Effectiveness of COVID-19 Vaccines Over Time Against Clinical and Radiologic Outcomes Related to Severe SARS-CoV-2 Infection.

Background The impact of waning vaccine effectiveness on the severity of COVID-19-related findings discovered with radiologic examinations remains underexplored. Purpose To evaluate the effectiveness of vaccines over time against severe clinical and radiologic outcomes related to SARS-CoV-2 infections. Materials and Methods This multicenter retrospective study included patients in the Korean Imaging Cohort of COVID-19 database who were hospitalized for COVID-19 between June 2021 and December 2022. Patients who had received at least one dose of a SARS-CoV-2 vaccine were categorized based on the time elapsed between diagnosis and their last vaccination. Adjusted multivariable logistic regression analysis was used to estimate vaccine effectiveness against a composite of severe clinical outcomes (invasive ventilation, extracorporeal membrane oxygenation, or in-hospital death) and severe radiologic pneumonia (≥25% of lung involvement), and odds ratios (ORs) were compared between patients vaccinated within 90 days of diagnosis and those vaccinated more than 90 days before diagnosis. Results Of 4196 patients with COVID-19 (mean age, 66 years ± 17 [SD]; 2132 [51%] women, 2064 [49%] men), the ratio of severe pneumonia since their most recent vaccination was as follows: 90 days or less, 18% (277 of 1527); between 91 and 120 days, 22% (172 of 783); between 121 and 180 days, 27% (274 of 1032); between 181 and 240 days, 32% (159 of 496); and more than 240 days, 31% (110 of 358). Patients vaccinated more than 240 days before diagnosis showed increased odds of severe clinical outcomes compared with patients vaccinated within 90 days (OR = 1.94 [95% CI: 1.16, 3.24]; P = .01). Similarly, patients vaccinated more than 240 days before diagnosis showed increased odds of severe pneumonia on chest radiographs compared with patients vaccinated within 90 days (OR = 1.65 [95% CI: 1.13, 2.40]; P = .009). No difference in odds of severe clinical outcomes (P = .13 to P = .68) or severe pneumonia (P = .15 to P = .86) were observed between patients vaccinated 91-240 days before diagnosis and those vaccinated within 90 days of diagnosis. Conclusion Vaccine effectiveness against severe clinical outcomes and severe pneumonia related to SARS-CoV-2 infection gradually declined, with increased odds of both observed in patients vaccinated more than 240 days before diagnosis. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Wells in this issue.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities.

OBJECTIVE: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. METHODS: This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. RESULTS: Radiologists' diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67-0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). CONCLUSION: Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. KEY POINTS: Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

Lung Cancer Staging Using Chest CT and FDG PET/CT Free-Text Reports: Comparison Among Three ChatGPT Large-Language Models and Six Human Readers of Varying Experience.

Background: Although radiology reports are commonly used for lung cancer staging, this task can be challenging given radiologists' variable reporting styles as well as reports' potentially ambiguous and/or incomplete staging-related information. Objective: To compare performance of ChatGPT large-language models (LLMs) and human readers of varying experience in lung cancer staging using chest CT and FDG PET/CT free-text reports. Methods: This retrospective study included 700 patients (mean age, 73.8±29.5 years; 509 male, 191 female) from four institutions in Korea who underwent chest CT or FDG PET/CT for non-small cell lung cancer initial staging from January, 2020 to December, 2023. Examinations' reports used a free-text format, written exclusively in English or in mixed English and Korean. Two thoracic radiologists in consensus determined the overall stage group (IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IVA, IVB) for each report using the AJCC 8th-edition staging system, establishing the reference standard. Three ChatGPT models (GPT-4o, GPT-4, GPT-3.5) determined an overall stage group for each report using a script-based application programming interface, zero-shot learning, and prompt incorporating a staging system summary. Six human readers (two fellowship-trained radiologists with lesser experience than the radiologists who determined the reference standard, two fellows, two residents) also independently determined overall stage groups. GPT-4o's overall accuracy for determining the correct stage among the nine groups was compared with that of the other LLMs and human readers using McNemar tests. Results: GPT-4o had an overall staging accuracy of 74.1%, significantly better than the accuracy of GPT-4 (70.1%, p=.02), GPT-3.5 (57.4%, p<.001), and resident 2 (65.7%, p<.001); significantly worse than the accuracy of fellowship-trained radiologist 1 (82.3%, p<.001) and fellowship-trained radiologist 2 (85.4%, p<.001); and not significantly different from the accuracy of fellow 1 (77.7%, p=.09), fellow 2 (75.6%, p=.53), and resident 1 (72.3%, p=.42). Conclusions: The best-performing model, GPT-4o, showed no significant difference in staging accuracy versus fellows, but significantly worse performance versus fellowship-trained radiologists. The findings do not support use of LLMs for lung cancer staging in place of expert healthcare professionals. Clinical Impact: The findings indicate the importance of domain expertise for performing complex specialized tasks such as cancer staging.

