Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI)

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease - Reporting and Data System.: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI)

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI)

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease - Reporting and Data System An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI)

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care. Keywords: Coronary Artery Disease, Coronary CTA, CAD-RADS, Reporting and Data System, Stenosis Severity, Report Standardization Terminology, Plaque Burden, Ischemia Supplemental material is available for this article. This article is published synchronously in Radiology: Cardiothoracic Imaging, Journal of Cardiovascular Computed Tomography, JACC: Cardiovascular Imaging, Journal of the American College of Radiology, and International Journal for Cardiovascular Imaging. © 2022 Society of Cardiovascular Computed Tomography. Published by RSNA with permission.

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

Background

There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottleneck for developing multi-disease classifiers is a lack of manually annotated data. The purpose of this work was to develop high throughput multi-label annotators for body CT reports that can be applied across a variety of abnormalities, organs, and disease states thereby mitigating the need for human annotation.

Methods

We used a dictionary approach to develop rule-based algorithms (RBA) for extraction of disease labels from radiology text reports. We targeted three organ systems (lungs/pleura, liver/gallbladder, kidneys/ureters) with four diseases per system based on their prevalence in our dataset. To expand the algorithms beyond pre-defined keywords, attention-guided recurrent neural networks (RNN) were trained using the RBA-extracted labels to classify reports as being positive for one or more diseases or normal for each organ system. Alternative effects on disease classification performance were evaluated using random initialization or pre-trained embedding as well as different sizes of training datasets. The RBA was tested on a subset of 2158 manually labeled reports and performance was reported as accuracy and F-score. The RNN was tested against a test set of 48,758 reports labeled by RBA and performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.

Results

Manual validation of the RBA confirmed 91–99% accuracy across the 15 different labels. Our models extracted disease labels from 261,229 radiology reports of 112,501 unique subjects. Pre-trained models outperformed random initialization across all diseases. As the training dataset size was reduced, performance was robust except for a few diseases with a relatively small number of cases. Pre-trained classification AUCs reached > 0.95 for all four disease outcomes and normality across all three organ systems.

Conclusions

Our label-extracting pipeline was able to encompass a variety of cases and diseases in body CT reports by generalizing beyond strict rules with exceptional accuracy. The method described can be easily adapted to enable automated labeling of hospital-scale medical data sets for training image-based disease classifiers.

Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning

PURPOSE

To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports.

MATERIALS AND METHODS

This retrospective study included a total of 12 092 patients (mean age, 57 years ± 18 [standard deviation]; 6172 women) for model development and testing. Rule-based algorithms were used to extract 19 225 disease labels from 13 667 body CT scans performed between 2012 and 2017. Using a three-dimensional DenseVNet, three organ systems were segmented: lungs and pleura, liver and gallbladder, and kidneys and ureters. For each organ system, a three-dimensional convolutional neural network classified each as no apparent disease or for the presence of four common diseases, for a total of 15 different labels across all three models. Testing was performed on a subset of 2158 CT volumes relative to 2875 manually derived reference labels from 2133 patients (mean age, 58 years ± 18; 1079 women). Performance was reported as area under the receiver operating characteristic curve (AUC), with 95% CIs calculated using the DeLong method.

RESULTS

Manual validation of the extracted labels confirmed 91%-99% accuracy across the 15 different labels. AUCs for lungs and pleura labels were as follows: atelectasis, 0.77 (95% CI: 0.74, 0.81); nodule, 0.65 (95% CI: 0.61, 0.69); emphysema, 0.89 (95% CI: 0.86, 0.92); effusion, 0.97 (95% CI: 0.96, 0.98); and no apparent disease, 0.89 (95% CI: 0.87, 0.91). AUCs for liver and gallbladder were as follows: hepatobiliary calcification, 0.62 (95% CI: 0.56, 0.67); lesion, 0.73 (95% CI: 0.69, 0.77); dilation, 0.87 (95% CI: 0.84, 0.90); fatty, 0.89 (95% CI: 0.86, 0.92); and no apparent disease, 0.82 (95% CI: 0.78, 0.85). AUCs for kidneys and ureters were as follows: stone, 0.83 (95% CI: 0.79, 0.87); atrophy, 0.92 (95% CI: 0.89, 0.94); lesion, 0.68 (95% CI: 0.64, 0.72); cyst, 0.70 (95% CI: 0.66, 0.73); and no apparent disease, 0.79 (95% CI: 0.75, 0.83).

CONCLUSION

Weakly supervised deep learning models were able to classify diverse diseases in multiple organ systems from CT scans.Keywords: CT, Diagnosis/Classification/Application Domain, Semisupervised Learning, Whole-Body Imaging© RSNA, 2022.

The direct costs of coronary CT angiography relative to contrast-enhanced thoracic CT: Time-driven activity-based costing

BACKGROUND

Coronary CT angiography (CCTA) and contrast-enhanced thoracic CT (CECT) are distinctly different diagnostic procedures that involve intravenous contrast-enhanced CT of the chest. The technical component of these procedures is reimbursed at the same rate by the Centers for Medicare and Medicaid Services (CMS). This study tests the hypothesis that the direct costs of performing these exams are significantly different.

