Three-dimensional ultrashort echo time magnetic resonance imaging in pediatric patients with pneumonia: a comparative study

BACKGROUND

UTE has been used to depict lung parenchyma. However, the insufficient discussion of its performance in pediatric pneumonia compared with conventional sequences is a gap in the existing literature. The objective of this study was to compare the diagnostic value of 3D-UTE with that of 3D T1-GRE and T2-FSE sequences in young children diagnosed with pneumonia.

METHODS

Seventy-seven eligible pediatric patients diagnosed with pneumonia at our hospital, ranging in age from one day to thirty-five months, were enrolled in this study from March 2021 to August 2021. All patients underwent imaging using a 3 T pediatric MR scanner, which included three sequences: 3D-UTE, 3D-T1 GRE, and T2-FSE. Subjective analyses were performed by two experienced pediatric radiologists based on a 5-point scale according to six pathological findings (patchy shadows/ground-glass opacity (GGO), consolidation, nodule, bulla/cyst, linear opacity, and pleural effusion/thickening). Additionally, they assessed image quality, including the presence of artifacts, and evaluated the lung parenchyma. Interrater agreement was assessed using intraclass correlation coefficients (ICCs). Differences among the three sequences were evaluated using the Wilcoxon signed-rank test.

RESULTS

The visualization of pathologies in most parameters (patchy shadows/GGO, consolidation, nodule, and bulla/cyst) was superior with UTE compared to T2-FSE and T1 GRE. The visualization scores for linear opacity were similar between UTE and T2-FSE, and both were better than T1-GRE. In the case of pleural effusion/thickening, T2-FSE outperformed the other sequences. However, statistically significant differences between UTE and other sequences were only observed for patchy shadows/GGO and consolidation. The overall image quality was superior or at least comparable with UTE compared to T2-FSE and T1-GRE. Interobserver agreements for all visual assessments were significant and rated "substantial" or "excellent."

CONCLUSIONS

In conclusion, UTE MRI is a useful and promising method for evaluating pediatric pneumonia, as it provided better or similar visualization of most imaging findings compared with T2-FSE and T1-GRE. We suggest that the UTE MRI is well-suited for pediatric population, especially in younger children with pneumonia who require longitudinal and repeated imaging for clinical care or research and are susceptible to ionizing radiation.

Comparison of Ultrasound and CT Imaging for the Diagnosis of Coronavirus Disease and Influenza A Pneumonia

OBJECTIVE

The outbreak of coronavirus disease (COVID-19) coincided with the season of influenza A pneumonia, a common respiratory infectious disease. Therefore, this study compared ultrasonography and computed tomography (CT) for the diagnosis of the two diseases.

METHODS

Patients with COVID-19 or influenza A infection hospitalized at our hospital were included. The patients were examined by ultrasonography every day. The CT examination results within 1 day before and after the day of the highest ultrasonography score were selected as the controls. The similarities and differences between the ultrasonography and CT results in the two groups were compared.

RESULTS

There was no difference between the ultrasonography and CT scores (P = .307) for COVID-19, while there was a difference between ultrasonography and CT scores for influenza A pneumonia (P = .024). The ultrasonography score for COVID-19 was higher than that for influenza A pneumonia (P = .000), but there was no difference between the CT scores (P = .830). For both diseases, there was no difference in ultrasonography and CT scores between the left and right lungs; there were differences between the CT scores of the upper and middle lobes, as well as between the upper and lower lobes of the lungs; however, there was no difference between the lower and middle lobes of the lungs.

CONCLUSION

Ultrasonography is equivalent to the gold standard CT for diagnosing and monitoring the progression of COVID-19. Because of its convenience, ultrasonography has important application value. Furthermore, the diagnostic value of ultrasonography for COVID-19 is higher than that for influenza A pneumonia.

Comparison between original SARS-CoV-2 strain and omicron variant on thin-section chest CT imaging of COVID-19 pneumonia

OBJECTIVES

We investigated different computed tomography (CT) features between Omicron-variant and original-strain SARS-CoV‑2 pneumonia to facilitate the clinical management.

