Is the generalizability of a developed artificial intelligence algorithm for COVID-19 on chest CT sufficient for clinical use? Results from the International Consortium for COVID-19 Imaging AI (ICOVAI).

OBJECTIVES: Only few published artificial intelligence (AI) studies for COVID-19 imaging have been externally validated. Assessing the generalizability of developed models is essential, especially when considering clinical implementation. We report the development of the International Consortium for COVID-19 Imaging AI (ICOVAI) model and perform independent external validation. METHODS: The ICOVAI model was developed using multicenter data (n = 1286 CT scans) to quantify disease extent and assess COVID-19 likelihood using the COVID-19 Reporting and Data System (CO-RADS). A ResUNet model was modified to automatically delineate lung contours and infectious lung opacities on CT scans, after which a random forest predicted the CO-RADS score. After internal testing, the model was externally validated on a multicenter dataset (n = 400) by independent researchers. CO-RADS classification performance was calculated using linearly weighted Cohen's kappa and segmentation performance using Dice Similarity Coefficient (DSC). RESULTS: Regarding internal versus external testing, segmentation performance of lung contours was equally excellent (DSC = 0.97 vs. DSC = 0.97, p = 0.97). Lung opacities segmentation performance was adequate internally (DSC = 0.76), but significantly worse on external validation (DSC = 0.59, p < 0.0001). For CO-RADS classification, agreement with radiologists on the internal set was substantial (kappa = 0.78), but significantly lower on the external set (kappa = 0.62, p < 0.0001). CONCLUSION: In this multicenter study, a model developed for CO-RADS score prediction and quantification of COVID-19 disease extent was found to have a significant reduction in performance on independent external validation versus internal testing. The limited reproducibility of the model restricted its potential for clinical use. The study demonstrates the importance of independent external validation of AI models. KEY POINTS: • The ICOVAI model for prediction of CO-RADS and quantification of disease extent on chest CT of COVID-19 patients was developed using a large sample of multicenter data. • There was substantial performance on internal testing; however, performance was significantly reduced on external validation, performed by independent researchers. The limited generalizability of the model restricts its potential for clinical use. • Results of AI models for COVID-19 imaging on internal tests may not generalize well to external data, demonstrating the importance of independent external validation.

Diffuse idiopathic skeletal hyperostosis is associated with incident stroke in patients with increased cardiovascular risk.

OBJECTIVES: Earlier retrospective studies have suggested a relation between DISH and cardiovascular disease, including myocardial infarction. The present study assessed the association between DISH and incidence of cardiovascular events and mortality in patients with high cardiovascular risk. METHODS: In this prospective cohort study, we included 4624 patients (mean age 58.4 years, 69.6% male) from the Second Manifestations of ARTerial disease cohort. The main end point was major cardiovascular events (MACE: stroke, myocardial infarction and vascular death). Secondary endpoints included all-cause mortality and separate vascular events. Cause-specific proportional hazard models were used to evaluate the risk of DISH on all outcomes, and subdistribution hazard models were used to evaluate the effect of DISH on the cumulative incidence. All models were adjusted for age, sex, body mass index, blood pressure, diabetes, non-HDL cholesterol, packyears, renal function and C-reactive protein. RESULTS: DISH was present in 435 (9.4%) patients. After a median follow-up of 8.7 (IQR 5.0-12.0) years, 864 patients had died and 728 patients developed a MACE event. DISH was associated with an increased cumulative incidence of ischaemic stroke. After adjustment in cause-specific modelling, DISH remained significantly associated with ischaemic stroke (HR 1.55; 95% CI: 1.01, 2.38), but not with MACE (HR 0.99; 95% CI: 0.79, 1.24), myocardial infarction (HR 0.88; 95% CI: 0.59, 1.31), vascular death (HR 0.94; 95% CI: 0.68, 1.27) or all-cause mortality (HR 0.94; 95% CI: 0.77, 1.16). CONCLUSION: The presence of DISH is independently associated with an increased incidence and risk for ischaemic stroke, but not with MACE, myocardial infarction, vascular death or all-cause mortality.