METHODS

Direct costs for both procedures were measured using a time-driven activity-based costing (TDABC) model. The exams were segmented into four phases: preparation, scanning, post-scan monitoring, and image processing. Room occupancy and direct labor times were collected for scans of 54 patients (28 CCTA and 26 CECT studies), in seven medical facilities within the USA and used to impute labor and equipment cost. Contrast material costs were measured directly. Cost differences between the exams were analyzed for significance and variability.

RESULTS

Mean CCTA duration was 3.2 times longer than CECT (121 and 37 ​min, respectively. p ​< ​0.01). Mean CCTA direct costs were 3.4 times those of CECT ($189.52 and $55.28, respectively, p ​< ​0.01). Both labor and capital equipment costs for CCTA were significantly more expensive (6.5 and 1.8-fold greater, respectively, p ​< ​0.001). Segmented by procedural phase, CCTA was both longer and more expensive for each (p ​< ​0.01). Mean direct costs for CCTA exceeded the standard CMS technical reimbursement of $182.25 without accounting for indirect or overhead costs.

CONCLUSION

The direct cost of performing CCTA is significantly higher than CECT, and thus reimbursement schedules that treat these procedures similarly undervalue the resources required to perform CCTA and possibly decrease access to the procedure.

COVID-19 Imaging: What We Know Now and What Remains Unknown

Infection with SARS-CoV-2 ranges from an asymptomatic condition to a severe and sometimes fatal disease, with mortality most frequently being the result of acute lung injury. The role of imaging has evolved during the pandemic, with CT initially being an alternative and possibly superior testing method compared with reverse transcriptase-polymerase chain reaction (RT-PCR) testing and evolving to having a more limited role based on specific indications. Several classification and reporting schemes were developed for chest imaging early during the pandemic for patients suspected of having COVID-19 to aid in triage when the availability of RT-PCR testing was limited and its level of performance was unclear. Interobserver agreement for categories with findings typical of COVID-19 and those suggesting an alternative diagnosis is high across multiple studies. Furthermore, some studies looking at the extent of lung involvement on chest radiographs and CT images showed correlations with critical illness and a need for mechanical ventilation. In addition to pulmonary manifestations, cardiovascular complications such as thromboembolism and myocarditis have been ascribed to COVID-19, sometimes contributing to neurologic and abdominal manifestations. Finally, artificial intelligence has shown promise for use in determining both the diagnosis and prognosis of COVID-19 pneumonia with respect to both radiography and CT.

Task-dependent estimability index to assess the quality of cardiac computed tomography angiography for quantifying coronary stenosis

Purpose: Quantifying stenosis in cardiac computed tomography angiography (CTA) images remains a difficult task, as image noise and cardiac motion can degrade image quality and distort underlying anatomic information. The purpose of this study was to develop a computational framework to objectively assess the precision of quantifying coronary stenosis in cardiac CTA.

Approach: The framework used models of coronary vessels and plaques, asymmetric motion point spread functions, CT image blur (task-based modulation transfer functions) and noise (noise-power spectrums), and an automated maximum-likelihood estimator implemented as a matched template squared-difference operator. These factors were integrated into an estimability index (e′) as a task-based measure of image quality in cardiac CTA. The e′ index was applied to assess how well it can to predict the quality of 132 clinical cases selected from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial. The cases were divided into two cohorts, high quality and low quality, based on clinical scores and the concordance of clinical evaluations of cases by experienced cardiac imagers. The framework was also used to ascertain protocol factors for CTA Biomarker initiative of the Quantitative Imaging Biomarker Alliance (QIBA).

Results: The e′ index categorized the patient datasets with an area under the curve of 0.985, an accuracy of 0.977, and an optimal e′ threshold of 25.58 corresponding to a stenosis estimation precision (standard deviation) of 3.91%. Data resampling and training–test validation methods demonstrated stable classifier thresholds and receiver operating curve performance. The framework was successfully applicable to the QIBA objective.

Conclusions: A computational framework to objectively quantify stenosis estimation task performance was successfully implemented and was reflective of clinical results in the context of a prominent clinical trial with diverse sites, readers, scanners, acquisition protocols, and patients. It also demonstrated the potential for prospective optimization of imaging protocols toward targeted precision and measurement consistency in cardiac CT images.

