Association of clinical and imaging characteristics with pulmonary function testing in patients with Long-COVID.

Purpose: The purpose of this study is to identify clinical and imaging characteristics associated with post-COVID pulmonary function decline. Methods: This study included 22 patients recovering from COVID-19 who underwent serial spirometry pulmonary function testing (PFT) before and after diagnosis. Patients were divided into two cohorts by difference between baseline and post-COVID follow-up PFT: Decline group (>10 % decrease in FEV1), and Stable group (≤10 % decrease or improvement in FEV1). Demographic, clinical, and laboratory data were collected, as well as PFT and chest computed tomography (CT) at the time of COVID diagnosis and follow-up. CTs were semi-quantitatively scored on a five-point severity scale for disease extent in each lobe by two radiologists. Mann-Whitney U-tests, T-tests, and Chi-Squared tests were used for comparison. P-values <0.05 were considered statistically significant. Results: The Decline group had a higher proportion of neutrophils (79.47 ± 4.83 % vs. 65.45 ± 10.22 %; p = 0.003), a higher absolute neutrophil count (5.73 ± 2.68 × 109/L vs. 3.43 ± 1.74 × 109/L; p = 0.031), and a lower proportion of lymphocytes (9.90 ± 4.20 % vs. 21.21 ± 10.97 %; p = 0.018) compared to the Stable group. The Decline group also had significantly higher involvement of ground-glass opacities (GGO) on follow-up chest CT [8.50 (4.50, 14.50) vs. 3.0 (1.50, 9.50); p = 0.032] and significantly higher extent of reticulations on chest CT at time of COVID diagnosis [6.50 (4.00, 9.00) vs. 2.00 (0.00, 6.00); p = 0.039] and follow-up [5.00 (3.00, 13.00) vs. 2.00 (0.00, 5.00); p = 0.041]. ICU admission was higher in the Decline group than in the Stable group (71.4 % vs. 13.3 %; p = 0.014). Conclusions: This study provides novel insight into factors influencing post-COVID lung function, irrespective of pre-existing pulmonary conditions. Our findings underscore the significance of neutrophil counts, reduced lymphocyte counts, pulmonary reticulation on chest CT at diagnosis, and extent of GGOs on follow-up chest CT as potential indicators of decreased post-COVID lung function. This knowledge may guide prediction and further understanding of long-term sequelae of COVID-19 infection.

Ablation of Stage I-II Non-Small Cell Lung Cancer in Patients With Interstitial Lung Disease: A Multicenter Retrospective Study.

BACKGROUND. Treatment options for patients with interstitial lung disease (ILD) who develop stage I-II non-small cell lung cancer (NSCLC) are severely limited, given that surgical resection, radiation, and systemic therapy are associated with significant morbidity and mortality. OBJECTIVE. The aim of this study was to evaluate the safety and efficacy of percutaneous ablation of stage I-II NSCLC in patients with ILD. METHODS. This retrospective study included patients with ILD and stage I-II NSCLC treated with percutaneous ablation in three health systems between October 2004 and February 2023. At each site, a single thoracic radiologist, blinded to clinical outcomes, reviewed preprocedural chest CT examinations for the presence and type of ILD according to 2018 criteria proposed by the American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Latin American Thoracic Society. The primary outcome was 90-day major (grade ≥ 3) adverse events, based on Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Secondary outcomes were hospital length of stay (HLOS), local tumor control, and overall survival (OS). RESULTS. The study included 33 patients (19 men, 14 women; median age, 78 years; 16 patients with Eastern Cooperative Oncology Group performance status ≤ 1) with ILD who underwent 42 percutaneous ablation sessions (21 cryoablations, 11 radiofrequency ablations, 10 microwave ablations) of 43 NSCLC tumors ((median tumor size, 1.6 cm; IQR, 1.4-2.5 cm; range, 0.7-5.4 cm; 37 stage I, six stage II). The extent of lung fibrosis was 20% or less in 24 patients; 17 patients had imaging findings of definite or probable usual interstitial pneumonia. The 90-day major adverse event rate was 14% (6/42), including one CTCAE grade 4 event. No acute ILD exacerbation or death occurred within 90 days after ablation. The median HLOS was 1 day (IQR, 0-2 days). Median imaging follow-up for local tumor control was 17 months (IQR, 11-32 months). Median imaging or clinical follow-up for OS was 16 months (IQR, 6-26 months). Local tumor control and OS were 78% and 77%, respectively, at 1 year and 73% and 46% at 2 years. CONCLUSION. Percutaneous ablation appears to be a safe and effective treatment option for stage I-II NSCLC in the setting of ILD after multidisciplinary selection. CLINICAL IMPACT. Patients with ILD and stage I-II NSCLC should be considered for percutaneous ablation given that they are frequently ineligible for surgical resection, radiation, and systemic therapy.

