Not all uncivil behaviours are equal: exploring responses to incivility in veterinary practice using vignettes.

Workplace incivility is considered a common workplace stressor, linked to a range of adverse impacts such as reduced wellbeing. However, there is a lack of research focused on how targets of incivility respond. The current study addresses that gap by examining responses to incivility within veterinary practice. Veterinarians and veterinary nurses (n = 132) evaluated six scenarios depicting two types of incivility (direct e.g. demeaning comments/indirect, for example, ignoring someone) across three instigators (clients, co-workers, senior colleagues), reporting their perception and appraisal of the uncivil behaviour depicted along with potential responses. Direct incivility was linked to responses such as reciprocation, exit, and support seeking, whereas indirect incivility was associated with affiliative and ignoring responses. Negative appraisal of incivility predicted a higher likelihood of exit, avoidance, support seeking and reporting responses. These findings suggest that incivility targets utilise a broad range of response options and adapt their response dependent on the situation.

This study investigated the influence of incivility type (direct/indirect) and instigator (client/co-worker/senior colleague) on response selection within veterinary practice. Participant responses were linked to incivility type and instigator status, indicating that utilisation of responses can be variable and adaptive to the situation.

Prevalence of hiatal hernia by blinded multidetector CT in patients with idiopathic pulmonary fibrosis.

Hiatal hernia (HH) is associated with gastro-oesophageal reflux (GOR) and/or GOR disease and may contribute to idiopathic pulmonary fibrosis (IPF). We hypothesised that HH evaluated by computed tomography is more common in IPF than in asthma or chronic obstructive pulmonary disease (COPD), and correlates with abnormal GOR measured by pH probe testing. Rates of HH were compared in three cohorts, IPF (n=100), COPD (n=60) and asthma (n=24), and evaluated for inter-observer agreement. In IPF, symptoms and anti-reflux medications were correlated with diffusing capacity of the lung for carbon monoxide (D(L,CO)) and composite physiologic index (CPI). HH was correlated with pH probe testing in IPF patients (n=14). HH was higher in IPF (39%) than either COPD (13.3%, p=0.00009) or asthma (16.67%, p=0.0139). The HH inter-observer κ agreement was substantial in IPF (κ=0.78) and asthma (κ=0.86), and moderate in COPD (κ=0.42). In IPF, HH did not correlate with lung function, except in those on anti-reflux therapy, who had a better D(L,CO) (p<0.03) and CPI (p<0.04). HH correlated with GOR as measured by DeMeester scores (p<0.04). HH is more common in IPF than COPD or asthma. In an IPF cohort, HH correlated with higher DeMeester scores, confirming abnormal acid GOR. Presence of HH alone was not associated with decreased lung function.

Automated detection of lung nodules in CT scans: preliminary results.

We have developed a fully automated computerized method for the detection of lung nodules in helical computed tomography (CT) scans of the thorax. This method is based on two-dimensional and three-dimensional analyses of the image data acquired during diagnostic CT scans. Lung segmentation proceeds on a section-by-section basis to construct a segmented lung volume within which further analysis is performed. Multiple gray-level thresholds are applied to the segmented lung volume to create a series of thresholded lung volumes. An 18-point connectivity scheme is used to identify contiguous three-dimensional structures within each thresholded lung volume, and those structures that satisfy a volume criterion are selected as initial lung nodule candidates. Morphological and gray-level features are computed for each nodule candidate. After a rule-based approach is applied to greatly reduce the number of nodule candidates that corresponds to nonnodules, the features of remaining candidates are merged through linear discriminant analysis. The automated method was applied to a database of 43 diagnostic thoracic CT scans. Receiver operating characteristic (ROC) analysis was used to evaluate the ability of the classifier to differentiate nodule candidates that correspond to actual nodules from false-positive candidates. The area under the ROC curve for this categorization task attained a value of 0.90 during leave-one-out-by-case evaluation. The automated method yielded an overall nodule detection sensitivity of 70% with an average of 1.5 false-positive detections per section when applied to the complete 43-case database. A corresponding nodule detection sensitivity of 89% with an average of 1.3 false-positive detections per section was achieved with a subset of 20 cases that contained only one or two nodules per case.

Automated segmentation and visualization of the pulmonary vascular tree in spiral CT angiography: an anatomy-oriented approach based on three-dimensional image analysis.

A new method for automated segmentation of the pulmonary vascular tree in spiral CT angiography was developed based on 3D image analysis techniques and anatomic knowledge. For efficient and effective segmentation, an anatomy-oriented approach was introduced, in which several anatomic structures are segmented sequentially and the properties of each segmented structure are used for the next step of segmentation and for validation of intermediate results. By use of clinical data of 12 patients, parameters for segmentation were analyzed and optimized. The effectiveness of the segmentation method was evaluated through the visual assessment by comparison between images of the segmentation results by volume rendering and images of maximum intensity projection of the original volume data.

