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.

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).

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.

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.

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.

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. 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.

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.

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.

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.

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.

Real-World Lung Cancer CT Screening Performance, Smoking Behavior, and Adherence to Recommendations: Lung-RADS Category and Smoking Status Predict Adherence.

Low-dose CT (LDCT) lung cancer screening (LCS) has been shown to decrease mortality in persons with a significant smoking history. However, adherence in real-world LCS programs is significantly lower than in randomized controlled trials. The purpose of this article is to assess real-world LDCT LCS performance and factors predictive of adherence to LCS recommendations.

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).

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.

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.

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).

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.

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.

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.

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Rationale And Objectives: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS.

Materials And Methods: Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points.

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.

Limited Utility of Pulmonary Nodule Risk Calculators for Managing Large Nodules.

Rationale And Objectives: The optimal management of large pulmonary nodules, at higher risk for lung cancer, has not been determined, and it remains unclear as to which patients should undergo follow-up imaging vs invasive tissue diagnosis via biopsy or surgical resection.

Materials And Methods: Through search of radiology reports, 86 nodules from our institution were identified using the inclusion criterion of solid nodules measuring greater than 8mm. We evaluated these nodules with a number of risk prediction calculators, including the Brock University model, and compared these against the proven diagnosis.

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.

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.

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.