ACR Lung CT Screening Reporting and Data System, a Systematic Review and Meta-Analysis Before Change in US Preventative Services Taskforce Eligibility Criteria: 2014 to 2021.

OBJECTIVE: To review Lung CT Screening Reporting and Data System (Lung-RADS) scores from 2014 to 2021, before changes in eligibility criteria proposed by the US Preventative Services Taskforce. METHODS: A registered systematic review and meta-analysis was conducted in MEDLINE, Embase, CINAHL, and Web of Science in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines; eligible studies examined low-dose CT (LDCT) lung cancer screening at institutions in the United States and reported Lung-RADS from 2014 to 2021. Patient and study characteristics, including age, gender, smoking status, pack-years, screening timeline, number of individual patients, number of unique studies, Lung-RADS scores, and positive predictive value (PPV) were extracted. Meta-analysis estimates were derived from generalized linear mixed modeling. RESULTS: The meta-analysis included 24 studies yielding 36,211 LDCT examinations for 32,817 patient encounters. The meta-analysis Lung-RADS 1-2 scores were lower than anticipated by ACR guidelines, at 84.4 (95% confidence interval [CI] 83.3-85.6) versus 90% respectively (P < .001). Lung-RADS 3 and 4 scores were both higher than anticipated by the ACR, at 8.7% (95% CI 7.6-10.1) and 6.5% (95% CI 5.707.4), compared with 5% and 4%, respectively (P < .001). The ACR's minimum estimate of PPV for Lung-RADS 3 to 4 is 21% or higher; we observed a rate of 13.1% (95% CI 10.1-16.8). However, our estimated PPV rate for Lung-RADS 4 was 28.6% (95% CI 21.6-36.8). CONCLUSION: Lung-RADS scores and PPV rates in the literature are not aligned with the ACR's own estimates, suggesting that perhaps Lung-RADS categorization needs to be reexamined for better concordance with real-world screening populations. In addition to serving as a benchmark before screening guideline broadening, this study provides guidance for future reporting of lung cancer screening and Lung-RADS data.

Can incorrect artificial intelligence (AI) results impact radiologists, and if so, what can we do about it? A multi-reader pilot study of lung cancer detection with chest radiography.

OBJECTIVE: To examine whether incorrect AI results impact radiologist performance, and if so, whether human factors can be optimized to reduce error. METHODS: Multi-reader design, 6 radiologists interpreted 90 identical chest radiographs (follow-up CT needed: yes/no) on four occasions (09/20-01/22). No AI result was provided for session 1. Sham AI results were provided for sessions 2-4, and AI for 12 cases were manipulated to be incorrect (8 false positives (FP), 4 false negatives (FN)) (0.87 ROC-AUC). In the Delete AI (No Box) condition, radiologists were told AI results would not be saved for the evaluation. In Keep AI (No Box) and Keep AI (Box), radiologists were told results would be saved. In Keep AI (Box), the ostensible AI program visually outlined the region of suspicion. AI results were constant between conditions. RESULTS: Relative to the No AI condition (FN = 2.7%, FP = 51.4%), FN and FPs were higher in the Keep AI (No Box) (FN = 33.0%, FP = 86.0%), Delete AI (No Box) (FN = 26.7%, FP = 80.5%), and Keep AI (Box) (FN = to 20.7%, FP = 80.5%) conditions (all ps < 0.05). FNs were higher in the Keep AI (No Box) condition (33.0%) than in the Keep AI (Box) condition (20.7%) (p = 0.04). FPs were higher in the Keep AI (No Box) (86.0%) condition than in the Delete AI (No Box) condition (80.5%) (p = 0.03). CONCLUSION: Incorrect AI causes radiologists to make incorrect follow-up decisions when they were correct without AI. This effect is mitigated when radiologists believe AI will be deleted from the patient's file or a box is provided around the region of interest. CLINICAL RELEVANCE STATEMENT: When AI is wrong, radiologists make more errors than they would have without AI. Based on human factors psychology, our manuscript provides evidence for two AI implementation strategies that reduce the deleterious effects of incorrect AI. KEY POINTS: • When AI provided incorrect results, false negative and false positive rates among the radiologists increased. • False positives decreased when AI results were deleted, versus kept, in the patient's record. • False negatives and false positives decreased when AI visually outlined the region of suspicion.