MATERIALS AND METHODS

Medical records were retrospectively reviewed to select patients with original-strain SARS-CoV‑2 pneumonia from February 22 to April 22, 2020, or Omicron-variant SARS-CoV‑2 pneumonia from March 26 to May 31, 2022. Data on the demographics, comorbidities, symptoms, clinical types, and CT features were compared between the two groups.

RESULTS

There were 62 and 78 patients with original-strain or Omicron-variant SARS-CoV‑2 pneumonia, respectively. There were no differences between the two groups in terms of age, sex, clinical types, symptoms, and comorbidities. The main CT features differed between the two groups (p = 0.003). There were 37 (59.7%) and 20 (25.6%) patients with ground-glass opacities (GGO) in the original-strain and Omicron-variant pneumonia, respectively. A consolidation pattern was more frequently observed in the Omicron-variant than original-strain pneumonia (62.8% vs. 24.2%). There was no difference in crazy-paving pattern between the original-strain and Omicron-variant pneumonia (16.1% vs. 11.6%). Pleural effusion was observed more often in Omicron-variant pneumonia, while subpleural lesions were more common in the original-strain pneumonia. The CT score in the Omicron-variant group was higher than that in the original-strain group for critical-type (17.00, 16.00-18.00 vs. 16.00, 14.00-17.00, p = 0.031) and for severe-type (13.00, 12.00-14.00 vs 12.00, 10.75-13.00, p = 0.027) pneumonia.

CONCLUSION

The main CT finding of the Omicron-variant SARS-CoV‑2 pneumonia included consolidations and pleural effusion. By contrast, CT findings of original-strain SARS-CoV‑2 pneumonia showed frequent GGO and subpleural lesions, but without pleural effusion. The CT scores were also higher in the critical and severe types of Omicron-variant than original-strain pneumonia.

Differential clinical and CT imaging features of pneumonic-type primary pulmonary lymphoma and pneumonia: a retrospective multicentre observational study

INTRODUCTION

Pneumonic-type primary pulmonary lymphoma (PPL) is often misdiagnosed as pneumonia in clinical practice. However, this disease requires different treatments, which calls for a correct diagnosis.

MATERIALS AND METHODS

A total of 227 patients with pneumonic-type PPL (n=72) and pneumonia (n=155) from 7 institutions were retrospectively enrolled between January 2017 and January 2022. Clinical features (age, sex, cough, sputum, fever, haemoptysis, chest pain, smoking, weight loss and laboratory results (haemoglobin, white blood cell count, C reactive protein level and erythrocyte sedimentation rate)) and CT imaging characteristics (air bronchogram, bronchiectasis, halo sign, pleural traction, pleural effusion, lymphadenopathy, lesion maximum diameter and CT attenuation value) were analysed. Receiver operating characteristic curve analysis was performed for model construction based on independent predictors in identifying pneumonic-type PPL. In addition, we used a calibration curve and decision curve analysis to estimate the diagnostic efficiency of the model.

RESULTS

The patients with pneumonia showed a higher prevalence of sputum, fever, leucocytosis and elevation of C reactive protein level than those with pneumonic-type PPL (p=0.002, p<0.001, p=0.011 and p<0.001, respectively). Bronchiectasis, halo sign and higher CT attenuation value were more frequently present in pneumonic-type PPL than in pneumonia (all p<0.001). Pleural effusion was more commonly observed in patients with pneumonia than those with pneumonic-type PPL (p<0.001). Also, sputum, fever, elevation of C reactive protein level, halo sign, bronchiectasis, pleural effusion and CT attenuation value were the independent predictors of the presence of pneumonic-type PPL with an area under the curve value of 0.908 (95% CI, 0.863 to 0.942).

CONCLUSION

Pneumonic-type PPL and pneumonia have different clinical and imaging features. These differential features could be beneficial in guiding early diagnosis and subsequent initiation of therapy.

