who presented with clinically defined pneumonia and had a CXR performed also had a  lung US performed by a study doctor. Each modality was reported by two readers, using standardized methodology. Agreement between modalities, accuracy (sensitivity and specificity) of lung US and inter-rater agreement were assessed. Either consolidation or any abnormality (consolidation or interstitial picture) was considered as endpoints. In the 98 included cases (median age: 7.2 months; 53% male; 69% hospitalized), prevalence was 37% vs. 39% for consolidation and 52% vs. 76% for any abnormality on lung US and CXR, respectively. Agreement between modalities was poor for consolidation (observed agreement=61%, Kappa=0.18, 95% confidence interval [95% CI]: - 0.02 to 0.37) and for any abnormality (observed agreement=56%, Kappa=0.10, 95% CI: - 0.07 to 0.28). Using CXR as the reference standard, sensitivity of lung US was low for consolidation (47%, 95% CI: 31-64%) or any abnormality (5%, 95% CI: 43-67%), while specificity was moderate for consolidation (70%, 95% CI: 57-81%), but lower for any abnormality (58%, 95% CI: 37-78%). Overall inter-observer agreement of CXR was poor (Kappa=0.25, 95% CI: 0.11-0.37) and was significantly lower than the substantial agreement of lung US (Kappa=0.61, 95% CI: 0.50-0.75). Lung US demonstrated better agreement than CXR for all categories of findings, showing a significant difference for consolidation (Kappa=0.72, 95% CI: 0.58-0.86 vs. 0.32, 95% CI: 0.13-0.51).

CONCLUSION

Lung US identified consolidation with similar frequency to CXR, but there was poor agreement between modalities. The significantly higher inter-observer agreement of LUS compared to CXR supports the utilization of lung US by clinicians in a low-resource setting.

Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies

BACKGROUND

Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primarily been compared with radiologists, whose diagnosis can be not completely accurate. Therefore, we evaluated the accuracy of DL in diagnosing pneumonia on CXR using a more robust reference diagnosis.

METHODS

We trained a DL convolutional neural network model to diagnose pneumonia and evaluated its accuracy in two prospective pneumonia cohorts including 430 patients, for whom the reference diagnosis was determined a posteriori by a multidisciplinary expert panel using multimodal data. The performance of the DL model was compared with that of senior radiologists and emergency physicians reviewing CXRs and that of radiologists reviewing computed tomography (CT) performed concomitantly.

RESULTS

Radiologists and DL showed a similar accuracy on CXR for both cohorts (p ≥ 0.269): cohort 1, radiologist 1 75.5% (95% confidence interval 69.1-80.9), radiologist 2 71.0% (64.4-76.8), DL 71.0% (64.4-76.8); cohort 2, radiologist 70.9% (64.7-76.4), DL 72.6% (66.5-78.0). The accuracy of radiologists and DL was significantly higher (p ≤ 0.022) than that of emergency physicians (cohort 1 64.0% [57.1-70.3], cohort 2 63.0% [55.6-69.0]). Accuracy was significantly higher for CT (cohort 1 79.0% [72.8-84.1], cohort 2 89.6% [84.9-92.9]) than for CXR readers including radiologists, clinicians, and DL (all p-values < 0.001).

CONCLUSIONS

When compared with a robust reference diagnosis, the performance of AI models to identify pneumonia on CXRs was inferior than previously reported but similar to that of radiologists and better than that of emergency physicians.

RELEVANCE STATEMENT

The clinical relevance of AI models for pneumonia diagnosis may have been overestimated. AI models should be benchmarked against robust reference multimodal diagnosis to avoid overestimating its performance.

TRIAL REGISTRATION

NCT02467192 , and NCT01574066 .

KEY POINT

• We evaluated an openly-access convolutional neural network (CNN) model to diagnose pneumonia on CXRs. • CNN was validated against a strong multimodal reference diagnosis. • In our study, the CNN performance (area under the receiver operating characteristics curve 0.74) was lower than that previously reported when validated against radiologists' diagnosis (0.99 in a recent meta-analysis). • The CNN performance was significantly higher than emergency physicians' (p ≤ 0.022) and comparable to that of board-certified radiologists (p ≥ 0.269).

Acute muscle wasting is associated with poor prognosis in older adults with severe community-acquired pneumonia

PURPOSE

To investigate the impact of acute muscle wasting on 90-day mortality in older patients with severe pneumonia using ultrasound and chest computed tomography (CT).