Summarizing the 4D image stack of ultrafast dynamic contrast enhancement MRI of breast cancer in 3D using color intensity projections.

BACKGROUND: Each ultrafast dynamic contrast-enhanced (DCE) MRI sequence for breast cancer generates thousands of images in a 4D stack that need to be reviewed by a radiologist. PURPOSE: To assess whether color intensity projections (CIP) effectively summarizes-using only the time of arrival (ToA) and amount of signal enhancement (AoE) of the contrast agent-the thousands of ultrafast images. STUDY TYPE: Retrospective cohort clinical trial. SUBJECTS: The study included 89 patients who had been scanned with an MRI beast protocol, of which 26 had breast cancer and 63 did not. FIELD STRENGTH/SEQUENCE: The 115-second ultrafast DCE sequence at 3T acquired 19 consecutive frames every 4.26 seconds with 152 slices per frame, yielding a 4D stack with 2888 2D images for each of water and fat. ASSESSMENT: For each slice of the water 4D stack a single CIP image was generated that encoded the ToA in the hue (red, orange, yellow, green, cyan, blue) and AoE in the brightness. Each of three experienced radiologists assigned a Breast Imaging and Reporting Data System (BI-RADS) score for each patient, first using only the CIP images, and subsequently using both CIP and the full 4D stack. STATISTICAL TESTS: The one-sided Fisher's exact test was used to determine statistical significance of both the sensitivity and specificity between the CIP alone and the CIP plus 4D stack. RESULTS: All malignancies were detected using only CIP by at least one of the radiologists. The CIP and CIP+4D sensitivities for reader 1 were 96% and 96% (P = 0.57), specificities were 59% and 65% (P = 0.29). For reader 2, the values were 96% and 100% (P = 0.51) with 62% and 71% (P = 0.17). For reader 3 the values were 92% and 96% (P = 0.50) with 51% and 62% (P = 0.07). DATA CONCLUSION: With a 95% sensitivity, CIP provides an effective summary of ultrafast DCE images of breast cancer. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1391-1399.

Observer variability for Lung-RADS categorisation of lung cancer screening CTs: impact on patient management.

OBJECTIVES: Lung-RADS represents a categorical system published by the American College of Radiology to standardise management in lung cancer screening. The purpose of the study was to quantify how well readers agree in assigning Lung-RADS categories to screening CTs; secondary goals were to assess causes of disagreement and evaluate its impact on patient management. METHODS: For the observer study, 80 baseline and 80 follow-up scans were randomly selected from the NLST trial covering all Lung-RADS categories in an equal distribution. Agreement of seven observers was analysed using Cohen's kappa statistics. Discrepancies were correlated with patient management, test performance and diagnosis of malignancy within the scan year. RESULTS: Pairwise interobserver agreement was substantial (mean kappa 0.67, 95% CI 0.58-0.77). Lung-RADS category disagreement was seen in approximately one-third (29%, 971) of 3360 reading pairs, resulting in different patient management in 8% (278/3360). Out of the 91 reading pairs that referred to scans with a tumour diagnosis within 1 year, discrepancies in only two would have resulted in a substantial management change. CONCLUSIONS: Assignment of lung cancer screening CT scans to Lung-RADS categories achieves substantial interobserver agreement. Impact of disagreement on categorisation of malignant nodules was low. KEY POINTS: • Lung-RADS categorisation of low-dose lung screening CTs achieved substantial interobserver agreement. • Major cause for disagreement was assigning a different nodule as risk-dominant. • Disagreement led to a different follow-up time in 8% of reading pairs.

Computer-aided detection improves detection of pulmonary nodules in chest radiographs beyond the support by bone-suppressed images.