Electronic Cigarettes for Smoking Cessation: The Gap Between Behavior in Smokers and Medical Education.

INTRODUCTION: E-cigarettes have engendered a great deal of controversy within the public health and medical communities.  Methods: Two cross-sectional surveys were administered. First, patients at an annual lung cancer screening appointment who self-identified as former smokers were asked about strategies for achieving and maintaining smoking cessation with open-ended questions. Second, medical students at a single university reported their opinion and knowledge of combustible cigarettes and e-cigarettes. RESULTS: Among the n=102 in the patient survey indicating that they used e-cigarettes or over-the-counter (OTC) nicotine replacement products for smoking cessation, 34.3% (35/102) vaped e-cigarettes, making it the second most common next to patches (47.1% {48/102}). By comparison, n=48 reported using medication. Medical student participants (n=168) were mixed regarding whether a patient should switch from traditional to electronic cigarettes (56.0% yes; 44.0% no) and reported receiving education about traditional cigarettes (92.3%) at a much higher rate than for e-cigarettes (46.4%), p<.001. CONCLUSION: Many former heavy smokers undergoing a lung cancer screen used e-cigarettes to achieve smoking cessation. However, nearly half of medical students surveyed do not think patients should switch from traditional to e-cigarettes.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.

BACKGROUND: Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS). METHODS: A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction. FINDINGS: 1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features. INTERPRETATION: CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care. FUNDING: NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Heavy Smoking Patients Receiving a Lung Cancer Screen Want to Quit: A Call for Tailored Cessation Interventions.

Background: Lung cancer screening for current or former heavy smokers is now recommended among all asymptomatic adults 50-80 years old with a 20 pack-year history of smoking. However, little is known about the smoking-related attitudes of this population. Method: An assessment was conducted among 1,472 current smokers who presented for an annual lung cancer screen at one of 12 diagnostic imaging sites in Rhode Island between April 2019 and May 2020. Patients were asked about their use of smoking products, interest in quitting, and smoking-related attitudes. Results: Patients smoked a median of 16 cigarettes per day; 86.6% were daily cigarette smokers and 30.1% were daily cigar smokers. In total, 91.4% of patients were, to some degree, interested in quitting smoking and 71.4% were seriously thinking about quitting in the next 6 months or sooner. Patients planned on smoking less regardless of whether their lung screen was positive or negative for cancer, though they were more likely to plan on smoking less if negative (on 0-3 pt Likert scale: 0.31, 95% CI [0.27, 0.34] vs. 0.77, 95% CI [0.72, 0.81]). Confidence in quitting and belief in one's inherent ability to quit smoking varied substantially within the sample. Conclusion: Nearly all current smokers receiving a lung cancer screen have some interest in smoking cessation. Due to the heterogeneity in some smoking-related attitudes, tailored interventions for this population should be tested.

Two-year follow-up of exposure, engineering controls, respiratory protection and respiratory health among workers at an indium-tin oxide (ITO) production and reclamation facility.