Application of a Deep Learning-Based Contrast-Boosting Algorithm to Low-Dose Computed Tomography Pulmonary Angiography With Reduced Iodine Load.

OBJECTIVE: The aim of this study was to assess the effectiveness of a deep learning-based image contrast-boosting algorithm by enhancing the image quality of low-dose computed tomography pulmonary angiography at reduced iodine load. METHODS: This study included 179 patients who underwent low-dose computed tomography pulmonary angiography with a reduced iodine load using 64 mL of a 1:1 mixture of contrast medium from January 1 to June 30, 2023. For single-energy computed tomography, the noise index was set at 15.4 to maintain a CTDIvol of <2 mGy at 80 kVp, and for dual-energy computed tomography, fast kV-switching between 80 and 140 kVp was employed with a fixed tube current of 145 mA. Images were reconstructed by 50% adaptive statistical iterative reconstruction (AR50) and a commercially available deep learning image reconstruction (TrueFidelity) package at a high strength level (TFH). In addition, AR50 images were further processed using a deep learning-based contrast-boosting algorithm (AR50-CB). Quantitative and qualitative image qualities and numbers of involved vessels with thrombus at each pulmonary artery level were compared in the 3 image types using the Friedman test and Wilcoxon signed rank test. RESULTS: Five hundred thirty-seven reconstructed image datasets of 179 patients were analyzed. Quantitative image analysis showed AR50-CB (30.8 ± 10.0 and 28.1 ± 9.6, respectively) had significantly higher signal-to-noise ratio and contrast-to-noise ratio values than AR50 (20.2 ± 6.2 and 17.8 ± 6.2, respectively) (P < 0.001) or TFH (28.3 ± 8.3 and 24.9 ± 8.1, respectively) (P < 0.001). Qualitative image analysis showed that contrast enhancement and noise scores of AR50-CB were significantly greater than those of AR50 (P < 0.001) and that AR50-CB enhancement scores were significantly higher than TFH enhancement scores (P < 0.001). The number of subsegmental pulmonary arteries affected by thrombus detected was significantly greater for AR50-CB (30 for AR50, 30 for TFH, and 55 for AR50-CB, P < 0.001). CONCLUSIONS: The use of a deep learning-based contrast-boosting algorithm improved image quality in terms of signal-to-noise ratio and contrast-to-noise ratio values and the detection of thrombi in subsegmental pulmonary arteries.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study.

BACKGROUND: Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability. OBJECTIVE: The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers. METHODS: We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods. RESULTS: Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910). CONCLUSIONS: RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.

Increased Scan Speed and Pitch on Ultra-Low-Dose Chest CT: Effect on Nodule Volumetry and Image Quality.

Background and Objectives: This study's objective was to investigate the influence of increased scan speed and pitch on image quality and nodule volumetry in patients who underwent ultra-low-dose chest computed tomography (CT). Material and Methods: One hundred and two patients who had lung nodules were included in this study. Standard-speed, standard-pitch (SSSP) ultra-low-dose CT and high-speed, high-pitch (HSHP) ultra-low-dose CT were obtained for all patients. Image noise was measured as the standard deviation of attenuation. One hundred and sixty-three nodules were identified and classified according to location, volume, and nodule type. Volume measurement of detected pulmonary nodules was compared according to nodule location, volume, and nodule type. Motion artifacts at the right middle lobe, the lingular segment, and both lower lobes near the lung bases were evaluated. Subjective image quality analysis was also performed. Results: The HSHP CT scan demonstrated decreased motion artifacts at the left upper lobe lingular segment and left lower lobe compared to the SSSP CT scan (p < 0.001). The image noise was higher and the radiation dose was lower in the HSHP scan (p < 0.001). According to the nodule type, the absolute relative volume difference was significantly higher in ground glass opacity nodules compared with those of part-solid and solid nodules (p < 0.001). Conclusion: Our study results suggest that HSHP ultra-low-dose chest CT scans provide decreased motion artifacts and lower radiation doses compared to SSSP ultra-low-dose chest CT. However, lung nodule volumetry should be performed with caution for ground glass opacity nodules.

