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.

Impact of interstitial lung abnormalities on postoperative pulmonary complications and survival of lung cancer.

BACKGROUND: Interstitial lung abnormalities (ILAs) are associated with the risk of lung cancer and its mortality. However, the impact of ILA on treatment-related complications and survival in patients who underwent curative surgery is still unknown. RESEARCH QUESTION: This study aimed to evaluate the significance of the presence of computed tomography-diagnosed ILA and histopathologically matched interstitial abnormalities on postoperative pulmonary complications (PPCs) and the long-term survival of patients who underwent surgical treatment for lung cancer. STUDY DESIGN AND METHODS: A matched case-control study was designed to compare PPCs and mortality among 50 patients with ILA, 50 patients with idiopathic pulmonary fibrosis (IPF) and 200 controls. Cases and controls were matched by sex, age, smoking history, tumour location, the extent of surgery, tumour histology and pathological TNM stage. RESULTS: Compared with the control group, the OR of the prevalence of PPCs increased to 9.56 (95% CI 2.85 to 32.1, p<0.001) in the ILA group and 56.50 (95% CI 17.92 to 178.1, p<0.001) in the IPF group. The 5-year overall survival (OS) rates of the control, ILA and IPF groups were 76% (95% CI 71% to 83%), 52% (95% CI 37% to 74%) and 32% (95% CI 19% to 53%), respectively (log-rank p<0.001). Patients with ILA had better 5-year OS than those with IPF (log-rank p=0.046) but had worse 5-year OS than those in the control group (log-rank p=0.002). CONCLUSIONS: The presence of radiological and pathological features of ILA in patients with lung cancer undergoing curative surgery was associated with frequent complications and decreased survival.

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.

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.

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.

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.

Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm.

OBJECTIVES: Diseases of the middle ear can interfere with normal sound transmission, which results in conductive hearing loss. Since video pneumatic otoscopy (VPO) findings reveal not only the presence of middle ear effusions but also dynamic movements of the tympanic membrane and part of the ossicles, analyzing VPO images was expected to be useful in predicting the presence of middle ear transmission problems. Using a convolutional neural network (CNN), a deep neural network implementing computer vision, this preliminary study aimed to create a deep learning model that detects the presence of an air-bone gap, conductive component of hearing loss, by analyzing VPO findings. DESIGN: The medical records of adult patients who underwent VPO tests and pure-tone audiometry (PTA) on the same day were reviewed for enrollment. Conductive hearing loss was defined as an average air-bone gap of more than 10 dB at 0.5, 1, 2, and 4 kHz on PTA. Two significant images from the original VPO videos, at the most medial position on positive pressure and the most laterally displaced position on negative pressure, were used for the analysis. Applying multi-column CNN architectures with individual backbones of pretrained CNN versions, the performance of each model was evaluated and compared for Inception-v3, VGG-16 or ResNet-50. The diagnostic accuracy predicting the presence of conductive component of hearing loss of the selected deep learning algorithm used was compared with experienced otologists. RESULTS: The conductive hearing loss group consisted of 57 cases (mean air-bone gap = 25 ± 8 dB): 21 ears with effusion, 14 ears with malleus-incus fixation, 15 ears with stapes fixation including otosclerosis, one ear with a loose incus-stapes joint, 3 cases with adhesive otitis media, and 3 ears with middle ear masses including congenital cholesteatoma. The control group consisted of 76 cases with normal hearing thresholds without air-bone gaps. A total of 1130 original images including repeated measurements were obtained for the analysis. Of the various network architectures designed, the best was to feed each of the images into the individual backbones of Inception-v3 (three-column architecture) and concatenate the feature maps after the last convolutional layer from each column. In the selected model, the average performance of 10-fold cross-validation in predicting conductive hearing loss was 0.972 mean areas under the curve (mAUC), 91.6% sensitivity, 96.0% specificity, 94.4% positive predictive value, 93.9% negative predictive value, and 94.1% accuracy, which was superior to that of experienced otologists, whose performance had 0.773 mAUC and 79.0% accuracy on average. The algorithm detected over 85% of cases with stapes fixations or ossicular chain problems other than malleus-incus fixations. Visualization of the region of interest in the deep learning model revealed that the algorithm made decisions generally based on findings in the malleus and nearby tympanic membrane. CONCLUSIONS: In this preliminary study, the deep learning algorithm created to analyze VPO images successfully detected the presence of conductive hearing losses caused by middle ear effusion, ossicular fixation, otosclerosis, and adhesive otitis media. Interpretation of VPO using the deep learning algorithm showed promise as a diagnostic tool to differentiate conductive hearing loss from sensorineural hearing loss, which would be especially useful for patients with poor cooperation.

