Understanding the Added Value of High-Resolution CT Beyond Chest X-ray in Determining Extent of Physiologic Impairment.

BACKGROUND: Sarcoidosis staging primarily has relied on the Scadding chest radiographic system, although chest CT imaging is finding increased clinical use. RESEARCH QUESTION: Whether standardized chest CT scan assessment provides additional understanding of lung function beyond Scadding stage and demographics is unknown and the focus of this study. STUDY DESIGN AND METHODS: We used National Heart, Lung, and Blood Institute study Genomics Research in Alpha-1 Antitrypsin Deficiency and Sarcoidosis (GRADS) cases of sarcoidosis (n = 351) with Scadding stage and chest CT scans obtained in a standardized manner. One chest radiologist scored all CT scans with a visual scoring system, with a subset read by another chest radiologist. We compared demographic features, Scadding stage and CT scan findings, and the correlation between these measures. Associations between spirometry and diffusing capacity of the lungs for carbon monoxide (Dlco) results and CT scan findings and Scadding stage were determined using regression analysis (n = 318). Agreement between readers was evaluated using Cohen's κ value. RESULTS: CT scan features were inconsistent with Scadding stage in approximately 40% of cases. Most CT scan features assessed on visual scoring were associated negatively with lung function. Associations persisted for FEV1 and Dlco when adjusting for Scadding stage, although some CT scan feature associations with FVC became insignificant. Scadding stage was associated primarily with FEV1, and inclusion of CT scan features reduced significance in association between Scadding stage and lung function. Multivariable regression modeling to identify radiologic measures explaining lung function included Scadding stage for FEV1 and FEV1 to FVC ratio (P < .05) and marginally for Dlco (P < .15). Combinations of CT scan measures accounted for Scadding stage for FVC. Correlations among Scadding stage and CT scan features were noted. Agreement between readers was poor to moderate for presence or absence of CT scan features and poor for degree and location of abnormality. INTERPRETATION: In this study, CT scan features explained additional variability in lung function beyond Scadding stage, with some CT scan features obviating the associations between lung function and Scadding stage. Whether CT scan features, phenotypes, or endotypes could be useful for treating patients with sarcoidosis needs more study.

Demographics and socioeconomic determinants of health predict continued participation in a CT lung cancer screening program.

PURPOSE: We developed machine learning (ML) models to assess demographic and socioeconomic status (SES) variables' value in predicting continued participation in a low-dose CT lung cancer screening (LCS) program. MATERIALS AND METHODS: 480 LCS subjects were retrospectively examined for the following outcomes: (#1) no follow-up (single LCS scan) vs. multiple follow-ups (220 and 260 subjects respectively) and (#2) absent or delayed (>1 month past the due date) follow-up vs timely follow-up (356 and 124 subjects respectively). We quantified the contributions of 14 socioeconomic, demographic, and clinical predictors to LCS adherence, and validated and compared prediction performances of multivariate logistic regression (MLR), support vector machine (SVM) and shallow neural network (NN) models. RESULTS: For outcome #1, age, sex, race, insurance status, personal cancer history, and median household income were found to be associated with returning for follow-ups. For outcome #2, age, sex, race, and insurance status were significant predictor of absent/delayed LCS follow-up. Across 5-fold cross-validation, the MLR model achieved an average AUC of 0.732 (95% CI, 0.661-0.803) for outcome #1 and 0.633 (95% CI, 0.602-0.664) for outcome #2 and is the model with best predictive performance overall, whereas NN and SVM tended to overfit training data and fell short on testing data performance for either outcome. CONCLUSIONS: We identified significant predictors of LCS adherence, and our ML models can predict which subjects are at higher risk of receiving no or delayed LCS follow-ups. Our results could inform data-driven interventions to engage vulnerable populations and extend the benefits of LCS.

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models.

OBJECTIVE: To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features. METHODS: We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors' contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance. RESULTS: On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840. CONCLUSION: Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

Prediction of Complication Risk in Computed Tomography-guided Thoracic Biopsy: A Prescription for Improving Procedure Safety.