Chest CT Diagnosis and Clinical Management of Drug-related Pneumonitis in Patients Receiving Molecular Targeting Agents and Immune Checkpoint Inhibitors: A Position Paper from the Fleischner Society

Use of molecular targeting agents and immune checkpoint inhibitors (ICIs) has increased the frequency and broadened the spectrum of lung toxicity, particularly in patients with cancer. The diagnosis of drug-related pneumonitis (DRP) is usually achieved by excluding other potential known causes. Awareness of the incidence and risk factors for DRP is becoming increasingly important. The severity of symptoms associated with DRP may range from mild or none to life-threatening with rapid progression to death. Imaging features of DRP should be assessed in consideration of the distribution of lung parenchymal abnormalities (radiologic pattern approach). The CT patterns reflect acute (diffuse alveolar damage) interstitial pneumonia and transient (simple pulmonary eosinophilia) lung abnormality, subacute interstitial disease (organizing pneumonia and hypersensitivity pneumonitis), and chronic interstitial disease (nonspecific interstitial pneumonia). A single drug can be associated with multiple radiologic patterns. Treatment of a patient suspected of having DRP generally consists of drug discontinuation, immunosuppressive therapy, or both, along with supportive measures eventually including supplemental oxygen and intensive care. In this position paper, the authors provide diagnostic criteria and management recommendations for DRP that should be of interest to radiologists, clinicians, clinical trialists, and trial sponsors, among others. This article is a simultaneous joint publication in Radiology and CHEST. The articles are identical except for stylistic changes in keeping with each journal's style. Either version may be used in citing this article. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

Chest CT Diagnosis and Clinical Management of Drug-Related Pneumonitis in Patients Receiving Molecular Targeting Agents and Immune Checkpoint Inhibitors: A Position Paper From the Fleischner Society

Use of molecular targeting agents and immune checkpoint inhibitors (ICIs) has increased the frequency and broadened the spectrum of lung toxicity, particularly in patients with cancer. The diagnosis of drug-related pneumonitis (DRP) is usually achieved by excluding other potential known causes. Awareness of the incidence and risk factors for DRP is becoming increasingly important. The severity of symptoms associated with DRP may range from mild or none to life-threatening with rapid progression to death. Imaging features of DRP should be assessed in consideration of the distribution of lung parenchymal abnormalities (radiologic pattern approach). The CT patterns reflect acute (diffuse alveolar damage) interstitial pneumonia and transient (simple pulmonary eosinophilia) lung abnormality, subacute interstitial disease (organizing pneumonia and hypersensitivity pneumonitis), and chronic interstitial disease (nonspecific interstitial pneumonia). A single drug can be associated with multiple radiologic patterns. Treatment of a patient suspected of having DRP generally consists of drug discontinuation, immunosuppressive therapy, or both, along with supportive measures eventually including supplemental oxygen and intensive care. In this position paper, the authors provide diagnostic criteria and management recommendations for DRP that should be of interest to radiologists, clinicians, clinical trialists, and trial sponsors, among others.

Imaging of pulmonary hypertension in adults: a position paper from the Fleischner Society

Pulmonary hypertension (PH) is defined by a mean pulmonary artery pressure greater than 20 mmHg and classified into five different groups sharing similar pathophysiologic mechanisms, haemodynamic characteristics, and therapeutic management. Radiologists play a key role in the multidisciplinary assessment and management of PH. A working group was formed from within the Fleischner Society based on expertise in the imaging and/or management of patients with PH, as well as experience with methodologies of systematic reviews. The working group identified key questions focusing on the utility of CT, MRI, and nuclear medicine in the evaluation of PH: a) Is noninvasive imaging capable of identifying PH? b) What is the role of imaging in establishing the cause of PH? c) How does imaging determine the severity and complications of PH? d) How should imaging be used to assess chronic thromboembolic PH before treatment? e) Should imaging be performed after treatment of PH? This systematic review and position paper highlights the key role of imaging in the recognition, work-up, treatment planning, and follow-up of PH.

Imaging of Pulmonary Hypertension in Adults: A Position Paper from the Fleischner Society

Pulmonary hypertension (PH) is defined by a mean pulmonary artery pressure greater than 20 mm Hg and classified into five different groups sharing similar pathophysiologic mechanisms, hemodynamic characteristics, and therapeutic management. Radiologists play a key role in the multidisciplinary assessment and management of PH. A working group was formed from within the Fleischner Society based on expertise in the imaging and/or management of patients with PH, as well as experience with methodologies of systematic reviews. The working group identified key questions focusing on the utility of CT, MRI, and nuclear medicine in the evaluation of PH: (a) Is noninvasive imaging capable of identifying PH? (b) What is the role of imaging in establishing the cause of PH? (c) How does imaging determine the severity and complications of PH? (d) How should imaging be used to assess chronic thromboembolic PH before treatment? (e) Should imaging be performed after treatment of PH? This systematic review and position paper highlights the key role of imaging in the recognition, work-up, treatment planning, and follow-up of PH. This article is a simultaneous joint publication in Radiology and European Respiratory Journal. The articles are identical except for stylistic changes in keeping with each journal's style. Either version may be used in citing this article. © 2021 RSNA and the European Respiratory Society. Online supplemental material is available for this article.

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes

Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest multiply-annotated volumetric medical imaging data set reported. To annotate this data set, we developed a rule-based method for automatically extracting abnormality labels from free-text radiology reports with an average F-score of 0.976 (min 0.941, max 1.0). We also developed a model for multi-organ, multi-disease classification of chest CT volumes that uses a deep convolutional neural network (CNN). This model reached a classification performance of AUROC >0.90 for 18 abnormalities, with an average AUROC of 0.773 for all 83 abnormalities, demonstrating the feasibility of learning from unfiltered whole volume CT data. We show that training on more labels improves performance significantly: for a subset of 9 labels - nodule, opacity, atelectasis, pleural effusion, consolidation, mass, pericardial effusion, cardiomegaly, and pneumothorax - the model's average AUROC increased by 10% when the number of training labels was increased from 9 to all 83. All code for volume preprocessing, automated label extraction, and the volume abnormality prediction model is publicly available. The 36,316 CT volumes and labels will also be made publicly available pending institutional approval.

