Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.

OBJECTIVES: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs. METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review. RESULTS: In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35). CONCLUSION: The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low. CLINICAL RELEVANCE STATEMENT: The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography. KEY POINTS: • A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

X-ray dark-field chest radiography: a reader study to evaluate the diagnostic quality of attenuation chest X-rays from a dual-contrast scanning prototype.

OBJECTIVES: To compare the visibility of anatomical structures and overall quality of the attenuation images obtained with a dark-field X-ray radiography prototype with those from a commercial radiography system. METHODS: Each of the 65 patients recruited for this study obtained a thorax radiograph at the prototype and a reference radiograph at the commercial system. Five radiologists independently assessed the visibility of anatomical structures, the level of motion artifacts, and the overall image quality of all attenuation images on a five-point scale, with 5 points being the highest rating. The average scores were compared between the two image types. The differences were evaluated using an area under the curve (AUC) based z-test with a significance level of p ≤ 0.05. To assess the variability among the images, the distributions of the average scores per image were compared between the systems. RESULTS: The overall image quality was rated high for both devices, 4.2 for the prototype and 4.6 for the commercial system. The rating scores varied only slightly between both image types, especially for structures relevant to lung assessment, where the images from the commercial system were graded slightly higher. The differences were statistically significant for all criteria except for the bronchial structures, the cardiophrenic recess, and the carina. CONCLUSIONS: The attenuation images acquired with the prototype were assigned a high diagnostic quality despite a lower resolution and the presence of motion artifacts. Thus, the attenuation-based radiographs from the prototype can be used for diagnosis, eliminating the need for an additional conventional radiograph. KEY POINTS: • Despite a low tube voltage (70 kVp) and comparably long acquisition time, the attenuation images from the dark-field chest radiography system achieved diagnostic quality for lung assessment. • Commercial chest radiographs obtained a mean rating score regarding their diagnostic quality of 4.6 out of 5, and the grating-based images had a slightly lower mean rating score of 4.2 out of 5. • The difference in rating scores for anatomical structures relevant to lung assessment is below 5%.

Multicentre external validation of a commercial artificial intelligence software to analyse chest radiographs in health screening environments with low disease prevalence.

OBJECTIVES: To externally validate the performance of a commercial AI software program for interpreting CXRs in a large, consecutive, real-world cohort from primary healthcare centres. METHODS: A total of 3047 CXRs were collected from two primary healthcare centres, characterised by low disease prevalence, between January and December 2018. All CXRs were labelled as normal or abnormal according to CT findings. Four radiology residents read all CXRs twice with and without AI assistance. The performances of the AI and readers with and without AI assistance were measured in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: The prevalence of clinically significant lesions was 2.2% (68 of 3047). The AUROC, sensitivity, and specificity of the AI were 0.648 (95% confidence interval [CI] 0.630-0.665), 35.3% (CI, 24.7-47.8), and 94.2% (CI, 93.3-95.0), respectively. AI detected 12 of 41 pneumonia, 3 of 5 tuberculosis, and 9 of 22 tumours. AI-undetected lesions tended to be smaller than true-positive lesions. The readers' AUROCs ranged from 0.534-0.676 without AI and 0.571-0.688 with AI (all p values < 0.05). For all readers, the mean reading time was 2.96-10.27 s longer with AI assistance (all p values < 0.05). CONCLUSIONS: The performance of commercial AI in these high-volume, low-prevalence settings was poorer than expected, although it modestly boosted the performance of less-experienced readers. The technical prowess of AI demonstrated in experimental settings and approved by regulatory bodies may not directly translate to real-world practice, especially where the demand for AI assistance is highest. KEY POINTS: • This study shows the limited applicability of commercial AI software for detecting abnormalities in CXRs in a health screening population. • When using AI software in a specific clinical setting that differs from the training setting, it is necessary to adjust the threshold or perform additional training with such data that reflects this environment well. • Prospective test accuracy studies, randomised controlled trials, or cohort studies are needed to examine AI software to be implemented in real clinical practice.

