Commonly Missed Findings on Chest Radiographs: Causes and Consequences.

Chest radiography (CXR) continues to be the most frequently performed imaging examination worldwide, yet it remains prone to frequent errors in interpretation. These pose potential adverse consequences to patients and are a leading motivation for medical malpractice lawsuits. Commonly missed CXR findings and the principal causes of these errors are reviewed and illustrated. Perceptual errors are the predominant source of these missed findings. The medicolegal implications of such errors are explained. Awareness of commonly missed CXR findings, their causes, and their consequences are important in developing approaches to reduce and mitigate these errors.

Reducing Errors Resulting From Commonly Missed Chest Radiography Findings.

Chest radiography (CXR), the most frequently performed imaging examination, is vulnerable to interpretation errors resulting from commonly missed findings. Methods to reduce these errors are presented. A practical approach using a systematic and comprehensive visual search strategy is described. The use of a checklist for quality control in the interpretation of CXR images is proposed to avoid overlooking commonly missed findings of clinical importance. Artificial intelligence is among the emerging and promising methods to enhance detection of CXR abnormalities. Despite their potential adverse consequences, errors offer opportunities for continued education and quality improvements in patient care, if managed within a just, supportive culture.

Value of quantitative airspace disease measured on chest CT and chest radiography at initial diagnosis compared to clinical variables for prediction of severe COVID-19.

Purpose: Rapid prognostication of COVID-19 patients is important for efficient resource allocation. We evaluated the relative prognostic value of baseline clinical variables (CVs), quantitative human-read chest CT (qCT), and AI-read chest radiograph (qCXR) airspace disease (AD) in predicting severe COVID-19. Approach: We retrospectively selected 131 COVID-19 patients (SARS-CoV-2 positive, March to October, 2020) at a tertiary hospital in the United States, who underwent chest CT and CXR within 48 hr of initial presentation. CVs included patient demographics and laboratory values; imaging variables included qCT volumetric percentage AD (POv) and qCXR area-based percentage AD (POa), assessed by a deep convolutional neural network. Our prognostic outcome was need for ICU admission. We compared the performance of three logistic regression models: using CVs known to be associated with prognosis (model I), using a dimension-reduced set of best predictor variables (model II), and using only age and AD (model III). Results: 60/131 patients required ICU admission, whereas 71/131 did not. Model I performed the poorest ( AUC = 0.67 [0.58 to 0.76]; accuracy = 77 % ). Model II performed the best ( AUC = 0.78 [0.71 to 0.86]; accuracy = 81 % ). Model III was equivalent ( AUC = 0.75 [0.67 to 0.84]; accuracy = 80 % ). Both models II and III outperformed model I ( AUC difference = 0.11 [0.02 to 0.19], p = 0.01 ; AUC difference = 0.08 [0.01 to 0.15], p = 0.04 , respectively). Model II and III results did not change significantly when POv was replaced by POa. Conclusions: Severe COVID-19 can be predicted using only age and quantitative AD imaging metrics at initial diagnosis, which outperform the set of CVs. Moreover, AI-read qCXR can replace qCT metrics without loss of prognostic performance, promising more resource-efficient prognostication.

Pulmonary Functional Imaging: Part 1-State-of-the-Art Technical and Physiologic Underpinnings.

Over the past few decades, pulmonary imaging technologies have advanced from chest radiography and nuclear medicine methods to high-spatial-resolution or low-dose chest CT and MRI. It is currently possible to identify and measure pulmonary pathologic changes before these are obvious even to patients or depicted on conventional morphologic images. Here, key technological advances are described, including multiparametric CT image processing methods, inhaled hyperpolarized and fluorinated gas MRI, and four-dimensional free-breathing CT and MRI methods to measure regional ventilation, perfusion, gas exchange, and biomechanics. The basic anatomic and physiologic underpinnings of these pulmonary functional imaging techniques are explained. In addition, advances in image analysis and computational and artificial intelligence (machine learning) methods pertinent to functional lung imaging are discussed. The clinical applications of pulmonary functional imaging, including both the opportunities and challenges for clinical translation and deployment, will be discussed in part 2 of this review. Given the technical advances in these sophisticated imaging methods and the wealth of information they can provide, it is anticipated that pulmonary functional imaging will be increasingly used in the care of patients with lung disease. © RSNA, 2021 Online supplemental material is available for this article.

Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care.

Pulmonary functional imaging may be defined as the regional quantification of lung function by using primarily CT, MRI, and nuclear medicine techniques. The distribution of pulmonary physiologic parameters, including ventilation, perfusion, gas exchange, and biomechanics, can be noninvasively mapped and measured throughout the lungs. This information is not accessible by using conventional pulmonary function tests, which measure total lung function without viewing the regional distribution. The latter is important because of the heterogeneous distribution of virtually all lung disorders. Moreover, techniques such as hyperpolarized xenon 129 and helium 3 MRI can probe lung physiologic structure and microstructure at the level of the alveolar-air and alveolar-red blood cell interface, which is well beyond the spatial resolution of other clinical methods. The opportunities, challenges, and current stage of clinical deployment of pulmonary functional imaging are reviewed, including applications to chronic obstructive pulmonary disease, asthma, interstitial lung disease, pulmonary embolism, and pulmonary hypertension. Among the challenges to the deployment of pulmonary functional imaging in routine clinical practice are the need for further validation, establishment of normal values, standardization of imaging acquisition and analysis, and evidence of patient outcomes benefit. When these challenges are addressed, it is anticipated that pulmonary functional imaging will have an expanding role in the evaluation and management of patients with lung disease.

Automated Detection and Quantification of COVID-19 Airspace Disease on Chest Radiographs: A Novel Approach Achieving Expert Radiologist-Level Performance Using a Deep Convolutional Neural Network Trained on Digital Reconstructed Radiographs From Computed Tomography-Derived Ground Truth.

OBJECTIVES: The aim of this study was to leverage volumetric quantification of airspace disease (AD) derived from a superior modality (computed tomography [CT]) serving as ground truth, projected onto digitally reconstructed radiographs (DRRs) to (1) train a convolutional neural network (CNN) to quantify AD on paired chest radiographs (CXRs) and CTs, and (2) compare the DRR-trained CNN to expert human readers in the CXR evaluation of patients with confirmed COVID-19. MATERIALS AND METHODS: We retrospectively selected a cohort of 86 COVID-19 patients (with positive reverse transcriptase-polymerase chain reaction test results) from March to May 2020 at a tertiary hospital in the northeastern United States, who underwent chest CT and CXR within 48 hours. The ground-truth volumetric percentage of COVID-19-related AD (POv) was established by manual AD segmentation on CT. The resulting 3-dimensional masks were projected into 2-dimensional anterior-posterior DRR to compute area-based AD percentage (POa). A CNN was trained with DRR images generated from a larger-scale CT dataset of COVID-19 and non-COVID-19 patients, automatically segmenting lungs, AD, and quantifying POa on CXR. The CNN POa results were compared with POa quantified on CXR by 2 expert readers and to the POv ground truth, by computing correlations and mean absolute errors. RESULTS: Bootstrap mean absolute error and correlations between POa and POv were 11.98% (11.05%-12.47%) and 0.77 (0.70-0.82) for average of expert readers and 9.56% to 9.78% (8.83%-10.22%) and 0.78 to 0.81 (0.73-0.85) for the CNN, respectively. CONCLUSIONS: Our CNN trained with DRR using CT-derived airspace quantification achieved expert radiologist level of accuracy in the quantification of AD on CXR in patients with positive reverse transcriptase-polymerase chain reaction test results for COVID-19.

Preliminary Assessment of an Optical Flow Method (OFM) for Nonrigid Registration and Temporal Subtraction (TS) of Serial CT Examinations to Facilitate Evaluation of Interval Change in Metastatic Lung Nodules.