METHODS

Quadriceps muscle layer thickness was measured via ultrasound on days 1, 7, and 14, and cross-sectional area of the erector spinae muscle was assessed using chest CT on days 1 and 14 in patients aged ≥ 65 years old. The primary outcome was all-cause 90-day mortality. Receiver operating characteristic curves were conducted for muscle loss to predict 90-day mortality. Cox proportional hazard models and Kaplan-Meier survival curves were employed to evaluate the association between muscle loss and 90-day mortality.

RESULTS

Sixty-two patients were enrolled with median age of 80.2 years, 29 (46.8%) were men and 28 (45.2%) patients died. Muscle mass measured using ultrasound and CT decreased significantly from baseline to day 14 in the non-survivor group. Muscle loss assessed by ultrasound (with minimum and maximum pressure) and CT independently predicted all-cause 90-day mortality (adjusted hazard ratios = 1.497, 1.400 and 1.082; P < 0.001, P = 0.002, and P = 0.004; respectively), and cutoff values of muscle loss were 0.34 cm, 0.11 cm and 4.92 cm2, correspondingly. A higher muscle loss had an increased risk of 90-day mortality.

CONCLUSIONS

Acute muscle wasting assessed by ultrasound and chest CT persisted for 14 days and was an independent predictor of adverse outcomes in older patients with severe pneumonia. A greater decline in muscle mass was associated with a higher 90-day mortality risk.

Cost of Pediatric Pneumonia Episodes With or Without Chest Radiography

BACKGROUND AND OBJECTIVES

Despite its routine use, it is unclear whether chest radiograph (CXR) is a cost-effective strategy in the workup of community-acquired pneumonia (CAP) in the pediatric emergency department (ED). We sought to assess the costs of CAP episodes with and without CXR among children discharged from the ED.

METHODS

This was a retrospective cohort study within the Healthcare Cost and Utilization Project State ED and Inpatient Databases of children aged 3 months to 18 years with CAP discharged from any EDs in 8 states from 2014 to 2019. We evaluated total 28-day costs after ED discharge, including the index visit and subsequent care. Mixed-effects linear regression models adjusted for patient-level variables and illness severity were performed to evaluate the association between CXR and costs.

RESULTS

We evaluated 225c781 children with CAP, and 86.2% had CXR at the index ED visit. Median costs of the 28-day episodes, index ED visits, and subsequent visits were $314 (interquartile range [IQR] 208-497), $288 (IQR 195-433), and $255 (IQR 133-637), respectively. There was a $33 (95% confidence interval [CI] 22-44) savings over 28-days per patient for those who received a CXR compared with no CXR after adjusting for patient-level variables and illness severity. Costs during subsequent visits ($26 savings, 95% CI 16-36) accounted for the majority of the savings as compared with the index ED visit ($6, 95% CI 3-10).

CONCLUSIONS

Performance of CXR for CAP diagnosis is associated with lower costs when considering the downstream provision of care among patients who require subsequent health care after initial ED discharge.

Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia From Chest X-Ray Images

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

Efficient pneumonia detection using Vision Transformers on chest X-rays

Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.

PLUS-IS-LESS project: Procalcitonin and Lung UltraSonography-based antibiotherapy in patients with Lower rESpiratory tract infection in Swiss Emergency Departments: study protocol for a pragmatic stepped-wedge cluster-randomized trial

BACKGROUND

Lower respiratory tract infections (LRTIs) are among the most frequent infections and a significant contributor to inappropriate antibiotic prescription. Currently, no single diagnostic tool can reliably identify bacterial pneumonia. We thus evaluate a multimodal approach based on a clinical score, lung ultrasound (LUS), and the inflammatory biomarker, procalcitonin (PCT) to guide prescription of antibiotics. LUS outperforms chest X-ray in the identification of pneumonia, while PCT is known to be elevated in bacterial and/or severe infections. We propose a trial to test their synergistic potential in reducing antibiotic prescription while preserving patient safety in emergency departments (ED).

METHODS

The PLUS-IS-LESS study is a pragmatic, stepped-wedge cluster-randomized, clinical trial conducted in 10 Swiss EDs. It assesses the PLUS algorithm, which combines a clinical prediction score, LUS, PCT, and a clinical severity score to guide antibiotics among adults with LRTIs, compared with usual care. The co-primary endpoints are the proportion of patients prescribed antibiotics and the proportion of patients with clinical failure by day 28. Secondary endpoints include measurement of change in quality of life, length of hospital stay, antibiotic-related side effects, barriers and facilitators to the implementation of the algorithm, cost-effectiveness of the intervention, and identification of patterns of pneumonia in LUS using machine learning.