PURPOSE: Computed tomography-guided transthoracic biopsy (CTTB) is a minimally invasive procedure with a high diagnostic yield for a variety of thoracic diseases. We comprehensively assessed a large CTTB cohort to predict procedural and patient factors associated with the risk of complications. MATERIALS AND METHODS: The medical record and computed tomography images of 1430 patients who underwent CTTB were reviewed individually to obtain clinical information and technical procedure factors. Statistical analyses included descriptive and summary statistics, univariate analysis with the Fisher test, and multivariate logistic regression. RESULTS: The most common type of complication was pneumothorax (17.4%), followed by bleeding (5.9%). Only 26 patients (1.8%) developed a major complication. Lung lesions carried a higher risk of complications than nonlung lesions. For lung lesions, the nondependent position of the lesion, vertical needle approach, trespassing aerated lung, and involvement of a trainee increased the risk of complication, whereas the use of the coaxial technique was a protective factor. The time with the needle in the lung, the number of biopsy samples, and the distance crossing the aerated lung were identified as additional risk factors in multivariate analysis. For nonlung lesions, trespassing the pleural space was the single best predictor of complications. A logistic regression-based model achieved an area under the receiver operating characteristic curve of 0.975, 0.699, and 0.722 for the prediction of major, minor, and no complications, respectively. CONCLUSIONS: Technical procedural factors that can be modified by the operator are highly predictive of the risk of complications in CTTB.

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.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

OBJECTIVES: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU). METHODS: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation. The models were tested on data from the other institution, and pairwise comparisons were used to determine the best-performing models. RESULTS: A higher proportion of deceased patients had elevated white blood cell count, decreased absolute lymphocyte count, elevated creatine concentration, and incidence of cardiovascular and chronic kidney disease. A model based on pre-ICU CXRs achieved an AUC of 0.632 and an accuracy of 0.593, and a model based on ICU CXRs achieved an AUC of 0.697 and an accuracy of 0.657. A model based on all longitudinal CXRs (both pre-ICU and ICU) achieved an AUC of 0.702 and an accuracy of 0.694. A model based on clinical data alone achieved an AUC of 0.653 and an accuracy of 0.657. The addition of longitudinal imaging to clinical data in a combined model significantly improved performance, reaching an AUC of 0.727 (p = 0.039) and an accuracy of 0.732. CONCLUSIONS: The addition of longitudinal CXRs to clinical data significantly improves mortality prediction with deep learning for COVID-19 patients in the ICU. KEY POINTS: • Deep learning was used to predict mortality in COVID-19 ICU patients. • Serial radiographs and clinical data were used. • The models could inform clinical decision-making and resource allocation.

Transparent Machine Learning Models to Diagnose Suspicious Thoracic Lesions Leveraging CT Guided Biopsy Data.

RATIONALE AND OBJECTIVES: To train and validate machine learning models capable of classifying suspicious thoracic lesions as benign or malignant and to further classify malignant lesions by pathologic subtype while quantifying feature importance for each classification. MATERIALS AND METHODS: 796 patients who had undergone CT guided thoracic biopsy for a concerning thoracic lesion (79.3% lung, 11.4% mediastinum, 6.5% pleura, 2.7% chest wall) were retrospectively enrolled. Lesions were classified as malignant or benign based on ground-truth pathology result, and malignant lesions were classified as primary or secondary cancer. Clinical variables were extracted from EMR and radiology reports. Supervised binary and multiclass classification models were trained to classify lesions based on the input features and evaluated on a held-out test set. Model specific feature analyses were performed to identify variables most predictive of each class, as well as to assess the independent importance of clinical, and imaging features. RESULTS: Binary classification models achieved a top accuracy of 80.6%, with predictive features included smoking history, age, lesion size, and lesion location. Multiclass classification models achieved a top weighted average f1-score of 0.73. Features predictive of primary cancer included smoking history, race, and age, while features predictive of secondary cancer included lesion location, and a history of cancer. CONCLUSION: Machine learning models enable classification of suspicious thoracic lesions based on clinical and imaging variables, achieving clinically useful performance while identifying importance of individual input features on a pathology-proven dataset. We believe models such as these are more likely to be trusted and adopted by clinicians.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

OBJECTIVES: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. METHODS: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning-based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. RESULTS: Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. CONCLUSIONS: Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. KEY POINTS: • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)-based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments.

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.

CT Fluoroscopy Guided Thoracic Biopsies (CTTB) Are Highly Accurate and Safe: Outcomes and Predictive Modeling of Complications Utilizing Machine Learning.