PURPOSE: To evaluate the added value of computer-aided detection (CAD) for lung nodules on chest radiographs when radiologists have bone-suppressed images (BSIs) available. MATERIALS AND METHODS: Written informed consent was waived by the institutional review board. Selection of study images and study setup was reviewed and approved by the institutional review boards. Three hundred posteroanterior (PA) and lateral chest radiographs (189 radiographs with negative findings and 111 radiographs with a solitary nodule) in 300 subjects were selected from image archives at four institutions. PA images were processed by using a commercially available CAD, and PA BSIs were generated. Five radiologists and three residents evaluated the radiographs with BSIs available, first, without CAD and, second, after inspection of the CAD marks. Readers marked locations suspicious for a nodule and provided a confidence score for that location to be a nodule. Location-based receiver operating characteristic analysis was performed by using jackknife alternative free-response receiver operating characteristic analysis. Area under the curve (AUC) functioned as figure of merit, and P values were computed with the Dorfman-Berbaum-Metz method. RESULTS: Average nodule size was 16.2 mm. Stand-alone CAD reached a sensitivity of 74% at 1.0 false-positive mark per image. Without CAD, average AUC for observers was 0.812. With CAD, performance significantly improved to an AUC of 0.841 (P = .0001). CAD detected 127 of 239 nodules that were missed after evaluation of the radiographs together with BSIs pooled over all observers. Only 57 of these detections were eventually marked by the observers after review of CAD candidates. CONCLUSION: CAD improved radiologists' performance for the detection of lung nodules on chest radiographs, even when baseline performance was optimized by providing lateral radiographs and BSIs. Still, most of the true-positive CAD candidates are dismissed by observers.

Artificial Intelligence-based Quantification of Pleural Plaque Volume and Association With Lung Function in Asbestos-exposed Patients.

PURPOSE: Pleural plaques (PPs) are morphologic manifestations of long-term asbestos exposure. The relationship between PP and lung function is not well understood, whereas the time-consuming nature of PP delineation to obtain volume impedes research. To automate the laborious task of delineation, we aimed to develop automatic artificial intelligence (AI)-driven segmentation of PP. Moreover, we aimed to explore the relationship between pleural plaque volume (PPV) and pulmonary function tests. MATERIALS AND METHODS: Radiologists manually delineated PPs retrospectively in computed tomography (CT) images of patients with occupational exposure to asbestos (May 2014 to November 2019). We trained an AI model with a no-new-UNet architecture. The Dice Similarity Coefficient quantified the overlap between AI and radiologists. The Spearman correlation coefficient ( r ) was used for the correlation between PPV and pulmonary function test metrics. When recorded, these were vital capacity (VC), forced vital capacity (FVC), and diffusing capacity for carbon monoxide (DLCO). RESULTS: We trained the AI system on 422 CT scans in 5 folds, each time with a different fold (n = 84 to 85) as a test set. On these independent test sets combined, the correlation between the predicted volumes and the ground truth was r = 0.90, and the median overlap was 0.71 Dice Similarity Coefficient. We found weak to moderate correlations with PPV for VC (n = 80, r = -0.40) and FVC (n = 82, r = -0.38), but no correlation for DLCO (n = 84, r = -0.09). When the cohort was split on the median PPV, we observed statistically significantly lower VC ( P = 0.001) and FVC ( P = 0.04) values for the higher PPV patients, but not for DLCO ( P = 0.19). CONCLUSION: We successfully developed an AI algorithm to automatically segment PP in CT images to enable fast volume extraction. Moreover, we have observed that PPV is associated with loss in VC and FVC.

Acute pulmonary embolism: effect of a computer-assisted detection prototype on diagnosis--an observer study.