RATIONALE AND OBJECTIVES: Accurate assessment of size change of lung nodules on chest computed tomography (CT) is important for diagnosis and response assessment. However, manual methods are time-consuming and error-prone. We therefore assessed whether an optical flow method (OFM) with temporal subtraction (TS) can facilitate detection and quantification of lung nodule change on serial CT datasets. MATERIALS AND METHODS: Serial chest CT examinations were selected from 12 patients with multiple pulmonary metastases. Lung nodules were evaluated for change in size using: (1) OFM with TS and (2) reference standard visual and manual assessment. Average time required to assess interval change using both methods was recorded and compared. Concordance of agreement between OFM with TS and reference standard assessment for nodule change was examined. RESULTS: 285 solid pulmonary nodules were evaluated. The average time per nodule to assess interval change in nodule size by OFM with TS (mean 1.15 + 0.5 minutes) was significantly less (P = 0.02) than that the reference standard approach (mean 1.56 + 0.5 minutes). Agreement between OFM with TS and reference standard occurred for 63.2% of nodules overall (kappa = 0.50, standard error 0.35, P< 0.00001), and significantly increased with larger nodule size (kappa = 0.48 for nodules <5 mm; kappa = 0.94 for nodules >20 mm, P < 0.0001). CONCLUSIONS: This preliminary study demonstrates the feasibility of an OFM with TS to assess for interval change in metastatic lung nodules on serial CT examinations with significantly improved reading speed and moderate agreement relative to reference standard assessment. Agreement improved with larger nodule size.

Expanding Applications of Pulmonary MRI in the Clinical Evaluation of Lung Disorders: Fleischner Society Position Paper.

Pulmonary MRI provides structural and quantitative functional images of the lungs without ionizing radiation, but it has had limited clinical use due to low signal intensity from the lung parenchyma. The lack of radiation makes pulmonary MRI an ideal modality for pediatric examinations, pregnant women, and patients requiring serial and longitudinal follow-up. Fortunately, recent MRI techniques, including ultrashort echo time and zero echo time, are expanding clinical opportunities for pulmonary MRI. With the use of multicoil parallel acquisitions and acceleration methods, these techniques make pulmonary MRI practical for evaluating lung parenchymal and pulmonary vascular diseases. The purpose of this Fleischner Society position paper is to familiarize radiologists and other interested clinicians with these advances in pulmonary MRI and to stratify the Society recommendations for the clinical use of pulmonary MRI into three categories: (a) suggested for current clinical use, (b) promising but requiring further validation or regulatory approval, and (c) appropriate for research investigations. This position paper also provides recommendations for vendors and infrastructure, identifies methods for hypothesis-driven research, and suggests opportunities for prospective, randomized multicenter trials to investigate and validate lung MRI methods.

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction using numerous processing layers. A distinctive feature of deep learning, compared with conventional machine learning methods, is that it can generate appropriate models for tasks directly from the raw data, removing the need for human-led feature extraction. Medical images are particularly suited for deep learning applications. Deep learning techniques have already demonstrated high performance in the detection of diabetic retinopathy on fundoscopic images and metastatic breast cancer cells on pathologic images. In radiology, deep learning has the opportunity to provide improved accuracy of image interpretation and diagnosis. Many groups are exploring the possibility of using deep learning-based applications to solve unmet clinical needs. In chest imaging, there has been a large effort to develop and apply computer-aided detection systems for the detection of lung nodules on chest radiographs and chest computed tomography. The essential limitation to computer-aided detection is an inability to learn from new information. To overcome these deficiencies, many groups have turned to deep learning approaches with promising results. In addition to nodule detection, interstitial lung disease recognition, lesion segmentation, diagnosis and patient outcomes have been addressed by deep learning approaches. The purpose of this review article was to cover the current state of the art for deep learning approaches and its limitations, and some of the potential impact on the field of radiology, with specific reference to chest imaging.

The value of delayed phase enhanced imaging in malignant pleural mesothelioma.