The Role of Chest Imaging in Patient Management during the COVID-19 Pandemic: A Multinational Consensus Statement from the Fleischner Society

With more than 900 000 confirmed cases worldwide and nearly 50 000 deaths during the first 3 months of 2020, the coronavirus disease 2019 (COVID-19) pandemic has emerged as an unprecedented health care crisis. The spread of COVID-19 has been heterogeneous, resulting in some regions having sporadic transmission and relatively few hospitalized patients with COVID-19 and others having community transmission that has led to overwhelming numbers of severe cases. For these regions, health care delivery has been disrupted and compromised by critical resource constraints in diagnostic testing, hospital beds, ventilators, and health care workers who have fallen ill to the virus exacerbated by shortages of personal protective equipment. Although mild cases mimic common upper respiratory viral infections, respiratory dysfunction becomes the principal source of morbidity and mortality as the disease advances. Thoracic imaging with chest radiography and CT are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pretest probability, risk factors for disease progression, and critical resource constraints. To address this deficit, a multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing patients with COVID-19 across a spectrum of health care environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints. Fourteen key questions, corresponding to 11 decision points within the three scenarios and three additional clinical situations, were rated by the panel based on the anticipated value of the information that thoracic imaging would be expected to provide. The results were aggregated, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of chest radiography and CT in the management of COVID-19.

The Role of Chest Imaging in Patient Management During the COVID-19 Pandemic: A Multinational Consensus Statement From the Fleischner Society

With more than 900,000 confirmed cases worldwide and nearly 50,000 deaths during the first 3 months of 2020, the coronavirus disease 2019 (COVID-19) pandemic has emerged as an unprecedented health care crisis. The spread of COVID-19 has been heterogeneous, resulting in some regions having sporadic transmission and relatively few hospitalized patients with COVID-19 and others having community transmission that has led to overwhelming numbers of severe cases. For these regions, health care delivery has been disrupted and compromised by critical resource constraints in diagnostic testing, hospital beds, ventilators, and health care workers who have fallen ill to the virus exacerbated by shortages of personal protective equipment. Although mild cases mimic common upper respiratory viral infections, respiratory dysfunction becomes the principal source of morbidity and mortality as the disease advances. Thoracic imaging with chest radiography and CT are key tools for pulmonary disease diagnosis and management, but their role in the management of COVID-19 has not been considered within the multivariable context of the severity of respiratory disease, pretest probability, risk factors for disease progression, and critical resource constraints. To address this deficit, a multidisciplinary panel comprised principally of radiologists and pulmonologists from 10 countries with experience managing patients with COVID-19 across a spectrum of health care environments evaluated the utility of imaging within three scenarios representing varying risk factors, community conditions, and resource constraints. Fourteen key questions, corresponding to 11 decision points within the three scenarios and three additional clinical situations, were rated by the panel based on the anticipated value of the information that thoracic imaging would be expected to provide. The results were aggregated, resulting in five main and three additional recommendations intended to guide medical practitioners in the use of chest radiography and CT in the management of COVID-19.

Influence of background lung characteristics on nodule detection with computed tomography

We sought to characterize local lung complexity in chest computed tomography (CT) and to characterize its impact on the detectability of pulmonary nodules. Forty volumetric chest CT scans were created by embedding between three and five simulated 5-mm lung nodules into one of three volumetric chest CT datasets. Thirteen radiologists evaluated 157 nodules, resulting in 2041 detection opportunities. Analyzing the substrate CT data prior to nodule insertion, 14 image features were measured within a region around each nodule location. A generalized linear mixed-effects statistical model was fit to the data to verify the contribution of each metric on detectability. The model was tuned for simplicity, interpretability, and generalizability using stepwise regression applied to the primary features and their interactions. We found that variables corresponding to each of five categories (local structural distractors, local intensity, global context, local vascularity, and contiguity with structural distractors) were significant ( p < 0.01 ) factors in a standardized model. Moreover, reader-specific models conveyed significant differences among readers with significant distraction (missed detections) influenced by local intensity- versus local-structural characteristics being mutually exclusive. Readers with significant local intensity distraction ( n = 10 ) detected substantially fewer lung nodules than those who were significantly distracted by local structure ( n = 2 ), 46.1% versus 65.3% mean nodules detected, respectively.