PURPOSE: CT guided transthoracic biopsy (CTTB) is an established, minimally invasive method for diagnostic evaluation of a variety of thoracic diseases. We assessed a large CTTB cohort diagnostic accuracy, complication rates, and developed machine learning models to predict complications. MATERIALS AND METHODS: We retrospectively identified 796 CTTB patients in a tertiary hospital (5-year interval). We gathered and coded patient demographics, characteristics of each lesion biopsied, type of biopsy, diagnostic yield, type of diagnosis, and complication rates. Statistical analyses included summary statistics, multivariate logistic regression and machine learning (neural network) methods. RESULTS: Seven hundred ninety-six CTTBs were performed (43% fine needle aspirations, 5% core biopsies, 52% both). Diagnostic yield was 97.0% (73.9% malignant, 23.1% benign). Complications occurred in 14.7% (12.7% minor, 2.0% major). The most common complication was pneumothorax (13.1%), mostly minor. Multivariate logistic regression models could predict severity of complications with accuracies ranging from 65.5% to 83.5%, with smaller lesion dimension the strongest predictor. Type of biopsy was not a statistically significant predictor. A neural network model improved accuracy to 77.0%-94.2%. CONCLUSION: CTTB performed by thoracic radiologists in a tertiary hospital demonstrate excellent diagnostic yield (97.0%) with a low clinically important complication rate (2.0%). Machine learning methods including neural networks can accurately predict the likelihood of complications, offering pathways to potentially improve patient selection and procedural technique, in order to further optimize the risk-benefit ratio of CTTB.

Racial/Ethnic Disparities in Follow-Up Adherence for Incidental Pulmonary Nodules: An Application of a Cascade-of-Care Framework.

PURPOSE: The aim of this study was to evaluate racial/ethnic disparities in follow-up adherence for incidental pulmonary nodules (IPNs) using a cascade-of-care framework, representing the multistage pathway from IPN diagnosis to timely follow-up adherence. METHODS: A cohort of 1,562 patients diagnosed with IPNs requiring follow-up in a tertiary health care system in 2016 were retrospectively identified. Racial/ethnic disparities in follow-up adherence were examined by developing a multistep cascade-of-care model (provider communication, follow-up examination ordering and scheduling, adherence) to identify where patients were most likely to fall off the path toward adherence. Racial/ethnic adherence disparities were measured using descriptive statistics and multivariate modeling, controlling for sociodemographic, communication, and health characteristics. RESULTS: Among 1,562 patients whose IPNs required follow-up, unadjusted results showed that nonwhite patients were less likely to meet each step on the cascade than White patients: for provider-patient IPN communication, 55% among Black patients and 80% among White patients; for follow-up ordering and scheduling, 42% and 41% among Black patients and 66% and 64% among White patients; and for timely adherence, 29% among Black patients and 54% among White patients. Adjusting for provider communication, sociodemographic, and health characteristics, Black patients had increased odds of never adhering to and delaying follow-up compared with White patients (odds ratios, 1.30 [95% confidence interval, 0.90-1.89] and 2.51 [95% confidence interval, 1.54-4.09], respectively). CONCLUSIONS: These findings demonstrate substantial racial/ethnic disparities in IPN follow-up adherence that persist after adjusting for multiple characteristics. The cascade of care demonstrates where on the adherence pathway patients are at risk for falling off, enabling specific targets for health policy and clinical interventions. Radiologists can play a key role in improving IPN follow-up via increased patient care involvement.

Statistical modeling can determine what factors are predictive of appropriate follow-up in patients presenting with incidental pulmonary nodules on CT.

PURPOSE: To assess the performance of statistical modeling in predicting follow-up adherence of incidentally detected pulmonary nodules (IPN) on CT, based on patient variables (PV), radiology report related variables (RRRV) and physician-patient communication variables (PPCV). METHODS: 200 patients with IPN on CT were retrospectively identified and randomly selected. PV (age, gender, smoking status, ethnicity), RRRV (nodule size, patient context, whether follow-up recommendations were provided) and PPCV (whether referring physician documented IPN and ordered follow-up on the electronic medical record) were recorded. Primary outcome was whether patients received appropriate follow-up within +/- 1 month of the recommended time frame. Statistical methods included logistic regression and machine learning (K-nearest neighbors and support vector machine). RESULTS: Adherence was low, with or without recommendations provided in the radiology report (23.4 %-27.4 %). Whether the referring physician ordered follow-up was the dominant predictor of adherence in all models. The following variables were statistically significant predictors of whether referring physician ordered follow-up: recommendations provided in the radiology report, smoking status, patient context and nodule size (FDR logworth of respectively 21.18, 11.66, 2.35, 1.63, p < 0.05). Prediction accuracy varied from 72 % (PV) to 93 % (PPCV, all variables). CONCLUSION: PPCV are the most important predictors of adherence. Amongst all variables, patient context, smoking status, nodule size, and whether the radiologist provided follow-up recommendations in the report were all statistically significant predictors of patient follow-up adherence, supporting the utility of statistical modeling for analytics, quality assurance and optimization of outcomes related to IPN.