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.

A Fully Automated Analytic System for Measuring Endolymphatic Hydrops Ratios in Patients With Ménière Disease via Magnetic Resonance Imaging: Deep Learning Model Development Study.

BACKGROUND: Recently, the analysis of endolymphatic hydropses (EHs) via inner ear magnetic resonance imaging (MRI) for patients with Ménière disease has been attempted in various studies. In addition, artificial intelligence has rapidly been incorporated into the medical field. In our previous studies, an automated algorithm for EH analysis was developed by using a convolutional neural network. However, several limitations existed, and further studies were conducted to compensate for these limitations. OBJECTIVE: The aim of this study is to develop a fully automated analytic system for measuring EH ratios that enhances EH analysis accuracy and clinical usability when studying Ménière disease via MRI. METHODS: We proposed the 3into3Inception and 3intoUNet networks. Their network architectures were based on those of the Inception-v3 and U-Net networks, respectively. The developed networks were trained for inner ear segmentation by using the magnetic resonance images of 124 people and were embedded in a new, automated EH analysis system-inner-ear hydrops estimation via artificial intelligence (INHEARIT)-version 2 (INHEARIT-v2). After fivefold cross-validation, an additional test was performed by using 60 new, unseen magnetic resonance images to evaluate the performance of our system. The INHEARIT-v2 system has a new function that automatically selects representative images from a full MRI stack. RESULTS: The average segmentation performance of the fivefold cross-validation was measured via the intersection of union method, resulting in performance values of 0.743 (SD 0.030) for the 3into3Inception network and 0.811 (SD 0.032) for the 3intoUNet network. The representative magnetic resonance slices (ie, from a data set of unseen magnetic resonance images) that were automatically selected by the INHEARIT-v2 system only differed from a maximum of 2 expert-selected slices. After comparing the ratios calculated by experienced physicians and those calculated by the INHEARIT-v2 system, we found that the average intraclass correlation coefficient for all cases was 0.941; the average intraclass correlation coefficient of the vestibules was 0.968, and that of the cochleae was 0.914. The time required for the fully automated system to accurately analyze EH ratios based on a patient's MRI stack was approximately 3.5 seconds. CONCLUSIONS: In this study, a fully automated full-stack magnetic resonance analysis system for measuring EH ratios was developed (named INHEARIT-v2), and the results showed that there was a high correlation between the expert-calculated EH ratio values and those calculated by the INHEARIT-v2 system. The system is an upgraded version of the INHEARIT system; it has higher segmentation performance and automatically selects representative images from an MRI stack. The new model can help clinicians by providing objective analysis results and reducing the workload for interpreting magnetic resonance images.

Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.

Background Multicenter studies are required to validate the added benefit of using deep convolutional neural network (DCNN) software for detecting malignant pulmonary nodules on chest radiographs. Purpose To compare the performance of radiologists in detecting malignant pulmonary nodules on chest radiographs when assisted by deep learning-based DCNN software with that of radiologists or DCNN software alone in a multicenter setting. Materials and Methods Investigators at four medical centers retrospectively identified 600 lung cancer-containing chest radiographs and 200 normal chest radiographs. Each radiograph with a lung cancer had at least one malignant nodule confirmed by CT and pathologic examination. Twelve radiologists from the four centers independently analyzed the chest radiographs and marked regions of interest. Commercially available deep learning-based computer-aided detection software separately trained, tested, and validated with 19 330 radiographs was used to find suspicious nodules. The radiologists then reviewed the images with the assistance of DCNN software. The sensitivity and number of false-positive findings per image of DCNN software, radiologists alone, and radiologists with the use of DCNN software were analyzed by using logistic regression and Poisson regression. Results The average sensitivity of radiologists improved (from 65.1% [1375 of 2112; 95% confidence interval {CI}: 62.0%, 68.1%] to 70.3% [1484 of 2112; 95% CI: 67.2%, 73.1%], P < .001) and the number of false-positive findings per radiograph declined (from 0.2 [488 of 2400; 95% CI: 0.18, 0.22] to 0.18 [422 of 2400; 95% CI: 0.16, 0.2], P < .001) when the radiologists re-reviewed radiographs with the DCNN software. For the 12 radiologists in this study, 104 of 2400 radiographs were positively changed (from false-negative to true-positive or from false-positive to true-negative) using the DCNN, while 56 of 2400 radiographs were changed negatively. Conclusion Radiologists had better performance with deep convolutional network software for the detection of malignant pulmonary nodules on chest radiographs than without. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Jacobson in this issue.

Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography.