PURPOSE: To assess the effect of a computer-assisted detection (CAD) prototype on observer performance for detection of acute pulmonary embolism (PE) with computed tomographic (CT) pulmonary angiography. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, six observers with varying experience evaluated 158 PE-negative and 51 PE-positive CT pulmonary angiographic studies (mean age, 57 years; 111 women, 98 men) obtained consecutively during nights and weekends. Observers were asked to determine the presence of PE and to rank their diagnostic confidence without CAD and subsequently with CAD within a single reading session. Reading time was separately measured for both readings. Reader data were compared with an independent standard established by two readers, with a third in case of discordant results. Statistical evaluation was performed on a per-patient basis by using logistic regression for repeated measurements and Pearson correlation. RESULTS: With CAD, there was a significant increase in readers' sensitivity (P = .014) without loss of specificity (P = .853) on a per-patient basis. CAD assisted the readers in correcting an initial false-negative diagnosis in 15 cases, with the most proximal embolus at the segmental level in four cases and at the subsegmental level in 11 cases. In eight cases, readers accepted false-positive CAD candidate lesions on scans negative for PE, and in one case, a reader dismissed a true-positive finding. Reading time was extended by a mean of 22 seconds with the use of CAD. CONCLUSION: At the expense of increased reading time, CAD has the potential to increase reader sensitivity for detecting segmental and subsegmental PE without significant loss of specificity.

Patients with diffuse idiopathic skeletal hyperostosis have an increased burden of thoracic aortic calcifications.

Objectives: DISH has been associated with increased coronary artery calcifications and incident ischaemic stroke. The formation of bone along the spine may share pathways with calcium deposition in the aorta. We hypothesized that patients with DISH have increased vascular calcifications. Therefore we aimed to investigate the presence and extent of DISH in relation to thoracic aortic calcification (TAC) severity. Methods: This cross-sectional study included 4703 patients from the Second Manifestation of ARTerial disease cohort, consisting of patients with cardiovascular events or risk factors for cardiovascular disease. Chest radiographs were scored for DISH using the Resnick criteria. Different severities of TAC were scored arbitrarily from no TAC to mild, moderate or severe TAC. Using multivariate logistic regression, the associations between DISH and TAC were analysed with adjustments for age, sex, BMI, diabetes, smoking status, non-high-density lipoprotein cholesterol, cholesterol lowering drug usage, renal function and blood pressure. Results: A total of 442 patients (9.4%) had evidence of DISH and 1789 (38%) patients had TAC. The prevalence of DISH increased from 6.6% in the no TAC group to 10.8% in the mild, 14.3% in the moderate and 17.1% in the severe TAC group. After adjustments, DISH was significantly associated with the presence of TAC [odds ratio (OR) 1.46 [95% CI 1.17, 1.82)]. In multinomial analyses, DISH was associated with moderate TAC [OR 1.43 (95% CI 1.06, 1.93)] and severe TAC [OR 1.67 (95% CI 1.19, 2.36)]. Conclusions: Subjects with DISH have increased TACs, providing further evidence that patients with DISH have an increased burden of vascular calcifications.

Impact of image quality on the performance of computer-aided detection of pulmonary embolism.

OBJECTIVE: The purpose of this article is to assess the relationship between CT image quality and the number and type of false-positive (FP) findings found by a prototype computer-aided detection (CAD) algorithm for automatic detection of pulmonary embolism (PE). MATERIALS AND METHODS: This retrospective study included 278 subjects (138 men and 140 women; mean age, 57 years; range, 18-88 years) who underwent consecutive CT pulmonary angiographies performed during off hours. Twenty-four percent (68/278) of studies were reported as positive for PE. CAD findings were classified as true-positive or FP by two independent readers and, in cases of discordance, by a third radiologist. Each FP result was classified according to underlying cause. The degree of vascular enhancement, image noise, motion artifacts, overall quality, and presence of underlying lung disease were rated on a 4- or 5-point scale. Chi-square tests and t tests were used to test significance of differences. RESULTS: The mean number of FP CAD findings was 4.7 (median, 2) per examination. Most were caused by veins (30% [389/1,298]) or airspace consolidations (22% [286/1,298]). There was a significant positive association between the number of FP findings and image noise, motion artifacts, low vascular enhancement, low overall quality, and the extent of underlying disease. On a per-embolism basis, sensitivity decreased from 70.6% (214/303) for scans with zero to five FP findings, to 62.3% (33/53) for scans with six to 10 FP findings, to 60% (12/20) for scans with more than 10 FP findings. CONCLUSION: There is a strong association between CT image quality and the number of FP findings indicated by a CAD algorithm for the detection of PE.