The heart to single vertebra ratio: A new objective method for radiographic assessment of cardiac silhouette size in dogs.

Vertebral heart size (VHS) is widely determined in clinical practice as an objective method to assess the cardiac silhouette dimensions. However, a key limitation is that it is difficult to determine VHS in dogs with vertebral alterations. This retrospective, method comparison, observer agreement study sought to overcome this limitation by using the heart-to-single vertebra ratio (HSVR), by evaluating the level of agreement between VHS and HSVR, as well as the intra- and inter-observer agreement for HSVR. Three independent observers retrospectively evaluated thoracic radiographs obtained over a set time period. Exclusion criteria were the presence of alterations of the thoracic spine and the inability to clearly outline the cardiac silhouette. The lengths of the vertebral bodies, from the fourth to eighth thoracic vertebra, and VHS were measured on each radiograph. The HSVR was calculated by dividing the sum of the cardiac long and short axes by the length of each vertebral body. Eighty dogs of different breeds were included in the final analysis. Lin's concordance correlation coefficients revealed strong correlations between VHS and HSVR (0.91-0.96), and the Bland-Altman plots showed low bias (0.01-0.2) between the methods. The mean absolute errors indicated low average magnitudes of error (0.11-0.28). The intraclass correlation coefficients showed good to excellent inter-observer (0.87-0.92; P = 0.000) and intra-observer (0.87-0.99; P < .001) agreement. In the authors' opinion, this new method, which is less time consuming and more objective, could offer a valuable alternative to VHS.

Lung Ultrasound Performed by Primary Care Physicians for Clinically Suspected Community-Acquired Pneumonia: A Multicenter Prospective Study.

PURPOSE: We investigated whether lung ultrasound (US) performed in primary care is useful and feasible for diagnosing community-acquired pneumonia (CAP) compared with chest radiography, as most previous research has been conducted in hospital settings. METHODS: We undertook a prospective observational cohort study of lung US performed in 12 primary care centers. Patients aged 5 years and older with symptoms suggesting CAP were examined with lung US (by 21 family physicians and 7 primary care pediatricians) and chest radiograph on the same day. We compared lung US findings with the radiologist's chest radiograph report as the reference standard, given that the latter is the most common imaging test performed for suspected CAP in primary care. The physicians had varied previous US experience, but all received a 5-hour lung US training program. RESULTS: The study included 82 patients. Compared with chest radiography, positive lung US findings (consolidation measuring >1 cm or a focal/asymmetrical B-lines pattern) showed a sensitivity of 87.8%, a specificity of 58.5%, a positive likelihood-ratio of 2.12, and a negative likelihood-ratio of 0.21. Findings were similar regardless of the physicians' previous US training or experience. We propose a practical algorithm whereby patients having consolidation measuring greater than 1 cm or normal findings on lung US could skip chest radiography, whereas patients with a B-lines pattern without consolidation (given its low specificity) would need chest radiography to ensure appropriate management. Lung US was generally performed in 10 minutes or less. CONCLUSION: Point-of-care lung US in primary care could be useful for investigating suspected CAP (avoiding chest radiography in most cases) and is likely feasible in daily practice, as short training programs appear sufficient and little time is needed to perform the scan.

[Pulmonary nodules and pneumonia : A diagnostic guideline]. / Pulmonale Rundherde und Pneumonie : Ein diagnostischer Leitfaden.

