Approach to chest computed tomography

Development and Evaluation of an Automated Protocol Recommendation System for Chest CT Using Natural Language Processing With CLEVER Terminology Word Replacement

Purpose: To evaluate the clinical performance of a Protocol Recommendation System (PRS) automatic protocolling of chest CT imaging requests. Materials and Methods: 322 387 consecutive historical imaging requests for chest CT between 2017 and 2022 were extracted from a radiology information system (RIS) database containing 16 associated patient information values. Records with missing fields and protocols with <100 occurrences were removed, leaving 18 protocols for training. After freetext pre-processing and applying CLEVER terminology word replacements, the features of a bag-of-words model were used to train a multinomial logistic regression classifier. Four readers protocolled 300 clinically executed protocols (CEP) based on all clinically available information. After their selection was made, the PRS and CEP were unblinded, and the readers were asked to score their agreement (1 = severe error, 2 = moderate error, 3 = disagreement but acceptable, 4 = agreement). The ground truth was established by the readers' majority selection, a judge helped break ties. For the PRS and CEP, the accuracy and clinical acceptability (scores 3 and 4) were calculated. The readers' protocolling reliability was measured using Fleiss' Kappa. Results: Four readers agreed on 203/300 protocols, 3 on 82/300 cases, and in 15 cases, a judge was needed. PRS errors were found by the 4 readers in 1%, 2.7%, 1%, and 0.7% of the cases, respectively. The accuracy/clinical acceptability of the PRS and CEP were 84.3%/98.6% and 83.0%/99.3%, respectively. The Fleiss' Kappa for all readers and all protocols was 0.805. Conclusion: The PRS achieved similar accuracy to human performance and may help radiologists master the ever-increasing workload.

Minimally Invasive Sampling of Mediastinal Lesions

This narrative review examines the existing literature on minimally invasive image-guided sampling techniques of mediastinal lesions gathered from international databases (Medline, PubMed, Scopus, and Google Scholar). Original studies, systematic reviews with meta-analyses, randomized controlled trials, and case reports published between January 2009 and November 2023 were included. Four authors independently conducted the search to minimize bias, removed duplicates, and selected and evaluated the studies. The review focuses on the recent advancements in mediastinal sampling techniques, including EBUS-TBNA, EUS-FNA and FNB, IFB, and nodal cryobiopsy. The review highlights the advantages of an integrated approach using these techniques for diagnosing and staging mediastinal diseases, which, when used competently, significantly increase diagnostic yield and accuracy.

Deep learning-driven multi-view multi-task image quality assessment method for chest CT image

BACKGROUND

Chest computed tomography (CT) image quality impacts radiologists' diagnoses. Pre-diagnostic image quality assessment is essential but labor-intensive and may have human limitations (fatigue, perceptual biases, and cognitive biases). This study aims to develop and validate a deep learning (DL)-driven multi-view multi-task image quality assessment (M[Formula: see text]IQA) method for assessing the quality of chest CT images in patients, to determine if they are suitable for assessing the patient's physical condition.

METHODS

This retrospective study utilizes and analyzes chest CT images from 327 patients. Among them, 1613 images from 286 patients are used for model training and validation, while the remaining 41 patients are reserved as an additional test set for conducting ablation studies, comparative studies, and observer studies. The M[Formula: see text]IQA method is driven by DL technology and employs a multi-view fusion strategy, which incorporates three scanning planes (coronal, axial, and sagittal). It assesses image quality for multiple tasks, including inspiration evaluation, position evaluation, radiation protection evaluation, and artifact evaluation. Four algorithms (pixel threshold, neural statistics, region measurement, and distance measurement) have been proposed, each tailored for specific evaluation tasks, with the aim of optimizing the evaluation performance of the M[Formula: see text]IQA method.

RESULTS

In the additional test set, the M[Formula: see text]IQA method achieved 87% precision, 93% sensitivity, 69% specificity, and a 0.90 F1-score. Extensive ablation and comparative studies have demonstrated the effectiveness of the proposed algorithms and the generalization performance of the proposed method across various assessment tasks.

CONCLUSION

This study develops and validates a DL-driven M[Formula: see text]IQA method, complemented by four proposed algorithms. It holds great promise in automating the assessment of chest CT image quality. The performance of this method, as well as the effectiveness of the four algorithms, is demonstrated on an additional test set.

Development of a patient-specific chest computed tomography imaging phantom with realistic lung lesions using silicone casting and three-dimensional printing

The validation of the accuracy of the quantification software in computed tomography (CT) images is very challenging. Therefore, we proposed a CT imaging phantom that accurately represents patient-specific anatomical structures and randomly integrates various lesions including disease-like patterns and lesions of various shapes and sizes using silicone casting and three-dimensional (3D) printing. Six nodules of various shapes and sizes were randomly added to the patient's modeled lungs to evaluate the accuracy of the quantification software. By using silicone materials, CT intensities suitable for the lesions and lung parenchyma were realized, and their Hounsfield unit (HU) values were evaluated on a CT scan of the phantom. As a result, based on the CT scan of the imaging phantom model, the measured HU values for the normal lung parenchyma, each nodule, fibrosis, and emphysematous lesions were within the target value. The measurement error between the stereolithography model and 3D-printing phantoms was 0.2 ± 0.18 mm. In conclusion, the use of 3D printing and silicone casting allowed the application and evaluation of the proposed CT imaging phantom for the validation of the accuracy of the quantification software in CT images, which could be applied to CT-based quantification and development of imaging biomarkers.