DISCUSSION

The PLUS algorithm aims to optimize prescription of antibiotics through improved diagnostic performance and maximization of physician adherence, while ensuring safety. It is based on previously validated tests and does therefore not expose participants to unforeseeable risks. Cluster randomization prevents cross-contamination between study groups, as physicians are not exposed to the intervention during or before the control period. The stepped-wedge implementation of the intervention allows effect calculation from both between- and within-cluster comparisons, which enhances statistical power and allows smaller sample size than a parallel cluster design. Moreover, it enables the training of all centers for the intervention, simplifying implementation if the results prove successful. The PLUS algorithm has the potential to improve the identification of LRTIs that would benefit from antibiotics. When scaled, the expected reduction in the proportion of antibiotics prescribed has the potential to not only decrease side effects and costs but also mitigate antibiotic resistance.

TRIAL REGISTRATION

This study was registered on July 19, 2022, on the ClinicalTrials.gov registry using reference number: NCT05463406.

TRIAL STATUS

Recruitment started on December 5, 2022, and will be completed on November 3, 2024. Current protocol version is version 3.0, dated April 3, 2023.

Photon Counting Computed Tomography with the Radiation Dose of a Chest X-Ray: Feasibility and Diagnostic Yield

INTRODUCTION

Photon counting (PC) detectors allow a reduction of the radiation dose in CT. Chest X-ray (CXR) is known to have a low sensitivity and specificity for detection of pneumonic infiltrates. The aims were to establish an ultra-low-dose CT (ULD-CT) protocol at a PC-CT with the radiation dose comparable to the dose of a CXR and to evaluate its clinical yield in patients with suspicion of pneumonia.

METHODS

A ULD-CT protocol was established with the aim to meet the radiation dose of a CXR. In this retrospective study, all adult patients who received a ULD-CT of the chest with suspected pneumonia were included. Radiation exposure of ULD-CT and CXR was calculated. The clinical significance (new diagnosis, change of therapy, additional findings) and limitations were evaluated by a radiologist and a pulmonologist considering previous CXR and clinical data.

RESULTS

Twenty-seven patients (70% male, mean age 68 years) were included. With our ULD-CT protocol, the radiation dose of a CXR could be reached (mean radiation exposure 0.11 mSv). With ULD-CT, the diagnosis changed in 11 patients (41%), there were relevant additional findings in 4 patients (15%), an infiltrate (particularly fungal infiltrate under immunosuppression) could be ruled out with certainty in 10 patients (37%), and the therapy changed in 10 patients (37%). Two patients required an additional CT with contrast medium to rule out a pulmonary embolism or pleural empyema.

CONCLUSIONS

With ULD-CT, the radiation dose of a CXR could be reached while the clinical impact is higher with change in diagnosis in 41%.

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.

The correlation between lung ultrasound scores and outcomes of high-flow nasal cannula therapy in infants with severe pneumonia

OBJECTIVE

The study aimed to explore the effectiveness of bedside lung ultrasound (LUS) combined with the PaO2/FiO2 (P/F) ratio in evaluating the outcomes of high-flow nasal cannula (HFNC) therapy in infants with severe pneumonia.

METHODS

This retrospective study analyzed the clinical data of 150 infants diagnosed with severe pneumonia and treated with HFNC therapy at our hospital from January 2021 to December 2021. These patients were divided into two groups based on their treatment outcomes: the HFNC success group (n = 112) and the HFNC failure group (n = 38). LUS was utilized to evaluate the patients' lung conditions, and blood gas results were recorded for both groups upon admission and after 12 h of HFNC therapy.