Incidental findings in thoracic CTs performed in trauma patients: an underestimated problem.

OBJECTIVES: Whole-body CT scans are commonly performed to assess trauma patients, and often reveal incidental findings (IFs) the patient may be unaware of. We assessed the prevalence, associations, and adequacy of follow-up of IFs. METHODS: We retrospectively identified 1113 patients who had a chest CT to assess for traumatic injuries (6-year interval). We coded the radiology reports for IFs and queried our EMR regarding clinical history and adherence to follow-up recommendations for IFs mentioned in the reports. RESULTS: IFs are much more likely (62.2%) to be found in a chest CT scan than acute traumatic injuries (ATI, 32.4%), in patients being evaluated for potential traumatic injuries. A total of 86.4% of patients who had IFs also had another relevant ICD-10 diagnosis (RD). Lung nodules were the most common IF (45.7%). A multivariate logistic regression model (MLR) demonstrated an accuracy of 89% to predict IFs; the 3 statistically significant predictors (p < 0.05) were any RD (FDR logworth 68.6), followed by smoking history (29.8) and age (4.1). Radiologists recommended follow-up for IF 53.5% of the time, but only 13.9% of patients ever received a follow-up imaging exam or invasive procedure. CONCLUSIONS: IFs are much more common than ATI and can be accurately predicted based on MLR utilizing only 3 clinical variables. While radiologists often recommend follow-up for IFs in trauma patients, most are never effectively followed up or addressed, leading to increased risk of poor outcomes. Clinicians should be aware of the high prevalence of IFs and develop systems for appropriate, evidence-based recommendations, and effective management. KEY POINTS: • Incidental findings (IFs) are much more common (2×) than acute traumatic injuries (ATI) in chest CTs performed in trauma patients. • IFs can be accurately predicted via logistic regression modeling with only 3 variables (any relevant ICD-10 diagnosis; positive smoking history; age), which may help radiologist to focus their attention on higher risk patients. • Radiologists recommend follow-up for IFs more than half of the time; however, IFs are seldom followed up appropriately (less than 14%), leading to missed opportunities and potentially poor patient outcomes.

Methods for the Watch the Spot Trial. A Pragmatic Trial of More- versus Less-Intensive Strategies for Active Surveillance of Small Pulmonary Nodules.

Small pulmonary nodules are most often managed by surveillance imaging with computed tomography (CT) of the chest, but the optimal frequency and duration of surveillance are unknown. The Watch the Spot Trial is a multicenter, pragmatic, comparative-effectiveness trial with cluster randomization by hospital or health system that compares more- versus less-intensive strategies for active surveillance of small pulmonary nodules. The study plans to enroll approximately 35,200 patients with a small pulmonary nodule that is newly detected on chest CT imaging, either incidentally or by screening. Study protocols for more- and less-intensive surveillance were adapted from published guidelines. The primary outcome is the percentage of cancerous nodules that progress beyond American Joint Committee on Cancer seventh edition stage T1a. Secondary outcomes include patient-reported anxiety and emotional distress, nodule-related health care use, radiation exposure, and adherence with the assigned surveillance protocol. Distinctive aspects of the trial include: 1) the pragmatic integration of study procedures into existing clinical workflow; 2) the use of cluster randomization by hospital or health system; 3) the implementation and evaluation of a system-level intervention for protocol-based care; 4) the use of highly efficient, technology-enabled methods to identify and (passively) enroll participants; 5) reliance on data collected as part of routine clinical care, including data from electronic health records and state cancer registries; 6) linkage with state cancer registries for complete ascertainment of the primary study outcome; and 7) intensive engagement with a diverse group of patient and nonpatient stakeholders in the design and execution of the study.

Translation of Quantitative Imaging Biomarkers into Clinical Chest CT.