BACKGROUND: Cross-sectional imaging of malignant pleural mesothelioma (MPM) can underestimate the presence of local tumor invasion. Since accurate staging is vital optimal choice of therapy, techniques that optimize pleural imaging are needed. Here we estimate the optimal timing of MPM enhancement on magnetic resonance imaging (MRI). METHODS: All MPM patients with intravenous (IV) contrast enhanced staging MRI between 2000-2016 at our institution were retrospectively selected for image analysis. Patients with incomplete imaging protocol and maximum pleural tumor thickness <1 cm were excluded. Quantitative measurements of tumor signal intensity were obtained on pre-contrast and post-contrast phases where MRI acquisition parameters were fixed. Using best-fit model curves, predicted maximum time points of enhancement were determined using a simulation of predicted values. Additionally, a qualitative assessment of tumor conspicuity was performed at all IV contrast time delays imaged. A statistical analysis assessed for correlation between qualitative lesion conspicuity and quantitative tumor enhancement. RESULTS: Of the 42 MPM patients who had undergone staging MRI during the study period, 12 patients met the study criteria. Peak tumor enhancement was between 150 and 300 sec following IV contrast administration. Within this time window, 80% of patients are projected to have reached >80%, >85%, and >90% peak tumor enhancement. There was a statistically significant correlation between increasing tumor enhancement and subjective lesion conspicuity. CONCLUSIONS: Optimal MPM enhancement on MRI likely occurs at a time delay between 2.5-5 min following IV contrast administration. Further study of delayed phase enhancement of MPM with dynamic contrast enhanced MRI is warranted.

Improving Abnormality Detection on Chest Radiography Using Game-Like Reinforcement Mechanics.

RATIONALE AND OBJECTIVES: Despite their increasing prevalence, online textbooks, question banks, and digital references focus primarily on explicit knowledge. Implicit skills such as abnormality detection require repeated practice on clinical service and have few digital substitutes. Using mechanics traditionally deployed in video games such as clearly defined goals, rapid-fire levels, and narrow time constraints may be an effective way to teach implicit skills. MATERIALS AND METHODS: We created a freely available, online module to evaluate the ability of individuals to differentiate between normal and abnormal chest radiographs by implementing mechanics, including instantaneous feedback, rapid-fire cases, and 15-second timers. Volunteer subjects completed the modules and were separated based on formal experience with chest radiography. Performance between training and testing sets were measured for each group, and a survey was administered after each session. RESULTS: The module contained 74 cases and took approximately 20 minutes to complete. Thirty-two cases were normal radiographs and 56 cases were abnormal. Of the 60 volunteers recruited, 25 were "never trained" and 35 were "previously trained." "Never trained" users scored 21.9 out of 37 during training and 24.0 out of 37 during testing (59.1% vs 64.9%, P value <.001). "Previously trained" users scored 28.0 out of 37 during training and 28.3 out of 37 during testing phases (75.6% vs 76.4%, P value = .56). Survey results showed that 87% of all subjects agreed the module is an efficient way of learning, and 83% agreed the rapid-fire module is valuable for medical students. CONCLUSIONS: A gamified online module may improve the abnormality detection rates of novice interpreters of chest radiography, although experienced interpreters are less likely to derive similar benefits. Users reviewed the educational module favorably.

Regional Fractional Ventilation by Using Multibreath Wash-in (3)He MR Imaging.