RESULTS

At admission, no significant differences were observed between the two groups in terms of age, gender, respiratory rate, partial pressure of oxygen, and partial pressure of carbon dioxide. However, the P/F ratios at admission and after 12 h of HFNC therapy were significantly lower in the HFNC failure group (193.08 ± 49.14, 228.63 ± 80.17, respectively) compared to the HFNC success group (248.51 ± 64.44, 288.93 ± 57.17, respectively) (p < 0.05). Likewise, LUS scores at admission and after 12 h were significantly higher in the failure group (18.42 ± 5.3, 18.03 ± 5.36, respectively) than in the success group (15.09 ± 4.66, 10.71 ± 3.78, respectively) (p < 0.05). Notably, in the success group, both P/F ratios and LUS scores showed significant improvement after 12 h of HFNC therapy, a trend not observed in the failure group. Multivariate regression analysis indicated that lower P/F ratios and higher LUS scores at admission and after 12 h were predictive of a greater risk of HFNC failure. ROC analysis demonstrated that an LUS score > 20.5 at admission predicted HFNC therapy failure with an AUC of 0.695, a sensitivity of 44.7%, and a specificity of 91.1%. A LUS score > 15.5 after 12 h of HFNC therapy had an AUC of 0.874, with 65.8% sensitivity and 89.3% specificity. An admission P/F ratio < 225.5 predicted HFNC therapy failure with an AUC of 0.739, 60.7% sensitivity, and 71.1% specificity, while a P/F ratio < 256.5 after 12 h of HFNC therapy had an AUC of 0.811, 74.1% sensitivity, and 73.7% specificity.

CONCLUSION

Decreased LUS scores and increased P/F ratio demonstrate a strong correlation with successful HFNC treatment outcomes in infants with severe pneumonia. These findings may provide valuable support for clinicians in managing such cases.

Pre-existing Interstitial Lung Abnormalities and Immune Checkpoint Inhibitor-Related Pneumonitis in Solid Tumors: A Retrospective Analysis

BACKGROUND

Immune checkpoint inhibitors (ICIs) have demonstrated efficacy over previous cytotoxic chemotherapies in clinical trials among various tumors. Despite their favorable outcomes, they are associated with a unique set of toxicities termed as immune-related adverse events (irAEs). Among the toxicities, ICI-related pneumonitis has poor outcomes with little understanding of its risk factors. This retrospective study aimed to investigate whether pre-existing interstitial lung abnormality (ILA) is a potential risk factor for ICI-related pneumonitis.

MATERIALS AND METHODS

Patients with non-small cell lung cancer, malignant melanoma, renal cell carcinoma, and gastric cancer, who was administered either nivolumab, pembrolizumab, or atezolizumab between September 2014 and January 2019 were retrospectively reviewed. Information on baseline characteristics, computed tomography findings before administration of ICIs, clinical outcomes, and irAEs were collected from their medical records. Pre-existing ILA was categorized based on previous studies.

RESULTS

Two-hundred-nine patients with a median age of 68 years were included and 23 (11.0%) developed ICI-related pneumonitis. While smoking history and ICI agents were associated with ICI-related pneumonitis (P = .005 and .044, respectively), the categories of ILA were not associated with ICI-related pneumonitis (P = .428). None of the features of lung abnormalities were also associated with ICI-related pneumonitis. Multivariate logistic analysis indicated that smoking history was the only significant predictor of ICI-related pneumonitis (P = .028).

CONCLUSION

This retrospective study did not demonstrate statistically significant association between pre-existing ILA and ICI-related pneumonitis, nor an association between radiologic features of ILA and ICI-related pneumonitis. Smoking history was independently associated with ICI-related pneumonitis. Further research is warranted for further understanding of the risk factors of ICI-related pneumonitis.

Attention-enhanced architecture for improved pneumonia detection in chest X-ray images

In this paper, we propose an attention-enhanced architecture for improved pneumonia detection in chest X-ray images. A unique attention mechanism is integrated with ResNet to highlight salient features crucial for pneumonia detection. Rigorous evaluation demonstrates that our attention mechanism significantly enhances pneumonia detection accuracy, achieving a satisfactory result of 96% accuracy. To address the issue of imbalanced training samples, we integrate an enhanced focal loss into our architecture. This approach assigns higher weights to minority classes during training, effectively mitigating data imbalance. Our model's performance significantly improves, surpassing that of traditional approaches such as the pretrained ResNet-50 model. Our attention-enhanced architecture thus presents a powerful solution for pneumonia detection in chest X-ray images, achieving an accuracy of 98%. By integrating enhanced focal loss, our approach effectively addresses imbalanced training sample. Comparative analysis underscores the positive impact of our model's spatial and channel attention modules. Overall, our study advances pneumonia detection in medical imaging and underscores the potential of attention-enhanced architectures for improved diagnostic accuracy and patient outcomes. Our findings offer valuable insights into image diagnosis and pneumonia prevention, contributing to future research in medical imaging and machine learning.