Quantitative imaging has been proposed as the next frontier in radiology as part of an effort to improve patient care through precision medicine. In 2007, the Radiological Society of North America launched the Quantitative Imaging Biomarkers Alliance (QIBA), an initiative aimed at improving the value and practicality of quantitative imaging biomarkers by reducing variability across devices, sites, patients, and time. Chest CT occupies a strategic position in this initiative because it is one of the most frequently used imaging modalities, anatomically encompassing the leading causes of mortality worldwide. To date, QIBA has worked on profiles focused on the accurate, reproducible, and meaningful use of volumetric measurements of lung lesions in chest CT. However, other quantitative methods are on the verge of translation from research grounds into clinical practice, including (a) assessment of parenchymal and airway changes in patients with chronic obstructive pulmonary disease, (b) analysis of perfusion with dual-energy CT biomarkers, and (c) opportunistic screening for coronary atherosclerosis and low bone mass by using chest CT examinations performed for other indications. The rationale for and the key facts related to the application of these quantitative imaging biomarkers in cardiothoracic chest CT are presented. ©RSNA, 2019 See discussion on this article by Buckler (pp 977-980).

Imaging Evaluation of Lung Transplantation Patients: A Time and Etiology-based Approach to High-resolution Computed Tomography Interpretation.

Lung transplantation is an established therapeutic option for patients with irreversible end-stage pulmonary disease limiting life expectancy and quality of life. Common indications for lung transplantation include chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, cystic fibrosis, pulmonary arterial hypertension, and alpha-1 antitrypsin deficiency. Complications of lung transplantation can be broadly divided etiologically into surgical, infectious, immunologic, or neoplastic. Moreover, specific complications often occur within a certain time interval following surgery, which can be broadly classified as early (<6 wk), intermediate (6 wk to 6 mo), and late (>6 mo). Thus, each group of complications can further be categorized on the basis of the time continuum from transplantation. Imaging, primarily by high-resolution computed tomography, plays a critical role in early diagnosis of complications after lung transplantation. Early recognition of complications by the radiologist, and initiation of therapy, contributes to improved morbidity and mortality. However, accurate diagnosis is only feasible if one has a thorough understanding of the major etiologic categories of complications and how they relate to the time course since transplantation. We review imaging manifestations of lung transplant complications via a framework that includes the following major etiologic categories: surgical; infectious; immunologic; and neoplastic; and the following time frames: surgery to 6 weeks; 6 weeks to 6 months; and beyond 6 months. We propose this approach as a logical, evidence-based algorithm to construct a narrow, optimal differential diagnosis of lung transplantation complications.

The Value of Real-Time Thoracic Radiology Consulting in an Integrated Lung Center Clinic: Bringing the Radiologist to the Center of Multidisciplinary Health Care.

PURPOSE: We have established an integrated thoracic radiology reading room within a multidisciplinary lung center clinic (LC). While our subjective experience has been positive, we sought to quantify how this model affects radiology workflow and whether the referring practitioners perceive value in having real-time access to a radiologist consultant. MATERIALS AND METHODS: Two diagnostic radiology workstations staffed by rotating thoracic radiologists and trainees were integrated within the LC. We assessed the impact on workflow by recording over 6 months the number, duration, and type of face-to-face radiology consultations to LC practitioners. We also conducted an anonymous survey to assess how LC practitioners felt with regard to the utility and value of our service. RESULTS: Face-to-face consultations account for an average of 10% of total time spent by radiologists in the LC, although on busy clinical days this can reach 25% to 30%. Our survey response rate was very high (86.4%, n=51), with overwhelming positive response by referring practitioners, who unanimously rate the usefulness of this service as high (9.8%) or extremely high (90.2%). Not a single respondent had a negative or even neutral view of this service. Moreover, 90.2% thought that radiology consultations directly add clinical value in >60% of episodes, whereas 86.2% responded that these alter management in >40% of episodes. CONCLUSIONS: Face-to-face radiology consultations in an integrated LC are numerous and comprise a sizable share of radiologist workload. More importantly, the radiologist is highly praised as a consultant, and this service is considered valuable and impactful for patient care.

Quantitative Computed Tomography Metrics From the Transplanted Lung can Predict Forced Expiratory Volume in the First Second After Lung Transplantation.