Purpose To assess the feasibility and optimize the accuracy of the multibreath wash-in hyperpolarized helium 3 ((3)He) approach to ventilation measurement by using magnetic resonance (MR) imaging as well as to examine the physiologic differences that this approach reveals among nonsmokers, asymptomatic smokers, and patients with chronic obstructive pulmonary disease (COPD). Materials and Methods All experiments were approved by the local institutional review board and compliant with HIPAA. Informed consent was obtained from all subjects. To measure fractional ventilation, the authors administered a series of identical normoxic hyperpolarized gas breaths to the subject; after each inspiration, an image was acquired during a short breath hold. Signal intensity buildup was fit to a recursive model that regionally solves for fractional ventilation. This measurement was successfully performed in nine subjects: three healthy nonsmokers (one man, two women; mean age, 45 years ± 4), three asymptomatic smokers (three men; mean age, 51 years ± 5), and three patients with COPD (three men; mean age, 59 years ± 5). Repeated measures analysis of variance was performed, followed by post hoc tests with Bonferroni correction, to assess the differences among the three cohorts. Results Whole-lung fractional ventilation as measured with hyperpolarized (3)He in all subjects (mean, 0.24 ± 0.06) showed a strong correlation with global fractional ventilation as measured with a gas delivery device (R(2) = 0.96, P < .001). Significant differences between the means of whole-lung fractional ventilation (F2,10 = 7.144, P = .012) and fractional ventilation heterogeneity (F2,10 = 7.639, P = .010) were detected among cohorts. In patients with COPD, the protocol revealed regions wherein fractional ventilation varied substantially over multiple breaths. Conclusion Multibreath wash-in hyperpolarized (3)He MR imaging of fractional ventilation is feasible in human subjects and demonstrates very good global (whole-lung) precision. Fractional ventilation measurement with this physiologically realistic approach reveals significant differences between patients with COPD and healthy subjects. To minimize error, several sources of potential bias must be corrected when calculating fractional ventilation. (©) RSNA, 2016 Online supplemental material is available for this article.

Optimization of Radiology Reports for Intensive Care Unit Portable Chest Radiographs: Perceptions and Preferences of Radiologists and ICU Practitioners.

PURPOSE: The aim of the study was to evaluate opinions and perceptions of radiologists and referring practitioners regarding reports of portable chest radiography (pCXR) obtained in the intensive care unit (ICU). MATERIALS AND METHODS: A total of 1265 referring practitioners and 76 radiologists were invited to participate in 2 internet-based surveys, containing 15 and 17 multiple choice questions, respectively, similarly presented to both groups, utilizing a Likert scale or multiple choices. Results were compared using the Fisher exact test or χ test. RESULTS: One hundred ninety-two referring practitioners and 63 radiologists answered the surveys, resulting in response rates of 15% and 83%. The majority of radiologists and referring practitioners are satisfied with the quality of the reports; however, radiologists and referring practitioners disagree about the reports' clinical value and impact, the referring practitioners having a more positive view. Both groups overwhelmingly agree that pertinent clinical information is crucial for optimal image interpretation. The 2 groups differ in their preferences regarding report style and information content, with radiologists strongly supporting concise reports emphasizing temporal changes and major findings, whereas referring practitioners prefer more complete, itemized structured reports describing support devices in detail. CONCLUSIONS: The results substantiate the perceived clinical value of radiologist reports for pCXR, from the perspective of referring practitioners. Nonetheless, there is disagreement regarding report structure and content. Several issues were raised, offering opportunities for improvement, which may increase referring practitioners' satisfaction and positively impact patient outcomes. Any strategy to implement standardized structured reports for pCXR will have to satisfy referring practitioners' needs while optimizing radiologists' efficiency, will have to be widely accepted, and will have to fulfill the overarching goal of maximizing the value of pCXR reports.

Oxygen-weighted Hyperpolarized (3)He MR Imaging: A Short-term Reproducibility Study in Human Subjects.

PURPOSE: To determine whether hyperpolarized helium 3 magnetic resonance (MR) imaging to measure alveolar partial pressure of oxygen (Pao2) shows sufficient test-retest repeatability and between-cohort differences to be used as a reliable technique for detection of alterations in gas exchange in asymptomatic smokers. MATERIALS AND METHODS: The protocol was approved by the local institutional review board and was HIPAA compliant. Informed consent was obtained from all subjects. Two sets of MR images were obtained 10 minutes apart in 25 subjects: 10 nonsmokers (five men, five women; mean ± standard deviation age, 50 years ± 6) and 15 smokers (seven women, eight men; mean age, 50 years ± 8). A mixed-effects model was developed to identify the regional repeatability of Pao2 measurements as an intraclass correlation coefficient. Ten smokers were matched with the 10 nonsmokers on the basis of signal-to-noise ratio (SNR). Three separate models were generated: one for nonsmokers, one for the SNR-matched smokers, and one for the five remaining smokers, who were imaged with a significantly higher SNR. RESULTS: Short-term back-to-back regional reproducibility was assessed by using intraclass correlation coefficients, which were 0.67 and 0.65 for SNR case-matched nonsmokers and smokers, respectively. Repeatability was a strong function of SNR; a 50% increase in SNR in the remaining smokers improved the intraclass correlation coefficient to 0.82. Although repeatability was not significantly different between the SNR-matched cohorts (P = .44), the smoker group showed higher spatial and temporal variability in Pao2. CONCLUSION: The short-term test-retest repeatability of hyperpolarized gas MR imaging of regional Pao2 was good. Asymptomatic smokers exhibited greater spatial and temporal variability in Pao2 than did the nonsmokers, which suggests that this parameter allows detection of small functional alterations associated with smoking.