Current and Emerging Knowledge in COVID-19.

COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperinflammatory phases. Clinical features, imaging features, and management are different among the three phases. In the early stage, peripheral ground-glass opacities are predominant CT findings, and therapy directly targeting SARS-CoV-2 is effective. In the later stage, organizing pneumonia or diffuse alveolar damage pattern are predominant CT findings and anti-inflammatory therapies are more beneficial. The risk of severe disease or hospitalization is lower in breakthrough or Omicron variant infection compared with nonimmunized or Delta variant infections. The protection rates of the fourth dose of mRNA vaccination were 34% and 67% against overall infection and hospitalizations for severe illness, respectively. After acute COVID-19 pneumonia, most residual CT abnormalities gradually decreased in extent, but they may remain as linear or multifocal reticular or cystic lesions. Advanced insights into the pathophysiologic and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post-COVID-19 condition, or long COVID, also presents radiology with new challenges.

Comparison of Clinical Outcomes and Imaging Features in Hospitalized Patients with SARS-CoV-2 Omicron Subvariants.

Background Differences in the clinical and radiological characteristics of SARS-CoV-2 Omicron subvariants have not been well studied. Purpose To compare clinical disease severity and radiologically severe pneumonia in patients with COVID-19 hospitalized during a period of either Omicron BA.1/BA.2 or Omicron BA.5 subvariant predominance. Materials and Methods This multicenter retrospective study, included patients registered in the Korean Imaging Cohort of COVID-19 database who were hospitalized for COVID-19 between January and December 2022. Publicly available relative variant genome frequency data were used to determine the dominant periods of Omicron BA.1/BA.2 subvariants (January 17 to June 20, 2022) and the Omicron BA.5 subvariant (July 4 to December 5, 2022). Clinical outcomes and imaging pneumonia outcomes based on chest radiography and CT were compared among predominant subvariants using multivariable analyses adjusted for covariates. Results Of 1916 confirmed patients with COVID-19 (mean age, 72 years ± 16 [SD]; 1019 males), 1269 were registered during the Omicron BA.1/BA.2 subvariant dominant period and 647 during the Omicron BA.5 subvariant dominant period. Patients in the BA.5 group showed lower odds of high-flow O2 requirement (adjusted odds ratio [OR], 0.75 [95% CI: 0.57, 0.99]; P = .04), mechanical ventilation (adjusted OR, 0.49 [95% CI: 0.34, 0.72]; P < .001]), and death (adjusted OR, 0.47 [95% CI: 0.33, 0.68]; P <.001) than those in the BA.1/BA.2 group. Additionally, the BA.5 group had lower odds of severe pneumonia on chest radiographs (adjusted OR, 0.68 [95% CI: 0.53, 0.88]; P = .004) and higher odds of atypical pattern pneumonia on CT images (adjusted OR, 1.81 [95% CI: 1.26, 2.58]; P = .001) than the BA.1/BA.2 group. Conclusions Patients hospitalized during the period of Omicron BA.5 subvariant predominance had lower odds of clinical and pneumonia severity than those hospitalized during the period of Omicron BA.1/BA.2 predominance, even after adjusting for covariates. See also the editorial by Hammer in this issue.

Prevalence and Long-term Outcomes of CT Interstitial Lung Abnormalities in a Health Screening Cohort.