PURPOSE: Bronchiolitis obliterans syndrome after lung transplantation (LTx) manifests as a sustained decline in forced expiratory volume in the first second (FEV1). Quantitative computed tomography (QCT) metrics may predict FEV1 better than semiquantitative scores (SQSs), and the transplanted lung may provide better information than the native lung in unilateral LTx. MATERIALS AND METHODS: Paired inspiratory-expiratory CT scans and pulmonary function testing of 178 LTx patients were analyzed retrospectively. SQS were graded (absent, mild, moderate, severe) for features including mosaic attenuation and bronchiectasis. QCT included lung volumes and air-trapping volumes, by lobe. Multivariate Pearson correlation and multivariate linear least squares regression analyses were performed. RESULTS: Multivariate linear least squares regression models using FEV1 as the outcome variable and SQS or QCT metrics as predictor variables demonstrated SQS to be a weak predictor of FEV1 (adjusted R, 0.114). QCT metrics were much stronger predictors of FEV1 (adjusted R, 0.654). QCT metrics demonstrated stronger correlation (r) with FEV1 than SQS. In bilateral LTx, whole lung volume difference (r=0.69), left lung volume difference (r=0.69), and right lung volume difference (r=0.65) were better than the sum of SQS (r=-0.54). Interestingly, in left LTx we obtained r=0.81, 0.86, 0.25, and -0.39, respectively. In right LTx, we obtained r=0.69, 0.49, 0.68, and -0.31, respectively. CONCLUSIONS: QCT metrics demonstrate stronger correlations with FEV1 and are better predictors of pulmonary function than SQS. SQS performs moderately well in bilateral LTx, but poorly on unilateral LTx. In unilateral LTx, QCT metrics from the transplanted lung are better predictors of FEV1 than QCT metrics from the nontransplanted lung.

Pulmonary Tuberculosis: Role of Radiology in Diagnosis and Management.

Tuberculosis is a public health problem worldwide, including in the United States-particularly among immunocompromised patients and other high-risk groups. Tuberculosis manifests in active and latent forms. Active disease can occur as primary tuberculosis, developing shortly after infection, or postprimary tuberculosis, developing after a long period of latent infection. Primary tuberculosis occurs most commonly in children and immunocompromised patients, who present with lymphadenopathy, pulmonary consolidation, and pleural effusion. Postprimary tuberculosis may manifest with cavities, consolidations, and centrilobular nodules. Miliary tuberculosis refers to hematogenously disseminated disease that is more commonly seen in immunocompromised patients, who present with miliary lung nodules and multiorgan involvement. The principal means of testing for active tuberculosis is sputum analysis, including smear, culture, and nucleic acid amplification testing. Imaging findings, particularly the presence of cavitation, can affect treatment decisions, such as the duration of therapy. Latent tuberculosis is an asymptomatic infection that can lead to postprimary tuberculosis in the future. Patients who are suspected of having latent tuberculosis may undergo targeted testing with a tuberculin skin test or interferon-γ release assay. Chest radiographs are used to stratify for risk and to assess for asymptomatic active disease. Sequelae of previous tuberculosis that is now inactive manifest characteristically as fibronodular opacities in the apical and upper lung zones. Stability of radiographic findings for 6 months distinguishes inactive from active disease. Nontuberculous mycobacterial disease can sometimes mimic the findings of active tuberculosis, and laboratory confirmation is required to make the distinction. Familiarity with the imaging, clinical, and laboratory features of tuberculosis is important for diagnosis and management. ©RSNA, 2017.

Predictive factors for malignancy in incidental pulmonary nodules detected in breast cancer patients at baseline CT.

OBJECTIVES: Pulmonary nodules are commonly encountered at staging CTs in patients with extrathoracic malignancies, but their significance on a per-patient basis remains uncertain. METHODS: We undertook a retrospective analysis of pulmonary nodules identified in patients with a diagnosis of breast cancer from 2010 - 2015, evaluating nodules present at a baseline CT (i.e. prevalent nodules). We reviewed 211 patients with 248 individual nodules. RESULTS: The rate of malignancy in prevalent nodules is low, approximately 13 %. Variables associated with metastasis include pleural studding, hilar lymphadenopathy and the presence of extrapulmonary metastasis, as well as number of nodules, nodule size and nodule shape. Using a combination of these factors, we have developed an evidence-based multivariate decision tree to predict which nodules are malignant in these patients, which is 91 % accurate and 100 % sensitive for metastasis. CONCLUSIONS: We propose a simplified clinical prediction algorithm to guide radiologists and oncologists in managing patients with breast cancer and incidental pulmonary nodules. KEY POINTS: • Incidental pulmonary nodules are common on computed tomography in breast cancer patients. • Nodules present at baseline have a lower malignancy risk than incident nodules. • We present an evidence-based decision algorithm predicting which nodules are likely malignant. • This algorithm can help direct patient management.