Local complexity metrics to quantify the effect of anatomical noise on detectability of lung nodules in chest CT imaging

The purpose of this study is to (1) develop metrics to characterize the regional anatomical complexity of the lungs, and (2) relate these metrics with lung nodule detection in chest CT. A free-scrolling reader-study with virtually inserted nodules (13 radiologists × 157 total nodules = 2041 responses) is used to characterize human detection performance. Metrics of complexity based on the local density and orientation of distracting vasculature are developed for two-dimensional (2-D) and three-dimensional (3-D) considerations of the image volume. Assessed characteristics included the distribution of 2-D/3-D vessel structures of differing orientation (dubbed "2-D/3-D and dot-like/line-like distractor indices"), contiguity of inserted nodules with local vasculature, mean local gray-level surrounding each nodule, the proportion of lung voxels to total voxels in each section, and 3-D distance of each nodule from the trachea bifurcation. A generalized linear mixed-effects statistical model is used to determine the influence of each these metrics on nodule detectability. In order of decreasing effect size: 3-D line-like distractor index, 2-D line-like distractor index, 2-D dot-like distractor index, local mean gray-level, contiguity with 2-D dots, lung area, and contiguity with 3-D lines all significantly affect detectability ( P < 0.05 ). These data demonstrate that local lung complexity degrades detection of lung nodules.

2018 ACC/HRS/NASCI/SCAI/SCCT Expert Consensus Document on Optimal Use of Ionizing Radiation in Cardiovascular Imaging-Best Practices for Safety and Effectiveness, Part 1: Radiation Physics and Radiation Biology: A Report of the American College of Cardiology Task Force on Expert Consensus Decision Pathways Developed in Collaboration With Mended Hearts

The stimulus to create this document was the recognition that ionizing radiation-guided cardiovascular procedures are being performed with increasing frequency, leading to greater patient radiation exposure and, potentially, to greater exposure for clinical personnel. Although the clinical benefit of these procedures is substantial, there is concern about the implications of medical radiation exposure. The American College of Cardiology leadership concluded that it is important to provide practitioners with an educational resource that assembles and interprets the current radiation knowledge base relevant to cardiovascular procedures. By applying this knowledge base, cardiovascular practitioners will be able to select procedures optimally, and minimize radiation exposure to patients and to clinical personnel. Optimal Use of Ionizing Radiation in Cardiovascular Imaging: Best Practices for Safety and Effectiveness is a comprehensive overview of ionizing radiation use in cardiovascular procedures and is published online. To provide the most value to our members, we divided the print version of this document into 2 focused parts. Part I: Radiation Physics and Radiation Biology addresses the issue of medical radiation exposure, the basics of radiation physics and dosimetry, and the basics of radiation biology and radiation-induced adverse effects. Part II: Radiological Equipment Operation, Dose-Sparing Methodologies, Patient and Medical Personnel Protection covers the basics of operation and radiation delivery for the 3 cardiovascular imaging modalities (x-ray fluoroscopy, x-ray computed tomography, and nuclear scintigraphy) and will be published in the next issue of the Journal.

Creating Value through Incremental Innovation: Managing Culture, Structure, and Process

While the looming threat of large-scale disruptive innovation consumes disproportionate attention, incremental innovation remains an important tool for preserving and growing radiology practices within a dynamic marketplace. Incremental innovation, defined as the process of making improvements or additions to an organization while maintaining the organization's core product or service model, is accessible to practices of all sizes and must not be overlooked if practices are to maintain their competitive advantage. This article explores cultural, structural, and process enablers for incremental innovation. Successful innovation cultures foster the ability to import and exploit external knowledge (adaptive capacity), encourage creative thought from all levels of the organization, display sensitivity toward the competency-destroying potential of certain changes, cultivate a positive perceptual bias toward organizational threats, and build tolerance for risk and uncertainty when prototyping new ideas. Structural elements promoting incremental innovation include dedicated resources for innovation planning, flexible and organic team structures, strong centralized governance models, robust communication systems, and organizational incentives encouraging exploration of new concepts. Processes important to innovation include periodic environmental scanning, strategic and scenario planning, use of an objectively gated system for testing and filtering new ideas, and use of an approach to implementation that emphasizes empowerment of project managers, removal of barriers, and proactive communication around change.

Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Coronary Artery Disease - Reporting and Data System (CAD-RADS): An Expert Consensus Document of SCCT, ACR and NASCI: Endorsed by the ACC

The intent of CAD-RADS - Coronary Artery Disease Reporting and Data System is to create a standardized method to communicate findings of coronary CT angiography (coronary CTA) in order to facilitate decision-making regarding further patient management. The suggested CAD-RADS classification is applied on a per-patient basis and represents the highest-grade coronary artery lesion documented by coronary CTA. It ranges from CAD-RADS 0 (Zero) for the complete absence of stenosis and plaque to CAD-RADS 5 for the presence of at least one totally occluded coronary artery and should always be interpreted in conjunction with the impression found in the report. Specific recommendations are provided for further management of patients with stable or acute chest pain based on the CAD-RADS classification. The main goal of CAD-RADS is to standardize reporting of coronary CTA results and to facilitate communication of test results to referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will provide a framework of standardization that may benefit education, research, peer-review and quality assurance with the potential to ultimately result in improved quality of care.

Variations in the functional visual field for detection of lung nodules on chest computed tomography: Impact of nodule size, distance, and local lung complexity

PURPOSE

To explore the characteristics that impact lung nodule detection by peripheral vision when searching for lung nodules on chest CT-scans.