BACKGROUND: Chest X­ray is one of the most frequent examinations in radiology and its interpretation is considered part of the basic knowledge of every radiologist. OBJECTIVES: The purpose of this article is to recognize common signs and patterns of pneumonias and pseudonodules in chest X­rays and to provide a diagnostic guideline for young radiologists. MATERIALS AND METHODS: Recent studies and data are analyzed and an overview of the most common signs and patterns in chest X­ray is provided. RESULTS: Knowledge about common signs and patterns in chest X­ray is helpful in the diagnosis of pneumonias and can be indicative for the cause of an infection. However, those signs are often unspecific and should, therefore, be set in clinical content. Computed tomography is becoming increasingly important in the primary diagnosis of pulmonary lesions because of its much higher sensitivity. CONCLUSION: Chest X­ray is still the first-line modality in the diagnosis of pneumonia and pulmonary nodules; however, radiologists should be aware of its limitations.

[Mediastinal lesions : The most common pathologies in chest X-rays and their correlations in computed tomography]. / Mediastinale Läsionen : Die häufigsten Pathologien in der Röntgenaufnahme des Thorax und deren Korrelation in der Computertomographie.

BACKGROUND: Many pathologies of the mediastinum can be diagnosed using standard radiographs. Correlation of radiographic findings with computed tomography (CT) is instructive for a better understanding and can help improve detection rates of mediastinal lesions. OBJECTIVES: To identify the most common mediastinal lesions and to correlate their features in chest radiographs and CT. METHODS: The International Thymic Malignancy Interest Group (ITMIG) classification in the anterior, middle, and posterior mediastinum is based on anatomic landmarks. Used as a tool to characterize mediastinal lesions this classification is applied in this article. RESULTS: The most common lesions include mediastinal goiter, germ cell and thymic neoplasms in the anterior mediastinum, lymphadenopathy in the middle mediastinum, and neurogenic neoplasms in the posterior mediastinum. Other lesions of neoplastic or non-neoplastic origin can be distinguished in the three compartments and should be considered in the differential diagnosis. CONCLUSIONS: Knowledge of the most common pathologies in the three mediastinal compartments can accelerate differential diagnosis. Understanding the normal mediastinal lines is key in anatomic localization and detection of many lesions in chest radiographs.

Automated identification of chest radiographs with referable abnormality with deep learning: need for recalibration.

OBJECTIVES: To evaluate the calibration of a deep learning (DL) model in a diagnostic cohort and to improve model's calibration through recalibration procedures. METHODS: Chest radiographs (CRs) from 1135 consecutive patients (M:F = 582:553; mean age, 52.6 years) who visited our emergency department were included. A commercialized DL model was utilized to identify abnormal CRs, with a continuous probability score for each CR. After evaluation of the model calibration, eight different methods were used to recalibrate the original model based on the probability score. The original model outputs were recalibrated using 681 randomly sampled CRs and validated using the remaining 454 CRs. The Brier score for overall performance, average and maximum calibration error, absolute Spiegelhalter's Z for calibration, and area under the receiver operating characteristic curve (AUROC) for discrimination were evaluated in 1000-times repeated, randomly split datasets. RESULTS: The original model tended to overestimate the likelihood for the presence of abnormalities, exhibiting average and maximum calibration error of 0.069 and 0.179, respectively; an absolute Spiegelhalter's Z value of 2.349; and an AUROC of 0.949. After recalibration, significant improvements in the average (range, 0.015-0.036) and maximum (range, 0.057-0.172) calibration errors were observed in eight and five methods, respectively. Significant improvement in absolute Spiegelhalter's Z (range, 0.809-4.439) was observed in only one method (the recalibration constant). Discriminations were preserved in six methods (AUROC, 0.909-0.949). CONCLUSION: The calibration of DL algorithm can be augmented through simple recalibration procedures. Improved calibration may enhance the interpretability and credibility of the model for users. KEY POINTS: • A deep learning model tended to overestimate the likelihood of the presence of abnormalities in chest radiographs. • Simple recalibration of the deep learning model using output scores could improve the calibration of model while maintaining discrimination. • Improved calibration of a deep learning model may enhance the interpretability and the credibility of the model for users.

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial.