Systemic to pulmonary venous shunt in superior vena cava occlusion.

This report describes systemic-to-pulmonary venous connections at the pleural level resulting from superior vena cava occlusion. The interval development of new venous collaterals within a 3-year period represents an advanced manifestation of SVC occlusion in this patient with a history of pleural disease. In this case, progressive venous thrombosis caused by underlying hypercoaguability led to the development of collaterals in unusual sites, including systemic-to-pulmonary venous shunting, and resulting in progressive cyanosis and death.

Automated registration of frontal and lateral radionuclide lung scans with digital chest radiographs.

RATIONALE AND OBJECTIVES: The purpose of this study was to develop and evaluate a fully automated method that spatially registers anterior, posterior, and lateral ventilation/perfusion (V/Q) images with posteroanterior and lateral digital chest radiographs to retrospectively combine the physiologic information contained in the V/Q scans with the anatomic detail in the chest radiographs. MATERIALS AND METHODS: Gray-level thresholding techniques were used to segment the aerated lung regions in the radiographic images. A variable-thresholding technique combined with an analysis of image noise was used to segment the adequately perfused or ventilated lung regions in the scintigraphic images. The physical dimensions of the segmented lung regions in images from both modalities were used to properly scale the radiographic images relative to the radionuclide images. Computer-determined locations of anatomic landmarks were then used to rotate and translate the images to achieve registration. Pairs of corresponding radionuclide and radiographic images were enhanced with color and then merged to create superimposed images. RESULTS: Five observers used a five-point rating scale to subjectively evaluate four image combinations for each of 50 cases. Of these ratings, 95.5% reflected very good, good, or fair registration. CONCLUSION: The automated method for the registration of radionuclide lung scans with digital chest radiographs to produce images that combine functional and structural information should benefit nuclear medicine physicians and radiologists, who must visually correlate images that differ greatly in physical size, resolution properties, and information content.

Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

PURPOSE: To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules. MATERIALS AND METHODS: Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis. RESULTS: Performance of the ANN was considerably greater with objective features (area under the ROC curve, Az = 0.854) than with subjective features (Az = 0.761). Performance of the ANN was also greater than that of the radiologists (Az = 0.752). CONCLUSION: The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.

Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs.

A novel contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities on a single chest radiograph. With this method, the lateral inclination is first corrected by rotating and shifting the original chest image so that the midline of the thorax is aligned with the vertical centerline of the original chest image. The rotated image is then flipped laterally to produce a reversed "mirror" image. Finally, the mirror image is warped and subtracted from the original image for derivation of the contralateral subtraction image. The three key techniques which are employed in this study are applied successively to the initial contralateral subtraction technique for acquisition of improved subtraction images. One hundred PA chest radiographs, including 50 normals and 50 abnormals, were used as the database for this study. The percentage of chest images, which were rated as being adequate, good, or excellent quality of subtraction images by employing a subjective evaluation method, was improved from 73% to 91% by use of the three key techniques. The contralateral subtraction technique can be used for detection of any asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on a single chest radiograph, and therefore has potential utility in a high proportion of abnormal cases.

Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography.

The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.

Computer-aided diagnosis of pulmonary nodules: results of a large-scale observer test.

PURPOSE: To determine the effect of computer-aided diagnosis (CAD) on the accuracy of pulmonary nodule detection. MATERIALS AND METHODS: Twenty abnormal chest radiographs, each with a single nodule, and 20 normal radiographs were digitized with a laser scanner. These images were analyzed by using a computer program that indicates areas that may represent pulmonary nodules. The radiographs were displayed on computer workstations in randomized order, and an observer test was performed. One hundred forty-six observers participated, including 23 chest radiologists, 54 other radiologists, 27 radiology residents, and 42 nonradiologists. Cases were interpreted first without and then with the use of CAD. The observers' responses were recorded on a continuous confidence rating scale. Detection accuracy both with and without CAD was evaluated with receiver operating characteristic analysis. RESULTS: The detection accuracy was significantly higher for all categories of observers when CAD was used (chest radiologists, P = 8 x 10(-6); other radiologists, P = 2 x 10(-16); radiology residents, P = 6 x 10(-7); and nonradiologists, P = 8 x 10(-9)). CONCLUSION: CAD has the potential to improve diagnostic accuracy in the detection of lung nodules on digital radiographs.

Computer-aided diagnosis in radiology: potential and pitfalls.

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.

Computerized detection of pulmonary nodules on CT scans.