[POCUS : diagnosis of pneumonia by lung ultrasonography]

Point-Of-Care Ultrasonography (POCUS) has emerged to support the diagnosis process and management strategies. Its use for the diagnosis of pneumonia has been shown to be reliable and effective over the past decade. Various ultrasonography patterns exist, none of which are pathognomonic for pneumonia. Therefore, POCUS findings must be interpreted in association with the clinical setting. POCUS enables early identification of complications such as parapneumonic effusion and pulmonary abscess. It also provides guidance for invasive procedure such as thoracocentesis and pleural drainage. The forthcoming results of the Swiss OCTOPLUS study will provide data on the clinical and economic impact of a diagnostic strategy based on targeted lung ultrasonography.

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system

To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists was improved by referring to our model. Our datasets contained chest X-rays (CXRs) for the following three categories: normal (NORMAL), non-COVID-19 pneumonia (PNEUMONIA), and COVID-19 pneumonia (COVID). We used two public datasets and private dataset collected from eight hospitals for the development and external validation of our DL model (26,393 CXRs). Eight radiologists performed two reading sessions: one session was performed with reference to CXRs only, and the other was performed with reference to both CXRs and the results of the DL model. The evaluation metrics for the reading session were accuracy, sensitivity, specificity, and area under the curve (AUC). The accuracy of our DL model was 0.733, and that of the eight radiologists without DL was 0.696 ± 0.031. There was a significant difference in AUC between the radiologists with and without DL for COVID versus NORMAL or PNEUMONIA (p = 0.0038). Our DL model alone showed better diagnostic performance than that of most radiologists. In addition, our model significantly improved the diagnostic performance of radiologists for COVID versus NORMAL or PNEUMONIA.

Association of Radiology Findings with Etiology of Community Acquired Pneumonia among Children

OBJECTIVE

To evaluate the association between consolidation on chest radiograph and typical bacterial etiology of childhood community acquired pneumonia (CAP) in the Etiology of Pneumonia in the Community study.

STUDY DESIGN

Hospitalized children <18 years of age with CAP enrolled in the Etiology of Pneumonia in the Community study at 3 children's hospitals between January 2010 and June 2012 were included. Testing of blood and respiratory specimens used multiple modalities to identify typical and atypical bacterial, or viral infection. Study radiologists classified chest radiographs (consolidation, other infiltrates [interstitial and/or alveolar], pleural effusion) using modified World Health Organization pneumonia criteria. Infiltrate patterns were compared according to etiology of CAP.

RESULTS

Among 2212 children, there were 1302 (59%) with consolidation with or without other infiltrates, 910 (41%) with other infiltrates, and 296 (13%) with pleural effusion. In 1795 children, at least 1 pathogen was detected. Among these patients, consolidation (74%) was the most frequently observed pattern (74% in typical bacterial CAP, 58% in atypical bacterial CAP, and 54% in viral CAP). Positive and negative predictive values of consolidation for typical bacterial CAP were 12% (95% CI 10%-15%) and 96% (95% CI 95%-97%) respectively. In a multivariable model, typical bacterial CAP was associated with pleural effusion (OR 7.3, 95% CI 4.7-11.2) and white blood cell ≥15 000/mL (OR 3.2, 95% CI 2.2-4.9), and absence of wheeze (OR 0.5, 95% CI 0.3-0.8) or viral detection (OR 0.2, 95% CI 0.1-0.4).

CONCLUSIONS

Consolidation predicted typical bacterial CAP poorly, but its absence made typical bacterial CAP unlikely. Pleural effusion was the best predictor of typical bacterial infection, but too uncommon to aid etiology prediction.

The yield of chest X-ray or ultra-low-dose chest-CT in emergency department patients suspected of pulmonary infection without respiratory symptoms or signs

OBJECTIVE

The yield of pulmonary imaging in patients with suspected infection but no respiratory symptoms or signs is probably limited, ultra-low-dose CT (ULDCT) is known to have a higher sensitivity than Chest X-ray (CXR). Our objective was to describe the yield of ULDCT and CXR in patients clinically suspected of infection, but without respiratory symptoms or signs, and to compare the diagnostic accuracy of ULDCT and CXR.

