Ability of high-resolution computed tomography to distinguish Mycoplasma pneumoniae pneumonia from other bacterial pneumonia: Significance of lateral bronchial lesions, less air bronchogram, and no peripheral predominance

Lobar pneumonia due to human metapneumovirus: a case report

Human metapneumovirus (hMPV) is a respiratory pathogen that can cause lower respiratory tract infections and pneumonia in immunocompetent adults. Pneumonia caused by hMPV is reportedly more likely to cause bronchial wall thickening and ground-glass opacity (GGO). A 44-year-old woman with no significant medical history developed fever, cough, and nausea. Computed tomography of the chest showed scattered GGOs in the right upper lobe and infiltrating shadows with air bronchograms in the left lingual and bilateral lower lobes. The patient was admitted to our hospital for further evaluation. Atypical pneumonia was suspected and lascufloxacin (LSFX) was started. Multiplex polymerase chain reaction (PCR) detected hMPV on hospital day 2 using the FilmArray Respiratory Panel 2.1. Pneumonia due to hMPV was suspected and LSFX was discontinued. The patient subsequently showed spontaneous improvement and was discharged on hospital day 6 after admission. After discharge, pneumonia continued to improve. Early detection of respiratory pathogens using multiplex PCR can help determine the appropriate treatment strategy. As hMPV can also cause lobar pneumonia, we should consider pneumonia due to hMPV in the differential diagnosis of lobar pneumonia.

Diagnostic significance of HRCT imaging features in adult mycoplasma pneumonia: a retrospective study

Mycoplasma pneumoniae pneumonia (MPP) often overlaps with the clinical manifestations and chest imaging manifestations of other types of community-acquired pneumonia (CAP). We retrospectively analyzed the clinical and imaging data of a group of patients with CAP, summarized their clinical and imaging characteristics, and discussed the diagnostic significance of their certain HRCT findings. The HRCT findings of CAP researched in our study included tree-in-bud sign (TIB), ground-glass opacity (GGO), tree fog sign (TIB + GGO), bronchial wall thickening, air-bronchogram, pleural effusion and cavity. The HRCT findings of all cases were analyzed. Among the 200 cases of MPP, 174 cases showed the TIB, 193 showed the GGO, 175 showed the tree fog sign, 181 lacked air-bronchogram. In case taking the tree fog sign and lack of air-bronchogram simultaneously as an index to distinguish MPP from OCAP, the sensitivity was 87.5%, the specificity was 97.5%, the accuracy was 92.5%. This study showed that that specific HRCT findings could be used to distinguish MPP from OCAP. The combined HRCT findings including the tree fog sign and lacked air-bronchogram simultaneously would contribute to a more accurate diagnosis of MPP.

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.

Atypical Pathogens in Adult Community-Acquired Pneumonia and Implications for Empiric Antibiotic Treatment: A Narrative Review

Atypical pathogens are intracellular bacteria causing community-acquired pneumonia (CAP) in a significant minority of patients. Legionella spp., Chlamydia pneumoniae and psittaci, Mycoplasma pneumoniae, and Coxiella burnetii are commonly included in this category. M. pneumoniae is present in 5-8% of CAP, being the second most frequent pathogen after Streptococcus pneumoniae. Legionella pneumophila is found in 3-5% of inpatients. Chlamydia spp. and Coxiella burnetii are present in less than 1% of patients. Legionella longbeachae is relatively frequent in New Zealand and Australia and might also be present in other parts of the world. Uncertainty remains on the prevalence of atypical pathogens, due to limitations in diagnostic means and methodological issues in epidemiological studies. Despite differences between CAP caused by typical and atypical pathogens, the clinical presentation alone does not allow accurate discrimination. Hence, antibiotics active against atypical pathogens (macrolides, tetracyclines and fluoroquinolones) should be included in the empiric antibiotic treatment of all patients with severe CAP. For patients with milder disease, evidence is lacking and recommendations differ between guidelines. Use of clinical prediction rules to identify patients most likely to be infected with atypical pathogens, and strategies of narrowing the antibiotic spectrum according to initial microbiologic investigations, should be the focus of future investigations.

Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children

Objective

Use of optimal Atlas segmentation algorithm to study the imaging signs of mycoplasma pneumonia with multi-slice spiral CT (HRCT), and to explore the value of HRCT in the diagnosis and efficacy in evaluation of mycoplasma pneumonia in children.

Methods

The study retrospectively analyzed 72 patients diagnosed with mycoplasma pneumonia in our hospital from January 2017 to January 2019. The imaging data and clinical data of 72 patients were collected. The optimal Atlas segmentation algorithm was used to analyze the characteristics of CT examination, and the value of CT in the diagnosis of mycoplasma pneumonia and the evaluation of curative effect was summarized.

Results

Among all patients, 37 cases were unilateral lesions, 35 cases were bilateral lesions, 19 cases were in the left upper lobe, 24 cases were in the left lower lobe, 21 cases were in the right upper lobe, 13 cases were in the right middle lobe, 25 The lesion was located in the right lower lobe. The main CT findings of the lesions before treatment were large patchy, spot-shaped shadows, and strip-shaped or ground-glass shadows. After treatment, the main CT findings of the lesions were reduced lesion density and reduced lesion range.

Conclusion

CT can clearly show the pulmonary lesions of mycoplasma pneumonia, and its unique imaging signs can improve the clinical diagnosis accuracy. In addition, CT scans can evaluate the treatment effect according to the changes in the characteristics of the lesion, which has important value for the evaluation of the effect for clinical diagnosis and efficacy evaluation of mycoplasma pneumonia.

An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images

Purpose

To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images.

Materials and methods

Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model.

Results

We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65–0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61–0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set.

Conclusion

This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.

CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method

BACKGROUND

Although typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.

METHODS

Thirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).

RESULTS

Older age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score >  2.

CONCLUSIONS

With a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.