METHODS

This study was approved by the local IRB and is HIPAA compliant. A simulated primary (1°) target mass (2 × 2 × 5 cm) was embedded into 5 cm thick subvolumes (SV) extracted from three unenhanced lung MDCT scans (64 row, 1.25 mm thickness, 0.7 mm increment). One of 30 solid, secondary nodules with either 3-4 mm and 5-8 mm diameters were embedded into 192 of 207 SVs. The secondary nodule was placed at a random depth within each SV, a transverse distance of 2.5, 5, 7.5, or 10 mm, and along one of eight rays cast every 45° from the center of the 1° mass. Video recordings of transverse paging in cranio-caudal direction were created for each SV (frame rate three sections/sec). Six radiologists observed each cine-loop once while gaze-tracking hardware assured that gaze was centered on the 1° mass. Each radiologist assigned a confidence rating (0-5) to the detection of a secondary nodule and indicated its location. Detection sensitivity was analyzed relative to secondary nodule size, transverse distance, radial orientation, and lung complexity. Lung complexity was characterized by the number of particles (connected pixels) and the sum of the area of all particles above a -500 HU threshold within regions of interest around the 1° mass and secondary nodule.

RESULTS

Using a proportional odds logistic regression model and eliminating redundant predictors, models fit individually to each reader resulted in the following decreasing order of association based on greatest reduction in Akaike Information Criterion: secondary nodule diameter (6/6 readers, P < 0.001), distance from central mass (6/6 readers, P < 0.001), lung complexity particle count (5/6 readers, P = 0.05), and lung complexity particle area (3/6 readers, P = 0.03). Substantial inter-reader differences in sensitivity to decreasing nodule diameter, distance, and complexity characteristics were observed.

CONCLUSIONS

Of the investigated parameters, secondary nodule size, distance from the gaze center and lung complexity (particle number and area) significantly impact nodule detection with peripheral vision.

Costing in Radiology and Health Care: Rationale, Relativity, Rudiments, and Realities

Costs direct decisions that influence the effectiveness of radiology in the care of patients on a daily basis. Yet many radiologists struggle to harness the power of cost measurement and cost management as a critical path toward establishing their value in patient care. When radiologists cannot articulate their value, they risk losing control over how imaging is delivered and supported. In the United States, recent payment trends directing value-based payments for bundles of care advance the imperative for radiology providers to articulate their value. This begins with the development of an understanding of the providers' own costs, as well as the complex interrelationships and imaging-associated costs of other participants across the imaging value chain. Controlling the costs of imaging necessitates understanding them at a procedural level and quantifying the costs of delivering specific imaging services. Effective product-level costing is dependent on a bottom-up approach, which is supported through recent innovations in time-dependent activity-based costing. Once the costs are understood, they can be managed. Within the high fixed cost and high overhead cost environment of health care provider organizations, stakeholders must understand the implications of misaligned top-down cost management approaches that can both paradoxically shift effort from low-cost workers to much costlier professionals and allocate overhead costs counterproductively. Radiology's engagement across a broad spectrum of care provides an excellent opportunity for radiology providers to take a leading role within the health care organizations to enhance value and margin through principled and effective cost management. Following a discussion of the rationale for measuring costs, this review contextualizes costs from the perspectives of a variety of stakeholders (relativity), discusses core concepts in how costs are classified (rudiments), presents common and improved methods for measuring costs in health care, and discusses how cost management strategies can either improve or hinder high-value health care (realities). ^© RSNA, 2017 Online supplemental material is available for this article.

Financial Forecasting and Stochastic Modeling: Predicting the Impact of Business Decisions

In health care organizations, effective investment of precious resources is critical to assure that the organization delivers high-quality and sustainable patient care within a supportive environment for patients, their families, and the health care providers. This holds true for organizations independent of size, from small practices to large health systems. For radiologists whose role is to oversee the delivery of imaging services and the interpretation, communication, and curation of imaging-informed information, business decisions influence where and how they practice, the tools available for image acquisition and interpretation, and ultimately their professional satisfaction. With so much at stake, physicians must understand and embrace the methods necessary to develop and interpret robust financial analyses so they effectively participate in and better understand decision making. This review discusses the financial drivers upon which health care organizations base investment decisions and the central role that stochastic financial modeling should play in support of strategically aligned capital investments. Given a health care industry that has been slow to embrace advanced financial analytics, a fundamental message of this review is that the skills and analytical tools are readily attainable and well worth the effort to implement in the interest of informed decision making. ^© RSNA, 2017 Online supplemental material is available for this article.

Guidelines for Management of Incidental Pulmonary Nodules Detected on CT Images: From the Fleischner Society 2017

The Fleischner Society Guidelines for management of solid nodules were published in 2005, and separate guidelines for subsolid nodules were issued in 2013. Since then, new information has become available; therefore, the guidelines have been revised to reflect current thinking on nodule management. The revised guidelines incorporate several substantive changes that reflect current thinking on the management of small nodules. The minimum threshold size for routine follow-up has been increased, and recommended follow-up intervals are now given as a range rather than as a precise time period to give radiologists, clinicians, and patients greater discretion to accommodate individual risk factors and preferences. The guidelines for solid and subsolid nodules have been combined in one simplified table, and specific recommendations have been included for multiple nodules. These guidelines represent the consensus of the Fleischner Society, and as such, they incorporate the opinions of a multidisciplinary international group of thoracic radiologists, pulmonologists, surgeons, pathologists, and other specialists. Changes from the previous guidelines issued by the Fleischner Society are based on new data and accumulated experience. ^© RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on March 13, 2017.