Background The association between interstitial lung abnormalities (ILAs) and long-term outcomes has not been reported in Asian health screening populations. Purpose To investigate ILA prevalence in an Asian health screening cohort and determine rates and risks for ILA progression, lung cancer development, and mortality within the 10-year follow-up. Materials and Methods This observational, retrospective multicenter study included patients aged 50 years or older who underwent chest CT at three health screening centers over a 4-year period (2007-2010). ILA status was classified as none, equivocal ILA, and ILA (nonfibrotic or fibrotic). Progression was evaluated from baseline to the last follow-up CT examination, when available. The log-rank test was performed to compare mortality rates over time between ILA statuses. Multivariable Cox proportional hazards models were used to assess factors associated with hazards of ILA progression, lung cancer development, and mortality. Results Of the 2765 included patients (mean age, 59 years ± 7 [SD]; 2068 men), 94 (3%) had a finding of ILA (35 nonfibrotic and 59 fibrotic ILA) and 119 (4%) had equivocal ILA. The median time for CT follow-up and the entire observation was 8 and 12 years, respectively. ILA progression was observed in 80% (48 of 60) of patients with ILA over 8 years. Those with fibrotic and nonfibrotic ILA had a higher mortality rate than those without ILA (P < .001 and P = .01, respectively) over 12 years. Fibrotic ILA was independently associated with ILA progression (hazard ratio [HR], 10.3; 95% CI: 6.4, 16.4; P < .001), lung cancer development (HR, 4.4; 95% CI: 2.1, 9.1; P < .001), disease-specific mortality (HR, 6.7; 95% CI: 3.7, 12.2; P < .001), and all-cause mortality (HR, 2.5; 95% CI: 1.6, 3.8; P < .001) compared with no ILA. Conclusion The prevalence of interstitial lung abnormalities (ILAs) in an Asian health screening cohort was approximately 3%, and fibrotic ILA was an independent risk factor for ILA progression, lung cancer development, and mortality. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Hatabu and Hata in this issue.

SARS-CoV-2 Variants Infection in Relationship to Imaging-based Pneumonia and Clinical Outcomes.

Background Few reports have evaluated the effect of the SARS-CoV-2 variant and vaccination on the clinical and imaging features of COVID-19. Purpose To evaluate and compare the effect of vaccination and variant prevalence on the clinical and imaging features of infections by the SARS-CoV-2. Materials and Methods Consecutive adults hospitalized for confirmed COVID-19 at three centers (two academic medical centers and one community hospital) and registered in a nationwide open data repository for COVID-19 between August 2021 and March 2022 were retrospectively included. All patients had available chest radiographs or CT images. Patients were divided into two groups according to predominant variant type over the study period. Differences between clinical and imaging features were analyzed with use of the Pearson χ2 test, Fisher exact test, or the independent t test. Multivariable logistic regression analyses were used to evaluate the effect of variant predominance and vaccination status on imaging features of pneumonia and clinical severity. Results Of the 2180 patients (mean age, 57 years ± 21; 1171 women), 1022 patients (47%) were treated during the Delta variant predominant period and 1158 (53%) during the Omicron period. The Omicron variant prevalence was associated with lower pneumonia severity based on CT scores (odds ratio [OR], 0.71 [95% CI: 0.51, 0.99; P = .04]) and lower clinical severity based on intensive care unit (ICU) admission or in-hospital death (OR, 0.43 [95% CI: 0.24, 0.77; P = .004]) than the Delta variant prevalence. Vaccination was associated with the lowest odds of severe pneumonia based on CT scores (OR, 0.05 [95% CI: 0.03, 0.13; P < .001]) and clinical severity based on ICU admission or in-hospital death (OR, 0.15 [95% CI: 0.07, 0.31; P < .001]) relative to no vaccination. Conclusion The SARS-CoV-2 Omicron variant prevalence and vaccination were associated with better clinical outcomes and lower severe pneumonia risk relative to Delta variant prevalence. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Little in this issue.

Non-ECG-gated high-pitch CT angiography versus hybrid ECG-gated CT angiography for aorta using 512-slice CT: comparison of image quality and radiation dose.