Triage for suspected acute Pulmonary Embolism: Think before opening Pandora's Box.

This is a review of the current strengths and weaknesses of the various imaging modalities available for the diagnosis of suspected non-massive Pulmonary Embolism (PE). Without careful consideration for the clinical presentation, and the timely application of clinical decision support (CDS) methodology, the current overutilization of imaging resources for this disease will continue. For a patient with a low clinical risk profile and a negative D-dimer there is no reason to consider further workup with imaging; as the negative predictive value in this scenario is the same as imaging. While the current efficacy and effectiveness data support the continued use of Computed Tomographic angiography (CTA) as the imaging golden standard for the diagnosis of PE; this test does have the unintended consequences of radiation exposure, possible overdiagnosis and overuse. There is a persistent lack of appreciation on the part of ordering physicians for the effectiveness of the alternatives to CTA (ventilation-perfusion imaging and contrast enhanced magnetic resonance angiography) in these patients. Careful use of standardized protocols for patient triage and the application of CDS will allow for a better use of imaging resources.

Alterations of regional alveolar oxygen tension in asymptomatic current smokers: assessment with hyperpolarized (3)He MR imaging.

PURPOSE: To assess the ability of helium 3 ((3)He) magnetic resonance (MR) imaging of regional alveolar partial pressure of oxygen (Pao2) to depict smoking-induced functional alterations and to compare its efficacy to that of current diagnostic techniques. MATERIALS AND METHODS: This study was approved by the local institutional review board and was compliant with HIPAA. All subjects provided informed consent. A total of 43 subjects were separated into three groups: nonsmokers, asymptomatic smokers, and symptomatic smokers. All subjects underwent a Pao2 imaging session followed by clinically standard pulmonary function tests (PFTs), the 6-minute walk test, and St George Respiratory Questionnaire (SGRQ). The whole-lung mean and standard deviation of Pao2 were compared with metrics derived from PFTs, the 6-minute walk test, and the SGRQ. A logistic regression model was developed to identify the predictors of alterations to the lungs of asymptomatic smokers. RESULTS: The whole-lung standard deviation of Pao2 correlated with PFT metrics (forced expiratory volume in 1 second [FEV1]/forced vital capacity [FVC], Pearson r = -0.69, P < .001; percentage predicted FEV1, Pearson r = -0.67, P < .001; diffusing capacity of lung for carbon monoxide [Dlco], Pearson r = -0.45, P = .003), SGRQ score (Pearson r = 0.67, P < .001), and distance walked in 6 minutes (Pearson r = -0.47, P = .002). The standard deviation of Pao2 was significantly higher in asymptomatic smokers than in nonsmokers (change in the standard deviation of Pao2 = 7.59 mm Hg, P = .041) and lower when compared with symptomatic smokers (change in the standard deviation of Pao2 = 10.72 mm Hg, P = .001). A multivariate prediction model containing FEV1/FVC and the standard deviation of Pao2 (as significant predictors of subclinical changes in smokers) and Dlco (as a confounding variable) was formulated. This model resulted in an area under the receiver operating characteristic curve with a significant increase of 29.2% when compared with a prediction model based solely on nonimaging clinical tests. CONCLUSION: The (3)He MR imaging heterogeneity metric (standard deviation of Pao2) enabled the differentiation of all three study cohorts, which indicates that it can depict smoking-related functional alterations in asymptomatic current smokers.