Visceral Adipose Tissue and Different Measures of Adiposity in Different Severities of Diffuse Idiopathic Skeletal Hyperostosis.

BACKGROUND: Diffuse idiopathic skeletal hyperostosis (DISH) is associated with both obesity and type 2 diabetes. Our objective was to investigate the relation between DISH and visceral adipose tissue (VAT) in particular, as this would support a causal role of insulin resistance and low grade inflammation in the development of DISH. METHODS: In 4334 patients with manifest vascular disease, the relation between different adiposity measures and the presence of DISH was compared using z-scores via standard deviation logistic regression analyses. Analyses were stratified by sex and adjusted for age, systolic blood pressure, diabetes, non-HDL cholesterol, smoking status, and renal function. RESULTS: DISH was present in 391 (9%) subjects. The presence of DISH was associated with markers of adiposity and had a strong relation with VAT in males (OR: 1.35; 95%CI: 1.20-1.54) and females (OR: 1.43; 95%CI: 1.06-1.93). In males with the most severe DISH (extensive ossification of seven or more vertebral bodies) the association between DISH and VAT was stronger (OR: 1.61; 95%CI: 1.31-1.98), while increased subcutaneous fat was negatively associated with DISH (OR: 0.65; 95%CI: 0.49-0.95). In females, increased subcutaneous fat was associated with the presence of DISH (OR: 1.43; 95%CI: 1.14-1.80). CONCLUSION: Markers of adiposity, including VAT, are strongly associated with the presence of DISH. Subcutaneous adipose tissue thickness was negatively associated with more severe cases of DISH in males, while in females, increased subcutaneous adipose tissue was associated with the presence of DISH.

Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement.

Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer. Keywords: CT, Thorax, Lung, Computer Applications-Detection/Diagnosis, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Computer-assisted detection of pulmonary embolism: evaluation of pulmonary CT angiograms performed in an on-call setting.

PURPOSE: The purpose of the study was to assess the stand-alone performance of computer-assisted detection (CAD) for evaluation of pulmonary CT angiograms (CTPA) performed in an on-call setting. METHODS: In this institutional review board-approved study, we retrospectively included 292 consecutive CTPA performed during night shifts and weekends over a period of 16 months. Original reports were compared with a dedicated CAD system for pulmonary emboli (PE). A reference standard for the presence of PE was established using independent evaluation by two readers and consultation of a third experienced radiologist in discordant cases. RESULTS: Original reports had described 225 negative studies and 67 positive studies for PE. CAD found PE in seven patients originally reported as negative but identified by independent evaluation: emboli were located in segmental (n = 2) and subsegmental arteries (n = 5). The negative predictive value (NPV) of the CAD algorithm was 92% (44/48). On average there were 4.7 false positives (FP) per examination (median 2, range 0-42). In 72% of studies or=10 FP. CONCLUSION: CAD identified small emboli originally missed under clinical conditions and found 93% of the isolated subsegmental emboli. On average there were 4.7 FP per examination.

Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.