Enhancing Public Access to Relevant and Valued Medical Information: Fresh Directions for RadiologyInfo.org

RadiologyInfo.org is a public information portal designed to support patient care and broaden public awareness of the essential role radiology plays in overall patient health care. Over the past 14 years, RadiologyInfo.org has evolved considerably to provide access to more than 220 mixed-media descriptions of tests, treatments, and diseases through a spectrum of mobile and desktop platforms, social media, and downloadable documents in both English and Spanish. In 2014, the RSNA-ACR Public Information Website Committee, which stewards RadiologyInfo.org, developed 3- to 5-year strategic and implementation plans for the website. The process was informed by RadiologyInfo.org user surveys, formal stakeholder interviews, focus groups, and usability testing. Metrics were established as key performance indicators to assess progress toward the stated goals of (1) optimizing content to enhance patient-centeredness, (2) enhancing reach and engagement, and (3) maintaining sustainability. Major changes resulting from this process include a complete redesign of the website, the replacement of text-rich PowerPoint presentations with conversational videos, and the development of an affiliate network. Over the past year, visits to RadiologyInfo.org have increased by 60.27% to 1,424,523 in August 2016 from 235 countries and territories. Twenty-two organizations have affiliated with RadiologyInfo.org with new organizations being added on a monthly basis. RadiologyInfo provides a tangible demonstration of how radiologists can engage directly with the global public to educate them on the value of radiology in their health care and to allay concerns and dispel misconceptions. Regular self-assessment and responsive planning will ensure its continued growth and relevance.

Interstitial Lung Diseases

The term interstitial lung diseases (ILD) comprises a diverse group of diseases that lead to inflammation and fibrosis of the alveoli, distal airways, and septal interstitium of the lungs. The ILD consist of disorders of known cause (e.g., collagen vascular diseases, drug-related diseases) as well as disorders of unknown etiology. The latter include idiopathic interstitial pneumonias (IIPs), and a group of miscellaneous, rare, but nonetheless interesting, diseases. In patients with ILD, MDCT enriches the diagnostic armamentarium by allowing volumetric high-resolution scanning, i.e., continuous data acquisition with thin collimation and a high spatial frequency reconstruction algorithm. CT is a key method in the identification and management of patients with ILD. It not only improves the detection and characterization of parenchymal abnormalities, but also increases the accuracy of diagnosis. The spectrum of morphologic characteristics that are indicative of interstitial lung disease is relatively limited and includes the linear and reticular pattern, the nodular pattern, the increased attenuation pattern (such as ground-glass opacities and consolidation), and the low attenuation pattern (such as emphysema and cystic lung diseases). In the correct clinical context, some patterns or combination of patterns, together with the anatomic distribution of the abnormality, i.e., from the lung apex to the base, or peripheral subpleural versus central bronchovascular, can lead the interpreter to a specific diagnosis. However, due to an overlap of the CT morphology between the various entities, the final diagnosis of many ILD requires close cooperation between clinicians and radiologists and complementary lung biopsy is recommended in many cases.

CAD-RADS™: Coronary Artery Disease - Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology

The intent of CAD-RADS - Coronary Artery Disease Reporting and Data System is to create a standardized method to communicate findings of coronary CT angiography (coronary CTA) in order to facilitate decision-making regarding further patient management. The suggested CAD-RADS classification is applied on a per-patient basis and represents the highest-grade coronary artery lesion documented by coronary CTA. It ranges from CAD-RADS 0 (Zero) for the complete absence of stenosis and plaque to CAD-RADS 5 for the presence of at least one totally occluded coronary artery and should always be interpreted in conjunction with the impression found in the report. Specific recommendations are provided for further management of patients with stable or acute chest pain based on the CAD-RADS classification. The main goal of CAD-RADS is to standardize reporting of coronary CTA results and to facilitate communication of test results to referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will provide a framework of standardization that may benefit education, research, peer-review and quality assurance with the potential to ultimately result in improved quality of care.

Computed tomography: revolutionizing the practice of medicine for 40 years

Computed tomography (CT) has had a profound effect on the practice of medicine. Both the spectrum of clinical applications and the role that CT has played in enhancing the depth of our understanding of disease have been profound. Although almost 90 000 articles on CT have been published in peer-reviewed journals over the past 40 years, fewer than 5% of these have been published in Radiology. Nevertheless, these almost 4000 articles have provided a basis for many important medical advances. By enabling a deepened understanding of anatomy, physiology, and pathology, CT has facilitated key advances in the detection and management of disease. This article celebrates this breadth of scientific discovery and development by examining the impact that CT has had on the diagnosis, characterization, and management of a sampling of major health challenges, including stroke, vascular diseases, cancer, trauma, acute abdominal pain, and diffuse lung diseases, as related to key technical advances in CT and manifested in Radiology.