METHODS

In the OPTIMACT trial, patients suspected of non-traumatic pulmonary disease at the emergency department (ED) were randomly allocated to undergo CXR (1210 patients) or ULDCT (1208 patients). We identified 227 patients in the study group with fever, hypothermia, and/or elevated C-reactive protein (CRP) but no respiratory symptoms or signs, and estimated ULDCT and CXR sensitivity and specificity in detecting pneumonia. The final day-28 diagnosis served as the clinical reference standard.

RESULTS

In the ULDCT group, 14/116 (12%) received a final diagnosis of pneumonia, versus 8/111 (7%) in the CXR group. ULDCT sensitivity was significantly higher than that of CXR: 13/14 (93%) versus 4/8 (50%), a difference of 43% (95% CI: 6 to 80%). ULDCT specificity was 91/102 (89%) versus 97/103 (94%) for CXR, a difference of - 5% (95% CI: - 12 to 3%). PPV was 54% (13/24) for ULDCT versus 40% (4/10) for CXR, NPV 99% (91/92) versus 96% (97/101).

CONCLUSION

Pneumonia can be present in ED patients without respiratory symptoms or signs who have a fever, hypothermia, and/or elevated CRP. ULDCT's sensitivity is a significant advantage over CXR when pneumonia has to be excluded.

CLINICAL RELEVANCE STATEMENT

Pulmonary imaging in patients with suspected infection but no respiratory symptoms or signs can result in the detection of clinically significant pneumonia. The increased sensitivity of ultra-low-dose chest CT compared to CXR is of added value in vulnerable and immunocompromised patients.

KEY POINTS

• Clinical significant pneumonia does occur in patients who have a fever, low core body temperature, or elevated CRP without respiratory symptoms or signs. • Pulmonary imaging should be considered in patients with unexplained symptoms or signs of infections. • To exclude pneumonia in this patient group, ULDCT's improved sensitivity is a significant advantage over CXR.

Heterogenous lung inflammation CT patterns distinguish pneumonia and immune checkpoint inhibitor pneumonitis and complement blood biomarkers in acute myeloid leukemia: proof of concept

Background

Immune checkpoint inhibitors (ICI) may cause pneumonitis, resulting in potentially fatal lung inflammation. However, distinguishing pneumonitis from pneumonia is time-consuming and challenging. To fill this gap, we build an image-based tool, and further evaluate it clinically alongside relevant blood biomarkers.

Materials and methods

We studied CT images from 97 patients with pneumonia and 29 patients with pneumonitis from acute myeloid leukemia treated with ICIs. We developed a CT-derived signature using a habitat imaging algorithm, whereby infected lungs are segregated into clusters ("habitats"). We validated the model and compared it with a clinical-blood model to determine whether imaging can add diagnostic value.

Results

Habitat imaging revealed intrinsic lung inflammation patterns by identifying 5 distinct subregions, correlating to lung parenchyma, consolidation, heterogenous ground-glass opacity (GGO), and GGO-consolidation transition. Consequently, our proposed habitat model (accuracy of 79%, sensitivity of 48%, and specificity of 88%) outperformed the clinical-blood model (accuracy of 68%, sensitivity of 14%, and specificity of 85%) for classifying pneumonia versus pneumonitis. Integrating imaging and blood achieved the optimal performance (accuracy of 81%, sensitivity of 52% and specificity of 90%). Using this imaging-blood composite model, the post-test probability for detecting pneumonitis increased from 23% to 61%, significantly (p = 1.5E - 9) higher than the clinical and blood model (post-test probability of 22%).

Conclusion

Habitat imaging represents a step forward in the image-based detection of pneumonia and pneumonitis, which can complement known blood biomarkers. Further work is needed to validate and fine tune this imaging-blood composite model and further improve its sensitivity to detect pneumonitis.