Clinical Applicability of Lung Ultrasound Methods in the Emergency Department to Detect Pulmonary Congestion on Computed Tomography

BACKGROUND

 B-lines on lung ultrasound are seen in decompensated heart failure, but their diagnostic value in consecutive patients in the acute setting is not clear. Chest CT is the superior method to evaluate interstitial lung disease, but no studies have compared lung ultrasound directly to congestion on chest CT.

PURPOSE

 To examine whether congestion on lung ultrasound equals congestion on a low-dose chest CT as the gold standard.

MATERIALS AND METHODS

 In a single-center, prospective observational study we included consecutive patients ≥ 50 years of age in the emergency department. Patients were concurrently examined by lung ultrasound and chest CT. Congestion on lung ultrasound was examined in three ways: I) the total number of B-lines, II) ≥ 3 B-lines bilaterally, III) ≥ 3 B-lines bilaterally and/or bilateral pleural effusion. Congestion on CT was assessed by two specialists blinded to all other data.

RESULTS

 We included 117 patients, 27 % of whom had a history of heart failure and 52 % chronic obstructive pulmonary disease. Lung ultrasound and CT were performed within a median time of 79.0 minutes. Congestion on CT was detected in 32 patients (27 %). Method I had an optimal cut-point of 7 B-lines with a sensitivity of 72 % and a specificity of 81 % for congestion. Method II had 44 % sensitivity, and 94 % specificity. Method III had a sensitivity of 88 % and a specificity of 85 %.

CONCLUSION

 Pulmonary congestion in consecutive dyspneic patients ≥ 50 years of age is better diagnosed if lung ultrasound evaluates both B-lines and pleural effusion instead of B-lines alone.

Association of pericardial adipose tissue with left ventricular structure and function: a region-specific effect?

BACKGROUND

The independent role of pericardial adipose tissue (PAT) as an ectopic fat associated with cardiovascular disease (CVD) remains controversial. This study aimed to determine whether PAT is associated with left ventricular (LV) structure and function independent of other markers of general obesity.

METHODS

We studied 2471 participants (50.9 % women) without known CVD from the Korean Genome Epidemiology Study, who underwent 2D-echocardiography with tissue Doppler imaging (TDI) and computed tomography measurement for PAT.

RESULTS

Study participants with more PAT were more likely to be men and had higher cardiometabolic indices, including blood pressure, glucose, and cholesterol levels (all P < 0.001). Greater pericardial fat levels across quartiles of PAT were associated with increased LV mass index and left atrial volume index (all P < 0.001) and decreased systolic (P = 0.015) and early diastolic (P < 0.001) TDI velocities, except for LV ejection fraction. These associations remained after a multivariable-adjusted model for traditional CV risk factors and persisted even after additional adjustment for general adiposity measures, such as waist circumference and body mass index. PAT was also the only obesity index independently associated with systolic TDI velocity (P < 0.001).

CONCLUSIONS

PAT was associated with subclinical LV structural and functional deterioration, and these associations were independent of and stronger than with general and abdominal obesity measures.

Beyond bronchitis: a review of the congenital and acquired abnormalities of the bronchus

Anomalies of the bronchus can be both congenital and acquired. Several different congenital aberrations of the bronchial anatomy are commonly encountered including tracheal bronchus, accessory cardiac bronchus, and bronchial agenesis/aplasia/hypoplasia. In addition, Williams-Campbell syndrome and cystic fibrosis are two other congenital conditions that result in bronchial pathology. Acquired pathology affecting the bronchi can typically be divided into three broad categories of bronchial disease: bronchial wall thickening, dilatation/bronchiectasis, and obstruction/stenosis. Bronchial wall thickening is the common final response of the airways to irritants, which cause the bronchi to become swollen and inflamed. Bronchiectasis/bronchial dilatation can develop in response to many aetiologies, including acquired conditions such as infection, pulmonary fibrosis, recurrent or chronic aspiration, as well as because of congenital conditions such as cystic fibrosis. The causes of obstruction and stenosis are varied and include foreign body aspiration, acute aspiration, tracheobronchomalacia, excessive dynamic airway collapse, neoplasm, granulomatous disease, broncholithiasis, and asthma. Knowledge of normal bronchial anatomy and its congenital variants is essential for any practicing radiologist. It is the role of the radiologist to identify common imaging patterns associated with the various categories of bronchial disease and provide the ordering clinician a useful differential diagnosis tailored to the patient’s clinical history and imaging findings.

Teaching Points

• Bronchial disorders are both congenital and acquired in aetiology.

• Bronchial disease can be divided by imaging appearance: wall thickening, dilatation, or obstruction.

• Bronchial wall thickening is the common final response of the airways to irritants.

• Imaging patterns must be recognised and the differential diagnosis tailored for patient management.

Imaging of Diseases of the Large Airways

Imaging of the large airways is key to the diagnosis and management of a wide variety of congenital, infectious, malignant, and inflammatory diseases. Involvement can be focal, regional, or diffuse, and abnormalities can take the form of masses, thickening, narrowing, enlargement, or a combination of patterns. Recognition of the typical morphologies, locations, and distributions of large airways disease is central to an accurate imaging differential diagnosis.