OBJECTIVES: To determine whether engineering controls and respiratory protection had measurable short-term impact on indium exposure and respiratory health among current indium-tin oxide production and reclamation facility workers. METHODS: We documented engineering controls implemented following our 2012 evaluation and recorded respirator use in 2012 and 2014. We measured respirable indium (Inresp) and plasma indium (InP) in 2012 and 2014, and calculated change in Inresp (∆Inresp) and InP (∆InP) by the 13 departments. We assessed symptoms, lung function, serum biomarkers of interstitial lung disease (Krebs von den Lungen (KL)-6 and surfactant protein (SP)-D) and chest high-resolution CT at both time points and evaluated workers who participated in both 2012 and 2014 for changes in health outcomes (new, worsened or improved). RESULTS: Engineering controls included installation of local exhaust ventilation in both grinding departments (Rotary and Planar) and isolation of the Reclaim department. Respiratory protection increased in most (77%) departments. ∆InP and ∆Inresp often changed in parallel by department. Among 62 workers participating in both 2012 and 2014, 18 (29%) had new or worsening chest symptoms and 2 (3%) had functional decline in lung function or radiographic progression, but average KL-6 and SP-D concentrations decreased, and no cases of clinical indium lung disease were recognised. CONCLUSIONS: Increased engineering controls and respiratory protection can lead to decreased Inresp, InP and biomarkers of interstitial lung disease among workers in 2 years. Ongoing medical monitoring of indium-exposed workers to confirm the longer-term effectiveness of preventive measures is warranted.

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.

An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Interstitial lung disease is a dominant feature in patients with circulating myositis-specific antibodies.

BACKGROUND: Many patients with polymyositis (PM) or dermatomyositis (DM) have circulating myositis-specific antibodies (MSAs). Interstitial lung disease (ILD) is a common manifestation of PM/DM, and it can even precede the onset of characteristic muscle or skin manifestations. Furthermore, there appear to be some patients with ILD and circulating MSAs who do not develop muscle or skin disease even after prolonged follow-up. We sought to determine whether ILD is equally or more common than myositis or dermatitis at the time of initial detection of MSAs. METHODS: We identified all patients found to have circulating MSAs at our institution over a 4-year period and assessed for the presence of lung, muscle, and skin disease at the time of initial detection of MSAs. Among those found to have ILD, we compared demographic and clinical features, chest CT scan findings, and outcomes between those with PM/DM-associated ILD and those with ILD but no muscle or skin disease. RESULTS: A total of 3078 patients were tested for MSAs, and of these 40 were positive. Nine different MSAs were detected, with anti-histidyl tRNA synthetase (anti-Jo-1) being the most common (35% of MSAs). Among patients with positive MSAs, 86% were found to have ILD, compared to 39% and 28% with muscle and skin involvement, respectively (p < 0.001). Fifty percent of all MSA-positive patients had isolated ILD, with no evidence of muscle or skin disease. Those with isolated ILD were more likely to be older and have fibrotic changes on chest CT, less likely to receive immunomodulatory therapy, and had worse overall survival. CONCLUSIONS: In this study we found that individuals with circulating MSAs were more likely to have ILD than classic muscle or skin manifestations of PM/DM at the time of initial detection of MSAs. Our findings suggest that the presence of ILD should be considered a disease-defining manifestation in the presence of MSAs and incorporated into classification criteria for PM/DM.

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Cardiothoracic Complications of Immune Checkpoint Inhibitor Therapy: An Imaging Review.

Immune checkpoint inhibitor therapy has revolutionized the treatment of many different types of cancer. However, despite dramatic improvements in tumor oncologic response and patient outcomes, immune checkpoint blockade has been associated with multiple distinctive side-effects termed immune-related adverse events. These often have important clinical implications because these can vary in severity, sometimes even resulting in death. Therefore, it is important for both radiologists and clinicians to recognize and be aware of these reactions to help appropriately guide patient management. This article specifically highlights imaging manifestations of the most common cardiothoracic toxicities of these agents, including pneumonitis, sarcoid-like granulomatosis and lymphadenopathy, and myocarditis.

Ablation Therapy for Advanced Stage Non-Small Cell Lung Cancer: A National Cancer Database Study.