Clinical applications of radiomics in non-small cell lung cancer patients with immune checkpoint inhibitor-related pneumonitis

Immune checkpoint inhibitors (ICIs) modulate the body's immune function to treat tumors but may also induce pneumonitis. Immune checkpoint inhibitor-related pneumonitis (ICIP) is a serious immune-related adverse event (irAE). Immunotherapy is currently approved as a first-line treatment for non-small cell lung cancer (NSCLC), and the incidence of ICIP in NSCLC patients can be as high as 5%-19% in clinical practice. ICIP can be severe enough to lead to the death of NSCLC patients, but there is a lack of a gold standard for the diagnosis of ICIP. Radiomics is a method that uses computational techniques to analyze medical images (e.g., CT, MRI, PET) and extract important features from them, which can be used to solve classification and regression problems in the clinic. Radiomics has been applied to predict and identify ICIP in NSCLC patients in the hope of transforming clinical qualitative problems into quantitative ones, thus improving the diagnosis and treatment of ICIP. In this review, we summarize the pathogenesis of ICIP and the process of radiomics feature extraction, review the clinical application of radiomics in ICIP of NSCLC patients, and discuss its future application prospects.

HDSNE a new unsupervised multiple image database fusion learning algorithm with flexible and crispy production of one database: a proof case study of lung infection diagnose In chest X-ray images

Continuous release of image databases with fully or partially identical inner categories dramatically deteriorates the production of autonomous Computer-Aided Diagnostics (CAD) systems for true comprehensive medical diagnostics. The first challenge is the frequent massive bulk release of medical image databases, which often suffer from two common drawbacks: image duplication and corruption. The many subsequent releases of the same data with the same classes or categories come with no clear evidence of success in the concatenation of those identical classes among image databases. This issue stands as a stumbling block in the path of hypothesis-based experiments for the production of a single learning model that can successfully classify all of them correctly. Removing redundant data, enhancing performance, and optimizing energy resources are among the most challenging aspects. In this article, we propose a global data aggregation scale model that incorporates six image databases selected from specific global resources. The proposed valid learner is based on training all the unique patterns within any given data release, thereby creating a unique dataset hypothetically. The Hash MD5 algorithm (MD5) generates a unique hash value for each image, making it suitable for duplication removal. The T-Distributed Stochastic Neighbor Embedding (t-SNE), with a tunable perplexity parameter, can represent data dimensions. Both the Hash MD5 and t-SNE algorithms are applied recursively, producing a balanced and uniform database containing equal samples per category: normal, pneumonia, and Coronavirus Disease of 2019 (COVID-19). We evaluated the performance of all proposed data and the new automated version using the Inception V3 pre-trained model with various evaluation metrics. The performance outcome of the proposed scale model showed more respectable results than traditional data aggregation, achieving a high accuracy of 98.48%, along with high precision, recall, and F1-score. The results have been proved through a statistical t-test, yielding t-values and p-values. It's important to emphasize that all t-values are undeniably significant, and the p-values provide irrefutable evidence against the null hypothesis. Furthermore, it's noteworthy that the Final dataset outperformed all other datasets across all metric values when diagnosing various lung infections with the same factors.

Esophageal lung complicated by recurrent pneumonia

A 6-month-old infant with recurrent respiratory infections, rapid breathing, and reduced air entry on the right side was diagnosed with congenital bronchopulmonary foregut malformation (CBPFM). Imaging revealed a collapsed and underdeveloped right lung, with the right bronchus appearing to originate from the lower part of the esophagus. An esophagogram confirmed the diagnosis by showing contrast flowing freely from the lower esophagus to the right bronchus.

Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems

OBJECTIVE

We aimed to evaluate the effectiveness of utilizing artificial intelligence (AI) to quantify the extent of pneumonia from chest CT scans, and to determine its ability to predict clinical deterioration or mortality in patients admitted to the hospital with COVID-19 in comparison to semi-quantitative visual scoring systems.