OBJECTIVE: To enhance the positive predictive value (PPV) of chest digital tomosynthesis (DTS) in the lung cancer detection with the analysis of radiomics features. METHOD: The investigation was carried out within the SOS clinical trial (NCT03645018) for lung cancer screening with DTS. Lung nodules were identified by visual analysis and then classified using the diameter and the radiological aspect of the nodule following lung-RADS. Haralick texture features were extracted from the segmented nodules. Both semantic variables and radiomics features were used to build a predictive model using logistic regression on a subset of variables selected with backward feature selection and using two machine learning: a Random Forest and a neural network with the whole subset of variables. The methods were applied to a train set and validated on a test set where diagnostic accuracy metrics were calculated. RESULTS: Binary visual analysis had a good sensitivity (0.95) but a low PPV (0.14). Lung-RADS classification increased the PPV (0.19) but with an unacceptable low sensitivity (0.65). Logistic regression showed a mildly increased PPV (0.29) but a lower sensitivity (0.20). Random Forest demonstrated a moderate PPV (0.40) but with a low sensitivity (0.30). Neural network demonstrated to be the best predictor with a high PPV (0.95) and a high sensitivity (0.90). CONCLUSIONS: The neural network demonstrated the best PPV. The use of visual analysis along with neural network could help radiologists to reduce the number of false positive in DTS. KEY POINTS: • We investigated several approaches to enhance the positive predictive value of chest digital tomosynthesis in the lung cancer detection. • Neural network demonstrated to be the best predictor with a nearly perfect PPV. • Neural network could help radiologists to reduce the number of false positive in DTS.

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning.

OBJECTIVES: To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. METHODS: We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. RESULTS: In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667-0.820) (p < 0.05). In RetinaNet, matrix size 896 was significantly higher FOM (0.906) than others (0.329-0.832) (p < 0.05). For sensitivity of abnormalities, there was a tendency to increase sensitivity when lesion size increases. For small nodules (< 10 mm), the sensitivities were 0.418 and 0.409, whereas the sensitivities were 0.937 and 0.956 for masses. Matrix size 896 and 1344 in Mask R-CNN and matrix size 896 in RetinaNet showed significantly higher sensitivity than others (p < 0.05). CONCLUSIONS: Matrix size 896 had the highest performance for various sizes of abnormalities using different CNNs. The optimal matrix size of chest radiograph could improve CAD performance without additional training data. KEY POINTS: • Input matrix size significantly affected the performance of a deep learning-based CAD for detection of nodules or masses on chest radiographs. • The matrix size 896 showed the best performance in two different CNN detection models. • The optimal matrix size of chest radiographs could enhance CAD performance without additional training data.

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs (CRs), in consecutive cohorts reflecting actual clinical situation. METHODS: We retrospectively included post-biopsy CRs of 1757 consecutive patients (1055 men, 702 women; mean age of 65.1 years) undergoing percutaneous lung biopsies from three institutions. A commercially available deep learning algorithm analyzed each CR to identify pneumothorax. We compared the performance of the algorithm with that of radiology reports made in the actual clinical practice. We also conducted a reader study, in which the performance of the algorithm was compared with those of four radiologists. Performances of the algorithm and radiologists were evaluated by area under receiver operating characteristic curves (AUROCs), sensitivity, and specificity, with reference standards defined by thoracic radiologists. RESULTS: Pneumothorax occurred in 17.5% (308/1757) of cases, out of which 16.6% (51/308) required catheter drainage. The AUROC, sensitivity, and specificity of the algorithm were 0.937, 70.5%, and 97.7%, respectively, for identification of pneumothorax. The algorithm exhibited higher sensitivity (70.2% vs. 55.5%, p < 0.001) and lower specificity (97.7% vs. 99.8%, p < 0.001), compared with those of radiology reports. In the reader study, the algorithm exhibited lower sensitivity (77.3% vs. 81.8-97.7%) and higher specificity (97.6% vs. 81.7-96.0%) than the radiologists. CONCLUSION: The deep learning algorithm appropriately identified pneumothorax in post-biopsy CRs in consecutive diagnostic cohorts. It may assist in accurate and timely diagnosis of post-biopsy pneumothorax in clinical practice. KEY POINTS: • A deep learning algorithm can identify chest radiographs with post-biopsy pneumothorax in multicenter consecutive cohorts reflecting actual clinical situation. • The deep learning algorithm has a potential role as a surveillance tool for accurate and timely diagnosis of post-biopsy pneumothorax.

Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings.

OBJECTIVE: To investigate the feasibility of a deep learning-based detection (DLD) system for multiclass lesions on chest radiograph, in comparison with observers. METHODS: A total of 15,809 chest radiographs were collected from two tertiary hospitals (7204 normal and 8605 abnormal with nodule/mass, interstitial opacity, pleural effusion, or pneumothorax). Except for the test set (100 normal and 100 abnormal (nodule/mass, 70; interstitial opacity, 10; pleural effusion, 10; pneumothorax, 10)), radiographs were used to develop a DLD system for detecting multiclass lesions. The diagnostic performance of the developed model and that of nine observers with varying experiences were evaluated and compared using area under the receiver operating characteristic curve (AUROC), on a per-image basis, and jackknife alternative free-response receiver operating characteristic figure of merit (FOM) on a per-lesion basis. The false-positive fraction was also calculated. RESULTS: Compared with the group-averaged observations, the DLD system demonstrated significantly higher performances on image-wise normal/abnormal classification and lesion-wise detection with pattern classification (AUROC, 0.985 vs. 0.958; p = 0.001; FOM, 0.962 vs. 0.886; p < 0.001). In lesion-wise detection, the DLD system outperformed all nine observers. In the subgroup analysis, the DLD system exhibited consistently better performance for both nodule/mass (FOM, 0.913 vs. 0.847; p < 0.001) and the other three abnormal classes (FOM, 0.995 vs. 0.843; p < 0.001). The false-positive fraction of all abnormalities was 0.11 for the DLD system and 0.19 for the observers. CONCLUSIONS: The DLD system showed the potential for detection of lesions and pattern classification on chest radiographs, performing normal/abnormal classifications and achieving high diagnostic performance. KEY POINTS: • The DLD system was feasible for detection with pattern classification of multiclass lesions on chest radiograph. • The DLD system had high performance of image-wise classification as normal or abnormal chest radiographs (AUROC, 0.985) and showed especially high specificity (99.0%). • In lesion-wise detection of multiclass lesions, the DLD system outperformed all 9 observers (FOM, 0.962 vs. 0.886; p < 0.001).

Effect of sugammadex on chest radiographic abnormality in the early postoperative period after video-assisted thoracoscopic lobectomy

Background/aim: Sugammadex, which offsets the effects of neuromuscular blocking agents (NMBs), has advantages over traditional reversal agents like pyridostigmine, as it enables fast and reliable recovery from neuromuscular blockade. This study compared the incidence of early postoperative chest radiographic abnormalities (CRA) between sugammadex (group S) and pyridostigmine (group P) following video-assisted thoracoscopic (VAT) lobectomy for lung cancer. Materials and methods: We performed a retrospective cohort analysis by reviewing the medical records of patients who underwent VAT lobectomy at a single university medical center. We defined the early postoperative CRA as a characteristic appearance on chest radiograph up to 2 days after surgery. Arterial blood gas analysis (ABGA), surgical time, anaesthesia time, extubation time, and the total dose of rocuronium were analysed. Postoperative nausea and vomiting (PONV) and pain scores were observed until 2 days after surgery. Results: A total of 257 patients underwent VAT lobectomy during the study period; 159 were included in the final analysis. Ninety patients received sugammadex while 69 received pyridostigmine. The incidence of early postoperative atelectasis was significantly lower in group S than in group P (26.7%, 95% CI: 17.5%‒35.8% and 43.5%, 95% CI: 31.8%‒55.2%, respectively, P = 0.013). The median dose of rocuronium was higher in group S than in group P (120 mg vs. 90 mg, P < 0.001). ABGA, extubation time, and PONV were similar in both groups. Conclusion: Sugammadex decreased the incidence of CRA in the early postoperative period despite higher NMB consumption.