PURPOSE: To compare human observers to a mathematically derived computer model for differentiation between malignant and benign pulmonary nodules detected on baseline screening computed tomography (CT) scans. METHODS: A case-cohort study design was chosen. The study group consisted of 300 chest CT scans from the Danish Lung Cancer Screening Trial (DLCST). It included all scans with proven malignancies (n = 62) and two subsets of randomly selected baseline scans with benign nodules of all sizes (n = 120) and matched in size to the cancers, respectively (n = 118). Eleven observers and the computer model (PanCan) assigned a malignancy probability score to each nodule. Performances were expressed by area under the ROC curve (AUC). Performance differences were tested using the Dorfman, Berbaum and Metz method. Seven observers assessed morphological nodule characteristics using a predefined list. Differences in morphological features between malignant and size-matched benign nodules were analyzed using chi-square analysis with Bonferroni correction. A significant difference was defined at p < 0.004. RESULTS: Performances of the model and observers were equivalent (AUC 0.932 versus 0.910, p = 0.184) for risk-assessment of malignant and benign nodules of all sizes. However, human readers performed superior to the computer model for differentiating malignant nodules from size-matched benign nodules (AUC 0.819 versus 0.706, p < 0.001). Large variations between observers were seen for ROC areas and ranges of risk scores. Morphological findings indicative of malignancy referred to border characteristics (spiculation, p < 0.001) and perinodular architectural deformation (distortion of surrounding lung parenchyma architecture, p < 0.001; pleural retraction, p = 0.002). CONCLUSIONS: Computer model and human observers perform equivalent for differentiating malignant from randomly selected benign nodules, confirming the high potential of computer models for nodule risk estimation in population based screening studies. However, computer models highly rely on size as discriminator. Incorporation of other morphological criteria used by human observers to superiorly discriminate size-matched malignant from benign nodules, will further improve computer performance.

Semi-automatic classification of textures in thoracic CT scans.

The textural patterns in the lung parenchyma, as visible on computed tomography (CT) scans, are essential to make a correct diagnosis in interstitial lung disease. We developed one automatic and two interactive protocols for classification of normal and seven types of abnormal lung textures. Lungs were segmented and subdivided into volumes of interest (VOIs) with homogeneous texture using a clustering approach. In the automatic protocol, VOIs were classified automatically by an extra-trees classifier that was trained using annotations of VOIs from other CT scans. In the interactive protocols, an observer iteratively trained an extra-trees classifier to distinguish the different textures, by correcting mistakes the classifier makes in a slice-by-slice manner. The difference between the two interactive methods was whether or not training data from previously annotated scans was used in classification of the first slice. The protocols were compared in terms of the percentages of VOIs that observers needed to relabel. Validation experiments were carried out using software that simulated observer behavior. In the automatic classification protocol, observers needed to relabel on average 58% of the VOIs. During interactive annotation without the use of previous training data, the average percentage of relabeled VOIs decreased from 64% for the first slice to 13% for the second half of the scan. Overall, 21% of the VOIs were relabeled. When previous training data was available, the average overall percentage of VOIs requiring relabeling was 20%, decreasing from 56% in the first slice to 13% in the second half of the scan.

Bone suppressed images improve radiologists' detection performance for pulmonary nodules in chest radiographs.

OBJECTIVES: To assess the effect of bone suppression imaging on observer performance in detecting lung nodules in chest radiographs. MATERIALS AND METHODS: Posteroanterior (PA) and lateral digital chest radiographs of 111 (average age 65) patients with a CT proven solitary nodule (median diameter 15 mm), and 189 (average age 63) controls were read by 5 radiologists and 3 residents. Conspicuity of nodules on the radiographs was classified in obvious (n = 32), moderate (n = 32), subtle (n = 29) and very subtle (n = 18). Observers read the PA and lateral chest radiographs without and with an additional PA bone suppressed image (BSI) (ClearRead Bone Suppression 2.4, Riverain Technologies, Ohio) within one reading session. Multi reader multi case (MRMC) receiver operating characteristics (ROC) were used for statistical analysis. RESULTS: ROC analysis showed improved detection with use of BSI compared to chest radiographs alone (AUC = 0.883 versus 0.855; p = 0.004). Performance also increased at high specificities exceeding 80% (pAUC = 0.136 versus 0.124; p = 0.0007). Operating at a specificity of 90%, sensitivity increased with BSI from 66% to 71% (p = 0.0004). Increase of detection performance was highest for nodules with moderate and subtle conspicuity (p = 0.02; p = 0.03). CONCLUSION: Bone suppressed images improve radiologists' detection performance for pulmonary nodules, especially for those of moderate and subtle conspicuity.