Helical computed tomography (CT) is the most sensitive imaging modality for detection of pulmonary nodules. However, a single CT examination produces a large quantity of image data. Therefore, a computerized scheme has been developed to automatically detect pulmonary nodules on CT images. This scheme includes both two- and three-dimensional analyses. Within each section, gray-level thresholding methods are used to segment the thorax from the background and then the lungs from the thorax. A rolling ball algorithm is applied to the lung segmentation contours to avoid the loss of juxtapleural nodules. Multiple gray-level thresholds are applied to the volumetric lung regions to identify nodule candidates. These candidates represent both nodules and normal pulmonary structures. For each candidate, two- and three-dimensional geometric and gray-level features are computed. These features are merged with linear discriminant analysis to reduce the number of candidates that correspond to normal structures. This method was applied to a 17-case database. Receiver operating characteristic (ROC) analysis was used to evaluate the automated classifier. Results yielded an area under the ROC curve of 0.93 in the task of classifying candidates detected during thresholding as nodules or nonnodules.

Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change.

A temporal subtraction technique has been developed to assist radiologists in the detection of interval changes on chest radiographs. Although the overall performance of the current temporal subtraction technique is relatively good, severe misregistration errors, mainly due to AP inclination and/or rotation, are observed in some cases. In order to reduce these errors, we attempted to improve the subtraction scheme by applying an iterative image warping technique. In cases obtained with the new temporal subtraction technique 177 (97.8%) of 181 showed adequate, good, or excellent quality. We also found that 156 (86.2%) of cases obtained with the new scheme showed improvements in the quality of the subtraction images compared with the previous scheme. The results indicate that the performance of the temporal subtraction technique was greatly improved by use of the iterative image warping technique.

Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching.

The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.

Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs.

OBJECTIVE: We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy. MATERIALS AND METHODS: The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale. RESULTS: When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output. CONCLUSION: An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.

Computerized analysis of abnormal asymmetry in digital chest radiographs: evaluation of potential utility.

The purpose of this study was to develop and test a computerized method for the fully automated analysis of abnormal asymmetry in digital posteroanterior (PA) chest radiographs. An automated lung segmentation method was used to identify the aerated lung regions in 600 chest radiographs. Minimal a priori lung morphology information was required for this gray-level thresholding-based segmentation. Consequently, segmentation was applicable to grossly abnormal cases. The relative areas of segmented right and left lung regions in each image were compared with the corresponding area distributions of normal images to determine the presence of abnormal asymmetry. Computerized diagnoses were compared with image ratings assigned by a radiologist. The ability of the automated method to distinguish normal from asymmetrically abnormal cases was evaluated by using receiver operating characteristic (ROC) analysis, which yielded an area under the ROC curve of 0.84. This automated method demonstrated promising performance in its ability to detect abnormal asymmetry in PA chest images. We believe this method could play a role in a picture archiving and communications (PACS) environment to immediately identify abnormal cases and to function as one component of a multifaceted computer-aided diagnostic scheme.

Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease.

RATIONALE AND OBJECTIVES: The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease. MATERIALS AND METHODS: The authors used three-layer, feed-forward ANNs with a back-propagation algorithm. The ANNs were designed to distinguish between 11 interstitial lung diseases on the basis of 10 clinical parameters and 16 radiologic findings extracted by chest radiologists. Thus, the ANNs consisted of 26 input units and 11 output units. One hundred fifty actual clinical cases, 110 cases from previously published articles, and 110 hypothetical cases were used for training and testing the ANNs by using a round-robin (or leave-one-out) technique. ANN performance was evaluated with receiver operating characteristic (ROC) analysis. RESULTS: The Az (area under the ROC curve) obtained with actual clinical cases was 0.947, and both the sensitivity and specificity of the ANNs were approximately 90% in terms of indicating the correct diagnosis with the two largest output values among the 11 diseases. CONCLUSION: ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.

Computer-aided diagnosis for detection of interstitial opacities on chest radiographs.

OBJECTIVE: Our objective was to evaluate the impact of a computer-aided diagnostic scheme on radiologists' interpretations of chest radiographs with interstitial opacities by performing an observer test using receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: Twenty chest radiographs with normal findings and 20 chest radiographs with abnormal findings were used. Each radiograph was divided into four quadrants. One hundred twenty-nine quadrants (80 normal and 49 abnormal quadrants) were used for testing because we excluded 31 equivocal quadrants. Sixteen independent observers (10 residents and six attending radiologists) participated in this study. The radiologists' performance without and with computer assistance, which indicated cases with normal and abnormal findings by various markers, was evaluated by ROC analysis. RESULTS: The diagnostic accuracy of the observers improved by a statistically significant magnitude when computer-aided diagnosis was used. Thus, the values for the area under the ROC curve obtained with and without the computer-aided diagnostic output were .970 and .948 (p = .0002), respectively, for all observers; .969 and .943 (p = .0006), respectively, for the residents' subgroup; and .972 and .960 (p = .162), respectively, for the attending radiologists' subgroup. The value for the area under the ROC curve for the computerized scheme by itself was .943. CONCLUSION: Our computer-aided diagnostic scheme can assist radiologists in the diagnosis or exclusion of interstitial disease on chest radiographs.