Dose estimation of ultra-low-dose chest CT to different sized adult patients.

OBJECTIVES: To evaluate the effect of patient size on radiation dose for standard CT (SD-CT), ultra-low-dose CT (ULD-CT) and two-view digital radiography (DR). METHODS: Dosimeters were distributed within the lungs of chest phantoms representing males of 65 kg and 82 kg (body mass indices 23 and 29). In contrast to SD-CT and DR which include automatic exposure control (AEC), the ULD scan employs a fixed mAs value. The phantoms were exposed to SD, ULD and DR while recording lung doses. Projected dose data were calculated from the phantoms. The resulting exposure settings were used in Monte Carlo programs to determine the effective dose for a standard-sized (BMI 24.2) adult male (170 cm/70 kg) and female (160 cm/59 kg). Patients previously examined by both ULD- and SD-CT were identified to determine post hoc size-specific dose estimates (SSDEs). RESULTS: ULD-CT dose was inversely related to patient size; average lung doses summarised in terms of patient size BMI23/29 are 5.2/8.1 (SD-CT), 0.56/0.35 (ULD-CT) and 0.05/0.13 mGy (DR), while the effective doses for these techniques on a standard-sized male were 2.9, 0.16 and 0.03 mSv and 2.3, 0.247 and 0.024 mSv for a standard-sized female respectively. SSDEs for 15 patients (averages: BMI 26, range 18-37) averaged 5.5 mGy (3.6-10) for SD-CT and 0.35 mGy (0.42-0.27) for ULD-CT. CONCLUSIONS: The effective doses for a standard-sized male and female examined by ULD-CT are (respectively) ~ 6%/~ 11% of SD-CT and ~ 5/~ 10 times higher than DR. ULD-CT gave a lower radiation dosage to larger patients than DR. AEC is warranted in ULD-CT for improved dose consistency. KEY POINTS: • For standard-sized patients, ULD-CT dose level is ~ 6%/~ 11% of SD-CT, and ~ 5/~ 10 times higher than DR. For larger patients, ULD-CT is currently being used clinically at lower dose levels than DR. • Using ULD-CT should greatly reduce the risk of late effects from ionising radiation. • AEC in ULD-CT is desirable for increased consistency in patient dose.

Cardiac dysfunction in exacerbations of chronic obstructive pulmonary disease is often not detected by electrocardiogram and chest radiographs.

BACKGROUND: Cardiac dysfunction is common in exacerbations of chronic obstructive pulmonary disease (COPD), even in patients without clinically suspected cardiac disorders. AIM: To investigate associations between electrocardiogram (ECG) and chest radiograph abnormalities and biochemical evidence of cardiac dysfunction (N-terminal pro-B-type natriuretic peptide and troponin T) in patients hospitalised with exacerbations of COPD at Waikato Hospital. METHODS: Independent examiners, blinded to NT-proBNP and troponin T levels, assessed ECG for tachycardia, atrial fibrillation, ventricular hypertrophy and ischaemic changes in 389 patients and chest radiographs for signs of heart failure in 350 patients. Associations between electrocardiographic and radiographic abnormalities with at least moderate interrater agreement and cardiac biomarkers were analysed. RESULTS: High NT-proBNP values (>220 pmol/L) were associated with atrial fibrillation (22 vs 6%), right ventricular hypertrophy (24 vs 15%), left ventricular hypertrophy (15 vs 4%), ischaemia (59 vs 33%) and cardiomegaly (42 vs 20%). High troponin T values (>0.03ug/L or high-sensitivity >50 ng/L) were associated with tachycardia (65 vs 41%), right ventricular hypertrophy (26 vs 15%) and ischaemia (60 vs 36%). None of the electrocardiographic or radiographic abnormalities was sensitive or specific for cardiac biomarker abnormalities. Ischaemia on ECG was the best indicator for raised NT-proBNP (sensitivity 59%, specificity 67%). Tachycardia and ischaemia were the best indicators of raised troponin T (sensitivity 65 and 60%, specificity 59 and 64% respectively). CONCLUSIONS: ECG and chest radiograph abnormalities have poor sensitivity and specificity for diagnosing acute cardiac dysfunction in exacerbations of COPD. Cardiac biomarkers provide additional diagnostic information about acute cardiac dysfunction in exacerbations of COPD.