Characterizing search, recognition, and decision in the detection of lung nodules on CT scans: elucidation with eye tracking

PURPOSE

To determine the effectiveness of radiologists' search, recognition, and acceptance of lung nodules on computed tomographic (CT) images by using eye tracking.

MATERIALS AND METHODS

This study was performed with a protocol approved by the institutional review board. All study subjects provided informed consent, and all private health information was protected in accordance with HIPAA. A remote eye tracker was used to record time-varying gaze paths while 13 radiologists interpreted 40 lung CT images with an average of 3.9 synthetic nodules (5-mm diameter) embedded randomly in the lung parenchyma. The radiologists' gaze volumes ( GV gaze volume s) were defined as the portion of the lung parenchyma within 50 pixels (approximately 3 cm) of all gaze points. The fraction of the total lung volume encompassed within the GV gaze volume s, the fraction of lung nodules encompassed within each GV gaze volume (search effectiveness), the fraction of lung nodules within the GV gaze volume detected by the reader (recognition-acceptance effectiveness), and overall sensitivity of lung nodule detection were measured.

RESULTS

Detected nodules were within 50 pixels of the nearest gaze point for 990 of 992 correct detections. On average, radiologists searched 26.7% of the lung parenchyma in 3 minutes and 16 seconds and encompassed between 86 and 143 of 157 nodules within their GV gaze volume s. Once encompassed within their GV gaze volume , the average sensitivity of nodule recognition and acceptance ranged from 47 of 100 nodules to 103 of 124 nodules (sensitivity, 0.47-0.82). Overall sensitivity ranged from 47 to 114 of 157 nodules (sensitivity, 0.30-0.73) and showed moderate correlation (r = 0.62, P = .02) with the fraction of lung volume searched.

CONCLUSION

Relationships between reader search, recognition and acceptance, and overall lung nodule detection rate can be studied with eye tracking. Radiologists appear to actively search less than half of the lung parenchyma, with substantial interreader variation in volume searched, fraction of nodules included within the search volume, sensitivity for nodules within the search volume, and overall detection rate.

CT angiography after 20 years: a transformation in cardiovascular disease characterization continues to advance

Through a marriage of spiral computed tomography (CT) and graphical volumetric image processing, CT angiography was born 20 years ago. Fueled by a series of technical innovations in CT and image processing, over the next 5-15 years, CT angiography toppled conventional angiography, the undisputed diagnostic reference standard for vascular disease for the prior 70 years, as the preferred modality for the diagnosis and characterization of most cardiovascular abnormalities. This review recounts the evolution of CT angiography from its development and early challenges to a maturing modality that has provided unique insights into cardiovascular disease characterization and management. Selected clinical challenges, which include acute aortic syndromes, peripheral vascular disease, aortic stent-graft and transcatheter aortic valve assessment, and coronary artery disease, are presented as contrasting examples of how CT angiography is changing our approach to cardiovascular disease diagnosis and management. Finally, the recently introduced capabilities for multispectral imaging, tissue perfusion imaging, and radiation dose reduction through iterative reconstruction are explored with consideration toward the continued refinement and advancement of CT angiography.

Rapid detection of bone metastasis at thoracoabdominal CT: accuracy and efficiency of a new visualization algorithm

PURPOSE

To retrospectively assess the use of a combination of cancellous bone reconstructions (CBR) and multiplanar reconstructions (MPRs) for the detection of bone metastases at thoracoabdominal computed tomography (CT) compared with the use of MPRs alone.

MATERIALS AND METHODS

The study was approved by the local institutional review board. Included were 156 consecutive patients with confirmed cancer who underwent a whole-body positron emission tomography (PET)/CT examination for clinical purposes (93 male and 63 female patients; mean age ± standard deviation, 59.8 years ± 14.9; range, 11-85 years). Only the CT images were processed with the CBR algorithm, which segments the bones and removes the cortical layer from the images. The PET images served as part of the reference standard. Images from 15 patients were used as a training set. Four radiologists independently evaluated images of half of the remaining 141 patients by using CBRs and MPRs together, and the other half by using MPRs only. Radiologists were blinded to patient names, and patient order was randomized. Results for detection rates and reporting time were recorded and compared with a standard of reference for each patient that was created by one senior radiologist and one nuclear medicine specialist by using all available CT and PET data, CBRs, and follow-up examinations. General estimation equations were used for statistical analysis.

RESULTS

There were 349 lesions found in 103 patients, with 203 classified as malignant. Each patient was assessed by two readers per method, leading to a total of 698 lesions. The detection rate for all bone lesions was 35% (247 of 698) for MPRs and 74% (520 of 698) when CBRs and MPRs were used together, which was significantly higher (P < .001). The average reading time decreased from 85 to 43 seconds (P < .001) when both reconstructions were used.

CONCLUSION

Advanced visualization of cancellous bone significantly increased the detection of bone metastases and reduced the time for interpretation.