METHODS

A deep-learning algorithm was utilized to quantify the pneumonia burden, while semi-quantitative pneumonia severity scores were estimated through visual means. The primary outcome was clinical deterioration, the composite end point including admission to the intensive care unit, need for invasive mechanical ventilation, or vasopressor therapy, as well as in-hospital death.

RESULTS

The final population comprised 743 patients (mean age 65  ±  17 years, 55% men), of whom 175 (23.5%) experienced clinical deterioration or death. The area under the receiver operating characteristic curve (AUC) for predicting the primary outcome was significantly higher for AI-assisted quantitative pneumonia burden (0.739, p = 0.021) compared with the visual lobar severity score (0.711, p < 0.001) and visual segmental severity score (0.722, p = 0.042). AI-assisted pneumonia assessment exhibited lower performance when applied for calculation of the lobar severity score (AUC of 0.723, p = 0.021). Time taken for AI-assisted quantification of pneumonia burden was lower (38 ± 10 s) compared to that of visual lobar (328 ± 54 s, p < 0.001) and segmental (698 ± 147 s, p < 0.001) severity scores.

CONCLUSION

Utilizing AI-assisted quantification of pneumonia burden from chest CT scans offers a more accurate prediction of clinical deterioration in patients with COVID-19 compared to semi-quantitative severity scores, while requiring only a fraction of the analysis time.

ADVANCES IN KNOWLEDGE

Quantitative pneumonia burden assessed using AI demonstrated higher performance for predicting clinical deterioration compared to current semi-quantitative scoring systems. Such an AI system has the potential to be applied for image-based triage of COVID-19 patients in clinical practice.

Ultrasound-assessed lung aeration, oxygenation and respiratory care in neonatal bile acid pneumonia: A nested case-control study

AIM

Neonatal bile acid pneumonia (NBAP) occurs in neonates following obstetric cholestasis. We aimed to study the lung aeration and respiratory support of NBAP.

METHODS

Nested, case/control study enrolling age-matched neonates with NBAP, respiratory distress syndrome (RDS) or transient tachypnoea (TTN). Lung aeration and oxygenation were assessed with lung ultrasound score, oxygenation index and SpO2 /FiO2 .

RESULTS

Nineteen, 22 and 25 neonates with NBAP, RDS and TTN, respectively were studied (mean gestational age = 33 (2.2) weeks, 30 (45.5%) males). Upon admission, RDS patients had the worst lung ultrasound score (p = 0.022) and oxygenation index (p = 0.001), while NBAP and TTN neonates had similar values. At the worst time-point, NBAP and RDS patients showed similar oxygenation index (NBAP: 4.6 [2], RDS: 5.7 [3]) and SpO2 /FiO2 (NBAP: 3.1 [1.1], RDS: 2.7 [1]) which were worse than those of TTN patients (oxygenation index: p = 0.015, SpO2 /FiO2 : p = 0.001). RDS neonates needed the longest continuous positive airway pressure and highest mean airway pressure, but NBAP neonates needed invasive ventilation (26.3%, p = 0.01) and surfactant (31.6%, p = 0.003) more often than TTN patients who never needed these.

CONCLUSION

NBAP was a mild disorder in the first hours of life but subsequently worsened and became similar to RDS.

Organizational Benefits of Ultra-Low-Dose Chest CT Compared to Chest Radiography in the Emergency Department for the Diagnostic Workup of Community-Acquired Pneumonia: A Real-Life Retrospective Analysis