Visual grading analysis of digital neonatal chest phantom X-ray images: Impact of detector type, dose and image processing on image quality.

OBJECTIVES: To evaluate the impact of digital detector, dose level and post-processing on neonatal chest phantom X-ray image quality (IQ). METHODS: A neonatal phantom was imaged using four different detectors: a CR powder phosphor (PIP), a CR needle phosphor (NIP) and two wireless CsI DR detectors (DXD and DRX). Five different dose levels were studied for each detector and two post-processing algorithms evaluated for each vendor. Three paediatric radiologists scored the images using European quality criteria plus additional questions on vascular lines, noise and disease simulation. Visual grading characteristics and ordinal regression statistics were used to evaluate the effect of detector type, post-processing and dose on VGA score (VGAS). RESULTS: No significant differences were found between the NIP, DXD and CRX detectors (p>0.05) whereas the PIP detector had significantly lower VGAS (p< 0.0001). Processing did not influence VGAS (p=0.819). Increasing dose resulted in significantly higher VGAS (p<0.0001). Visual grading analysis (VGA) identified a detector air kerma/image (DAK/image) of ~2.4 µGy as an ideal working point for NIP, DXD and DRX detectors. CONCLUSIONS: VGAS tracked IQ differences between detectors and dose levels but not image post-processing changes. VGA showed a DAK/image value above which perceived IQ did not improve, potentially useful for commissioning. KEY POINTS: • A VGA study detects IQ differences between detectors and dose levels. • The NIP detector matched the VGAS of the CsI DR detectors. • VGA data are useful in setting initial detector air kerma level. • Differences in NNPS were consistent with changes in VGAS.

Selection of appropriate endotracheal tube size using thoracic radiography in Beagle dogs.

OBJECTIVE: To determine the optimal endotracheal tube size in Beagle dogs using thoracic radiography. STUDY DESIGN: Prospective, randomized, crossover experimental study. ANIMALS: A total of eight healthy adult Beagle dogs. METHODS: Lateral thoracic radiographs were used to measure the internal tracheal diameter at the thoracic inlet. This measurement was multiplied by 60, 70 and 80% to determine the outer diameter of the endotracheal tube for each dog. In each treatment, medetomidine (5 µg kg-1) was administered intravenously (IV) for premedication. Anesthesia was induced with alfaxalone (2 mg kg-1) IV and maintained with isoflurane. After induction of anesthesia, the resistance to passage of the endotracheal tube through the trachea was scored by a single anesthesiologist. Air leak pressures (Pleak) were measured at intracuff pressures (Pcuff) of 20 and 25 mmHg (27 and 34 cmH2O). The results were analyzed using Friedman tests and repeated measures anova. RESULTS: There were statistically significant increases in resistance as the endotracheal tube size increased (p = 0.003). When Pcuff was 20 mmHg, mean Pleak for the 60, 70 and 80% treatments were 9.7 ± 6.7, 16.2 ± 4.2 and 17.4 ± 3.9 cmH2O, respectively, but no significant differences were found. When Pcuff was 25 mmHg, mean Pleak for the 60, 70 and 80% treatments were 10.6 ± 8.5, 19.7 ± 4.9 and 20.8 ± 3.6 cmH2O, respectively, and statistically significant increases were found between treatments 60 and 70% (p = 0.011) and between treatments 60 and 80% (p = 0.020). Three dogs in the 80% treatment had bloody mucus on the endotracheal tube cuff after extubation. CONCLUSIONS AND CLINICAL RELEVANCE: Results based on resistance to insertion of the endotracheal tube and the ability to achieve an air-tight seal suggest that an appropriately sized endotracheal tube for Beagle dogs is 70% of the internal tracheal diameter measured on thoracic radiography.