BACKGROUND: There have been few reports comparing image quality and radiation dose of aorta computed tomography angiography (CTA) between the high-pitch and the hybrid technique. PURPOSE: To compare the image quality and radiation dose among non-electrocardiogram (ECG)-gated high-pitch CTA and hybrid ECG-gated CTA of the aorta using 512-slice CT. MATERIAL AND METHODS: This retrospective study included 110 patients who underwent non-ECG-gated high-pitch CTA (group 1) or hybrid ECG-gated CTA (group 2) of the entire aorta. Interpretability, image noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and the mean effective radiation dose were compared. RESULTS: The mean image noise of the whole aorta was significantly lower (15.7 ± 1.8 HU vs. 16.5 ± 1.2 HU, P = 0.008) in group 1 than in group 2. The CNR (22.3 ± 4.7 vs. 20.0 ± 3.9, P < 0.001) and SNR (26.5 ± 4.9 vs. 23.2 ± 4.0, P < 0.001) were higher in group 2 compared with group 1. Neither group showed a significant difference in interpretability of the ascending aorta, cardiac chamber, aortic valve, right ostium, and left ostium (all P = 1). The mean effective radiation dose was significantly lower in group 1 than in group 2 (3.5 ± 0.9 mSv vs. 4.3 ± 0.8 mSv, P < 0.001). CONCLUSION: The non-ECG-gated high-pitch technique shows significantly improved CNR and SNR due to reduced noise with lower radiation exposure. The interpretability of the cardiac structure, ascending aorta, aortic valve, and both ostia did not differ significantly between the two groups.

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

BACKGROUND: An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19. OBJECTIVE: We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19. METHODS: This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration. RESULTS: The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859). CONCLUSIONS: The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Correction: Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

[This corrects the article DOI: 10.2196/42717.].

Erratum: Correction of Affiliations in the Article "Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019".

This corrects the article on p. e413 in vol. 35, PMID: 33258333.

BRAF V600E Mutation of Non-Small Cell Lung Cancer in Korean Patients.

Background and Objectives: BRAF mutational status in resected non-small cell lung cancer (NSCLC) in the Korean population is poorly understood. We explored BRAF (particularly BRAF V600E) mutational status among Korean patients with NSCLC. Materials and Methods: This study included 378 patients with resected primary NSCLC who were enrolled from January 2015 to December 2017. The authors obtained formalin-fixed paraffin-embedded (FFPE) tissue blocks and performed peptide nucleic acid (PNA)-clamping polymerase chain reaction (PCR) for detecting BRAF V600, real-time PCR for detecting BRAF V600E, and immunohistochemical analyses using the mutation-specific Ventana VE1 monoclonal antibody. For positive cases in any methods mentioned above, direct Sanger sequencing was additionally performed. Results: The PNA-clamping method revealed the BRAF V600 mutation in 5 (1.3%) of the 378 patients. Among these five patients, real-time PCR, direct Sanger sequencing detected BRAF V600E mutations in three (0.8%) patients. Thus, two cases showed differences in their PNA-clamping and the others. Direct Sanger sequencing of PNA-clamping PCR product was performed for two cases showing negative results on direct Sanger sequencing; both contained BRAF mutations other than V600E. All patients harboring BRAF mutations had adenocarcinomas, and all patients with V600E mutation exhibited minor micropapillary components. Conclusions: Despite the low incidence of the BRAF mutation among Korean patients with NSCLC, lung adenocarcinoma patients with micropapillary components should be prioritized in terms of BRAF mutation testing. Immunohistochemical staining using Ventana VE1 antibody may serve as a screening examination for BRAF V600E.

Imaging and Clinical Features of COVID-19 Breakthrough Infections: A Multicenter Study.

Background Since vaccines against COVID-19 became available, rare breakthrough infections have been reported despite their high efficacies. Purpose To evaluate the clinical and imaging characteristics of patients with COVID-19 breakthrough infections and compare them with those of unvaccinated patients with COVID-19. Materials and Methods In this retrospective multicenter cohort study, the authors analyzed patient (aged ≥18 years) data from three centers that were registered in an open data repository for COVID-19 between June and August 2021. Hospitalized patients with baseline chest radiographs were divided into three groups according to their vaccination status. Differences between clinical and imaging features were analyzed using the Pearson χ2 test, Fisher exact test, and analysis of variance. Univariable and multivariable logistic regression analyses were used to evaluate associations between clinical factors, including vaccination status and clinical outcomes. Results Of the 761 hospitalized patients with COVID-19, the mean age was 47 years and 385 (51%) were women; 47 patients (6%) were fully vaccinated (breakthrough infection), 127 (17%) were partially vaccinated, and 587 (77%) were unvaccinated. Of the 761 patients, 412 (54%) underwent chest CT during hospitalization. Among the patients who underwent CT, the proportions without pneumonia were 22% of unvaccinated patients (71 of 326), 30% of partially vaccinated patients (19 of 64), and 59% of fully vaccinated patients (13 of 22) (P < .001). Fully vaccinated status was associated with a lower risk of requiring supplemental oxygen (odds ratio [OR], 0.24 [95% CI: 0.09, 0.64; P = .005]) and lower risk of intensive care unit admission (OR, 0.08 [95% CI: 0.09, 0.78; P = .02]) compared with unvaccinated status. Conclusion Patients with COVID-19 breakthrough infections had a significantly higher proportion of CT scans without pneumonia compared with unvaccinated patients. Vaccinated patients with breakthrough infections had a lower likelihood of requiring supplemental oxygen and intensive care unit admission. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Schiebler and Bluemke in this issue.

Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study.

OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images. METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115). RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035). CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

Role of digital tomosynthesis in the context of tuberculosis contact investigation: comparisons with digital radiography.

BACKGROUND: Chest radiography value as a screening tool in those exposed to pulmonary tuberculosis (TB) is reduced by its lower sensitivity to detect small intrapulmonary lesions. PURPOSE: To evaluate the efficacy of digital tomosynthesis (DTS) screening of individuals that had contacted persons with active TB using low-dose computed tomography (CT) as the reference standard methods. MATERIAL AND METHODS: This retrospective, community-based screening study of 90 adults who had been in close contact with a TB case was undertaken at our institution. All individuals underwent clinical evaluation, digital radiography (DR), DTS, and low-dose chest CT. Observers assessed and classified DR and DTS images using CT as the reference-standard method. Based on clinical and imaging findings, TB status was classified as normal, latent, minimal, subclinical, and active. Diagnostic performances of DTS and DR for the interpretation of correct diagnosis were calculated. RESULTS: The estimated effective doses for DR, DTS, and low-dose CT were 0.01 mSv, 0.1 mSv, and 0.33 mSv, respectively. TB statuses of the 90 individuals were as follows: 62 latent (68.9%); two subclinical (2.2%); and one minimal (1.1%). The sensitivities, specificities, and accuracies of DTS and DR in the interpretation of correct diagnosis were 75.8%, 100%, 91.1% and 48.5%, 96.5%, 78.9%, respectively. CONCLUSION: DTS appears to be superior to DR for the detection of lung lesions in individuals with TB contacts. DTS can offer a reasonable option for TB contact investigation.

Impact of Mediastinal Lymphadenopathy on the Severity of COVID-19 Pneumonia: A Nationwide Multicenter Cohort Study.

BACKGROUND: We analyzed the differences between clinical characteristics and computed tomography (CT) findings in patients with coronavirus disease 2019 (COVID-19) to establish potential relationships with mediastinal lymphadenopathy and clinical outcomes. METHODS: We compared the clinical characteristics and CT findings of COVID-19 patients from a nationwide multicenter cohort who were grouped based on the presence or absence of mediastinal lymphadenopathy. Differences between clinical characteristics and CT findings in these groups were analyzed. Univariate and multivariate analyses were performed to determine the impact of mediastinal lymphadenopathy on clinical outcomes. RESULTS: Of the 344 patients included in this study, 53 (15.4%) presented with mediastinal lymphadenopathy. The rate of diffuse alveolar damage pattern pneumonia and the visual CT scores were significantly higher in patients with mediastinal lymphadenopathy than in those without (P < 0.05). A positive correlation between the number of enlarged mediastinal lymph nodes and visual CT scores was noted in patients with mediastinal lymphadenopathy (Spearman's ρ = 0.334, P < 0.001). Multivariate analysis showed that mediastinal lymphadenopathy was independently associated with a higher risk of intensive care unit (ICU) admission (odds ratio, 95% confidence interval; 3.25, 1.06-9.95) but was not significantly associated with an increased risk of in-hospital death in patients with COVID-19. CONCLUSION: COVID-19 patients with mediastinal lymphadenopathy had a larger extent of pneumonia than those without. Multivariate analysis adjusted for clinical characteristics and CT findings revealed that the presence of mediastinal lymphadenopathy was significantly associated with ICU admission.

Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort.

The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients' initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696−0.788), 0.794 (0.745−0.843) and 0.770 (0.724−0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820−0.889) than that of all other models (p < 0.001, using DeLong's test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.