Background and Objectives: Chest radiography remains the most frequently used examination in emergency departments (ED) for the diagnosis of community-acquired pneumonia (CAP), despite its poor diagnostic accuracy compared with ultra-low-dose (ULD) chest computed tomography (CT). However, although ULD CT appears to be an attractive alternative to radiography, its organizational impact in ED remains unknown. Our objective was to compare the relevant timepoints in ED management of CT and chest radiography. Materials and Methods: We conducted a retrospective study in two ED of a University Hospital including consecutive patients consulting for a CAP between 1 March 2019 and 29 February 2020 to assess the organizational benefits of ULD chest CT and chest radiography (length of stay (LOS) in the ED, time of clinical decision after imaging). Overlap weights (OW) were used to reduce covariate imbalance between groups. Results: Chest radiography was performed for 1476 patients (mean age: 76 years [63; 86]; 55% men) and ULD chest CT for 133 patients (mean age: 71 [57; 83]; 53% men). In the weighted population with OW, ULD chest CT did not significantly alter the ED LOS compared with chest radiography (11.7 to 12.2; MR 0.96 [0.85; 1.09]), although it did significantly reduce clinical decision time (6.9 and 9.5 h; MR 0.73 [0.59; 0.89]). Conclusion: There is real-life evidence that a strategy with ULD chest CT can be considered to be a relevant approach to replace chest radiography as part of the diagnostic workup for CAP in the ED without increasing ED LOS.

2-deoxy-2-[18F]fluoro-D-glucose Positron Emission Tomography to Monitor Lung Inflammation and Therapeutic Response to Dexamethasone in a Murine Model of Acute Lung Injury

PURPOSE

To image inflammation and monitor therapeutic response to anti-inflammatory intervention using 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) in a preclinical model of acute lung injury (ALI).

PROCEDURES

Mice were intratracheally administered lipopolysaccharide (LPS, 2.5 mg/kg) to induce ALI or phosphate-buffered saline as the vehicle control. A subset of mice in the ALI group received two intraperitoneal doses of dexamethasone 1 and 24 h after LPS. [18F]FDG PET/CT was performed 2 days after the induction of ALI. [18F]FDG uptake in the lungs was quantified by PET (%ID/mLmean and standardized uptake value (SUVmean)) and ex vivo γ-counting (%ID/g). The severity of lung inflammation was determined by quantifying the protein level of inflammatory cytokines/chemokines and the activity of neutrophil elastase and glycolytic enzymes. In separate groups of mice, flow cytometry was performed to estimate the contribution of individual immune cell types to the total pulmonary inflammatory cell burden under different treatment conditions.

RESULTS

Lung uptake of [18F]FDG was significantly increased during LPS-induced ALI, and a decreased [18F]FDG uptake was observed following dexamethasone treatment to an intermediate level between that of LPS-treated and control mice. Protein expression of inflammatory biomarkers and the activity of neutrophil elastase and glycolytic enzymes were increased in the lungs of LPS-treated mice versus those of control mice, and correlated with [18F]FDG uptake. Furthermore, dexamethasone-induced decreases in cytokine/chemokine protein levels and enzyme activities correlated with [18F]FDG uptake. Neutrophils were the most abundant cells in LPS-induced ALI, and the pattern of total cell burden during ALI with or without dexamethasone therapy mirrored that of [18F]FDG uptake.

CONCLUSIONS

[18F]FDG PET noninvasively detects lung inflammation in ALI and its response to anti-inflammatory therapy in a preclinical model. However, high [18F]FDG uptake by bone, brown fat, and myocardium remains a technical limitation for quantification of [18F]FDG in the lungs.

The potential clinical value of pairing procalcitonin and lung ultrasonography to guide antibiotic therapy in patients with community-acquired pneumonia: a narrative review

INTRODUCTION

Lower respiratory tract infections (LRTIs) are among the most frequent infections and are prone to inappropriate antibiotic treatments. This results from a limited accuracy of diagnostic tools in identifying bacterial pneumonia. Lung ultrasound (LUS) has excellent sensitivity and specificity in diagnosing pneumonia. Additionally, elevated procalcitonin (PCT) levels correlate with an increased likelihood of bacterial infection. LUS and PCT appear to be complementary in identifying patients with bacterial pneumonia who are likely to benefit from antibiotics.

AREAS COVERED

This narrative review aims to summarize the current evidence for LUS to diagnose pneumonia, for PCT to guide antibiotic therapy and the clinical value of pairing both tools.