Computed tomography pulmonary angiography in acute pulmonary embolism: the effect of a computer-assisted detection prototype used as a concurrent reader.

PURPOSE: To assess the effect of computer-assisted detection (CAD) on diagnostic accuracy, reader confidence, and reading time when used as a concurrent reader for the detection of acute pulmonary embolism in computed tomography pulmonary angiography. MATERIALS AND METHODS: In this institutional review board-approved retrospective study, 6 observers with varying experience evaluated 158 negative and 38 positive consecutive computed tomography pulmonary angiographies (mean patient age 60 y; 115 women) without and with CAD as a concurrent reader. Readers were asked to determine the presence of pulmonary embolism, assess their diagnostic confidence using a 5-point scale, and document their reading time. Results were compared with an independent standard established by 2 readers, and a third chest radiologist was consulted in case of discordant findings. RESULTS: Using logistic regression for repeated measurements, we found a significant increase in readers' sensitivity (P<0.001) without loss of specificity (P=0.855) with the effects being reader dependent (P<0.001). Sensitivities varied from 68% to 100% without CAD and from 76% to 100% with CAD. A 2-way analysis of variance showed a small but significant decrease in reading time (P<0.001), with the duration varying between 24 and 208 seconds without CAD and between 17 and 196 seconds with CAD, and a significant increase in readers' confidence scores using CAD as a concurrent reader (P<0.001). CONCLUSIONS: CAD as a concurrent reader has the potential to increase readers' sensitivity and confidence with a decrease in reading time without loss of specificity. The differences between readers, however, require further evaluation of CAD as a concurrent reader in a larger trial before stronger conclusions can be drawn.

Imaging of acute pulmonary embolism using multi-detector CT angiography: an update on imaging technique and interpretation.

Computed tomography angiography (CTA) of the pulmonary arteries has become the main diagnostic test for the evaluation of pulmonary embolism (PE). Not only due to the good availability, low cost and minimal invasiveness of this technique, but mainly because of the introduction of multi-detector CT techniques resulting in significant improvement in resolution, speed and image quality. This continuous gain in image acquisition speed went along with the introduction of new techniques of image acquisition, such as the dual-source CT scanning and novel concepts of image interpretation beyond morphological findings including the definition of the resulting perfusion defects and assessment of the cardiopulmonary circulation as a functional unit. This article will focus on technical and practical aspects to optimize CTPA examinations with modern multi-detector CT scanners, discusses aspects to be considered in specific patient groups (e.g., during pregnancy, young patients) and outlines new advents such as dual-source lung perfusion and automatic detection of pulmonary emboli.

Direct X-ray radiogrammetry versus dual-energy X-ray absorptiometry: assessment of bone density in children treated for acute lymphoblastic leukaemia and growth hormone deficiency.

BACKGROUND: In recent years interest in bone densitometry in children has increased. OBJECTIVE: To evaluate the clinical application of digital X-ray radiogrammetry (DXR) and compare the results with those of dual-energy X-ray absorptiometry (DXA). MATERIALS AND METHODS: A total of 41 children with acute lymphoblastic leukaemia (ALL) and 26 children with growth hormone deficiency (GHD) were included in this longitudinal study. Radiographs of the left hand were obtained and used for DXR. DXA of the total body and of the lumbar spine was performed. RESULTS: In both study populations significant correlations between DXR and DXA were found, and, with the exception of the correlation between DXR bone mineral density (DXR-BMD) and bone mineral apparent density in the GHD population, all correlations had a P-value of <0.001. During treatment a change in DXR-BMD was found in children with GHD. CONCLUSIONS: Our study showed that DXR in a paediatric population shows a strong correlation with DXA of the lumbar spine and total body and that it is able to detect a change in BMD during treatment.