PURPOSE: To compare overall survival (OS) of ablation with no treatment for patients with advanced stage non-small cell lung cancer. METHODS: Patients with clinical stage IIIB (T1-4N3M0, T4N2M0) and stage IV (T1-4N0-3M1) non-small cell lung cancer, in accordance with the American Joint Committee on Cancer, 7th edition, who did not receive treatment or who received ablation as their sole primary treatment besides chemotherapy from 2004 to 2014, were identified from the National Cancer Data Base. OS was estimated using the Kaplan-Meier method and evaluated by log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score-matched analysis. Relative survival analyses comparing age- and sex-matched United States populations were performed. RESULTS: A total of 140,819 patients were included. The 1-, 2-, 3- and 5-year survival rates relative to age- and sex-matched United States population were 28%, 18%, 12%, and 10%, respectively, for ablation (n = 249); and 30%, 15%, 9%, and 5%, respectively for no treatment (n = 140,570). Propensity score matching resulted in 249 patients in the ablation group versus 498 patients in the no-treatment group. After matching, ablation was associated with longer OS than that in the no-treatment group (median, 5.9 vs 4.7 months, respectively; hazard ratio, 0.844; 95% confidence interval, 0.719-0.990; P = .037). These results persisted in patients with an initial tumor size of ≤3 cm. CONCLUSIONS: Preliminary results suggest ablation may be associated with longer OS in patients with late-stage non-small cell lung cancer than survival in those who received no treatment.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Multicenter Study of Metastatic Lung Tumors Targeted by Interventional Cryoablation Evaluation (SOLSTICE).

OBJECTIVE: To assess the safety and local recurrence-free survival in patients after cryoablation for treatment of pulmonary metastases. METHODS: This multicenter, prospective, single-arm, phase 2 study included 128 patients with 224 lung metastases treated with percutaneous cryoablation, with 12 and 24 months of follow-up. The patients were enrolled on the basis of the outlined key inclusion criteria, which include one to six metastases from extrapulmonary cancers with a maximal diameter of 3.5 cm. Time to progression of the index tumor(s), metastatic disease, and overall survival rates were estimated using the Kaplan-Meier method. Complications were captured for 30 days after the procedure, and changes in performance status and quality of life were also evaluated. RESULTS: Median size of metastases was 1.0 plus or minus 0.6 cm (0.2-4.5) with a median number of tumors of 1.0 plus or minus 1.2 cm (one to six). Local recurrence-free response (local tumor efficacy) of the treated tumor was 172 of 202 (85.1%) at 12 months and 139 of 180 (77.2%) at 24 months after the initial treatment. After a second cryoablation treatment for recurrent tumor, secondary local recurrence-free response (local tumor efficacy) was 184 of 202 (91.1%) at 12 months and 152 of 180 (84.4%) at 24 months. Kaplan-Meier estimates of 12- and 24-month overall survival rates were 97.6% (95% confidence interval: 92.6-99.2) and 86.6% (95% confidence interval: 78.7-91.7), respectively. Rate of pneumothorax that required pleural catheter placement was 26% (44/169). There were eight grade 3 complication events in 169 procedures (4.7%) and one (0.6%) grade 4 event. CONCLUSION: Percutaneous cryoablation is a safe and effective treatment for pulmonary metastases.

Psychometric Validation of a Nonproprietary Survey of Patient Satisfaction for Use in Outpatient Radiology Centers.

OBJECTIVE: Demonstrate the psychometric evaluation process for and results from our radiology-specific patient experience measure. METHODS: We developed a survey to measure five dimensions of patient experience: (1) appointment, (2) reception, (3) registration, (4) procedure, and (5) facility. Each dimension included three to five questions. Each question was answered using a Likert scale (very dissatisfied to very satisfied). Data on procedure type, facility, radiologist interaction, health rating, survey length, and demographics were collected. The survey was implemented at 12 radiology offices. Analyses were conducted using responses from March 2018 to April 2019. Construct validation of the five dimensions was accomplished using confirmatory factor analysis (CFA). Internal consistency was examined using Cronbach and Guttman analysis. RESULTS: The sample included 20,736 subjects. There was strong evidence for construct validity of the five dimensions of patient experience. The CFA achieved the best fit with the five-factor model relative to other models (comparative fit index: 0.98, standardized root mean square error residual: 0.0307, root mean square error of approximation: 0.0371). There was high internal consistency (Cronbach's α 0.94, Guttman coefficient 0.93). Item analysis showed that no questions were consistently skipped. Eighty-two percent of participants said the survey was not too long. Patients reported high satisfaction on all dimensions of satisfaction across modalities and office sites. DISCUSSION: The CFA and internal consistency analyses provide evidence for this survey having good psychometric properties: construct validity for five dimensions of patient experience and high internal consistency among the items. This survey is intended to be used by, and to benefit, radiology practices and their patients.