EXPERT OPINION

LUS has excellent diagnostic accuracy for pneumonia in different settings, regardless of the examiner's experience. PCT guidance safely reduces antibiotic prescription in LRTIs. The combination of both tools has demonstrated an enhanced accuracy in the diagnosis of pneumonia, including CAP in the ED and VAP in the ICU, but randomized controlled studies need to validate the clinical impact of a combined approach.

Roadmaps for Guiding Chest Computed Tomography Interpretation involving Pneumonia. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023;2023:1-4. [PMID: 38083702 DOI: 10.1109/embc40787.2023.10340639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

CT scanning of the chest is one the most important imaging modalities available for pulmonary disease diagnosis. Lung segmentation plays a crucial step in the pipeline of computer-aided analysis and diagnosis. As deep learning models have achieved human-level accuracy in semantic segmentation of anatomical structures, we propose to use trained deep learning models to predict both healthy and infectious areas in chest CT slices. The semantic segmentation results are summarized and visualized using volume rendering technology in the form of roadmaps. The roadmaps consist of both location and volume information that can be used as a location guidance for inspecting suspected pulmonary lesions of chest CT and can possibly be combined into a rapid triage algorithm for treating acute pulmonary diseases.Clinical Relevance- This research applied trained semantic segmentation models in identifying normal lung and pneumonic infection areas to generate a roadmap for assisting medical doctors in browsing chest CT and prognostication.

Improve the performance of CT-based pneumonia classification via source data reweighting

Pneumonia is a life-threatening disease. Computer tomography (CT) imaging is broadly used for diagnosing pneumonia. To assist radiologists in accurately and efficiently detecting pneumonia from CT scans, many deep learning methods have been developed. These methods require large amounts of annotated CT scans, which are difficult to obtain due to privacy concerns and high annotation costs. To address this problem, we develop a three-level optimization based method which leverages CT data from a source domain to mitigate the lack of labeled CT scans in a target domain. Our method automatically identifies and downweights low-quality source CT data examples which are noisy or have large domain discrepancy with target data, by minimizing the validation loss of a target model trained on reweighted source data. On a target dataset with 2218 CT scans and a source dataset with 349 CT images, our method achieves an F1 score of 91.8% in detecting pneumonia and an F1 score of 92.4% in detecting other types of pneumonia, which are significantly better than those achieved by state-of-the-art baseline methods.

Association between pre-treatment chest imaging and pulmonary function abnormalities and immune checkpoint inhibitor pneumonitis

BACKGROUND

Immune checkpoint inhibitors (ICIs) are a first-line treatment for various metastatic solid tumors. Pneumonitis is a potentially devastating complication of ICI treatment and a leading cause of ICI-related mortality. Here, we evaluate whether abnormal pre-treatment pulmonary function tests (PFTs) or interstitial abnormalities on computed tomography of the chest (CT chest) prior to ICI are associated with the development of ICI-pneumonitis (ICI-p).

METHODS

We conducted a retrospective cohort study of consecutive patients who received at least one dose of ICI from 2011 to 2017 at The Ohio State University. Potential risk factors for ICI-p, including abnormal PFTs and CT chest, were recorded. These risk factors were compared between patients with and without pneumonitis.

RESULTS

In total, 1097 patients were included, 46 with ICI-p and 1051 without. Ninety percent of patients had pre-treatment chest imaging, while only 10% had pre-treatment PFTs. On multivariable analysis, interstitial abnormalities and reduced total lung capacity (TLC) were significantly associated with development of ICI-p (hazard ratio of 42.42 [95% CI; 15.04-119.67] and hazard ratio of 4.04 [95% CI; 1.32-12.37]), respectively. No other PFT abnormality was associated with increased risk of ICI-p. There was no significant difference in overall survival in patients who did or did not develop ICI-p (p = 0.332).

CONCLUSIONS

Pre-existing interstitial abnormalities on CT chest and reduced TLC were strongly associated with developing ICI-p. Prospective studies are warranted to further explore the role of PFTs as a potential tool for identifying patients at highest risk for developing ICI-p.