Early Radiographic and Tomographic Manifestations of Legionnaires' Disease.

PURPOSE: Legionnaires' disease (LD) may occur sporadically or in the course of outbreaks, where the typical radiological manifestations of the disease may better be delineated. We took advantage of a rare community-based epidemic of LD (181 patients) that occurred in 2012 in Quebec City, Canada, to describe the radiographic features of LD and compare the its tomographic presentation with that of community-acquired pneumonia caused by common bacteria other than Legionella pneumophila. METHODS: From the 181 individuals affected in the outbreak, we obtained the chest radiographs of 159 individuals (mean 63 ± 15 years of age) for detailed analysis; 33 patients had a computed tomography (CT) scan performed during the course of their illness. In a case-control study, we compared the CT scans of patients with LD with those of patients who had received a diagnosis of community-acquired pneumonia caused by a pathogen other than Legionella and confirmed by chest CT scan. RESULTS: Overall, LD most often presented as an airspace consolidation involving 1 of the lower lobes. Pleural effusion and mediastinal adenopathies were apparent only in a minority, whereas no pneumothorax or cavitation was noted. We did not find any significant difference in chest CT scan findings in patients with LD vs those with community-acquired pneumonia from other bacterial origin. No radiological finding was clearly associated with an increased risk of intensive care unit admission or mortality. CONCLUSIONS: The early radiographic and tomographic manifestations of LD are nonspecific and similar to those found in community-acquired pneumonia from other bacterial origin.

Aortic Arch Calcification Associated with Cardiovascular Events and Death among Patients with Acute Coronary Syndrome.

BACKGROUND: To date, it remains unsettled whether aortic arch calcification (AAC) has prognostic value in patients with acute coronary syndrome. METHODS: From January 1 to December 31, 2013, a total of 225 patients with acute coronary syndrome (mean age 72 ± 26 years, 75% male) were enrolled in this study. Patients admitted to the coronary care unit of a tertiary referral medical center under the preliminary diagnosis of acute coronary syndrome were retrospectively investigated. The primary endpoint was composite of long-term major adverse cardiovascular events. The secondary endpoints were 30-day and long-term all-cause mortality. RESULTS: Of the 225 patients enrolled in this study, 143 had detectable AAC. Those who had AAC were older, with higher Killip classification and thrombolysis in myocardial infarction (TIMI) score with a lower probability of single vessel disease. Acute coronary syndrome patients with AAC had significantly higher 30-day mortality (17.3% vs. 7.1%, log-rank p = 0.02). During a mean follow-up period of 165 ± 140 days (maximum 492 days), the calcification group had significantly increased cardiovascular deaths (27.6% vs. 11.2%, log-rank p = 0.002), all-cause mortality (28.3% vs. 11.2%, log-rank p = 0.001) and composite endpoint of major adverse cardiovascular events (39.4% vs. 24.6%, log-rank p = 0.01). After adjusting for age, gender, diabetes mellitus and hypertension, AAC was an independent risk factor for primary and secondary endpoints among patients with acute coronary syndrome. CONCLUSIONS: AAC provided valuable prognostic information on clinical outcomes in patients with acute coronary syndrome. However, different treatment strategies would be warranted for optimal risk reduction in such a population.