BACKGROUND: It is necessary to consider myopic optic disc tilt as it seriously impacts normal ocular parameters. However, ophthalmologic measurements are within inter-observer variability and time-consuming to get. This study aimed to develop and evaluate deep learning models that automatically recognize a myopic tilted optic disc in fundus photography. METHODS: This study used 937 fundus photographs of patients with normal or myopic tilted disc, collected from Samsung Medical Center between April 2016 and December 2018. We developed an automated computer-aided recognition system for optic disc tilt on color fundus photographs via a deep learning algorithm. We preprocessed all images with two image resizing techniques. GoogleNet Inception-v3 architecture was implemented. The performances of the models were compared with the human examiner's results. Activation map visualization was qualitatively analyzed using the generalized visualization technique based on gradient-weighted class activation mapping (Grad-CAM++). RESULTS: Nine hundred thirty-seven fundus images were collected and annotated from 509 subjects. In total, 397 images from eyes with tilted optic discs and 540 images from eyes with non-tilted optic discs were analyzed. We included both eye data of most included patients and analyzed them separately in this study. For comparison, we conducted training using two aspect ratios: the simple resized dataset and the original aspect ratio (AR) preserving dataset, and the impacts of the augmentations for both datasets were evaluated. The constructed deep learning models for myopic optic disc tilt achieved the best results when simple image-resizing and augmentation were used. The results were associated with an area under the receiver operating characteristic curve (AUC) of 0.978 ± 0.008, an accuracy of 0.960 ± 0.010, sensitivity of 0.937 ± 0.023, and specificity of 0.963 ± 0.015. The heatmaps revealed that the model could effectively identify the locations of the optic discs, the superior retinal vascular arcades, and the retinal maculae. CONCLUSIONS: We developed an automated deep learning-based system to detect optic disc tilt. The model demonstrated excellent agreement with the previous clinical criteria, and the results are promising for developing future programs to adjust and identify the effect of optic disc tilt on ophthalmic measurements.

Establishment of a Nationwide Korean Imaging Cohort of Coronavirus Disease 2019.

BACKGROUND: The Korean Society of Thoracic Radiology (KSTR) recently constructed a nation-wide coronavirus disease 2019 (COVID-19) database and imaging repository, referred to the Korean imaging cohort of COVID-19 (KICC-19) based on the collaborative efforts of its members. The purpose of this study was to provide a summary of the clinico-epidemiological data and imaging data of the KICC-19. METHODS: The KSTR members at 17 COVID-19 referral centers retrospectively collected imaging data and clinical information of consecutive patients with reverse transcription polymerase chain reaction-proven COVID-19 in respiratory specimens from February 2020 through May 2020 who underwent diagnostic chest computed tomography (CT) or radiograph in each participating hospital. RESULTS: The cohort consisted of 239 men and 283 women (mean age, 52.3 years; age range, 11-97 years). Of the 522 subjects, 201 (38.5%) had an underlying disease. The most common symptoms were fever (n = 292) and cough (n = 245). The 151 patients (28.9%) had lymphocytopenia, 86 had (16.5%) thrombocytopenia, and 227 patients (43.5%) had an elevated CRP at admission. The 121 (23.4%) needed nasal oxygen therapy or mechanical ventilation (n = 38; 7.3%), and 49 patients (9.4%) were admitted to an intensive care unit. Although most patients had cured, 21 patients (4.0%) died. The 465 (89.1%) subjects underwent a low to standard-dose chest CT scan at least once during hospitalization, resulting in a total of 658 CT scans. The 497 subjects (95.2%) underwent chest radiography at least once during hospitalization, which resulted in a total of 1,475 chest radiographs. CONCLUSION: The KICC-19 was successfully established and comprised of 658 CT scans and 1,475 chest radiographs of 522 hospitalized Korean COVID-19 patients. The KICC-19 will provide a more comprehensive understanding of the clinical, epidemiological, and radiologic characteristics of patients with COVID-19.

Resected Pure Small Cell Lung Carcinomas and Combined Small Cell Lung Carcinomas: Histopathology Features, Imaging Features, and Prognoses.

OBJECTIVE: The objective of our study was to investigate histopathology features, imaging features, and prognoses of surgically resected pure small cell lung carcinomas (SCLCs) and combined SCLCs. MATERIALS AND METHODS: Forty-one patients with a pure SCLC or a combined SCLC underwent preoperative chest CT and 18F-FDG PET/CT and subsequent surgical resection. The clinicopathologic findings were noted by reviewing the electronic medical records. The imaging features of individual tumors were analyzed on chest CT and PET/CT scans. Each tumor was classified as being located centrally (at or in the segmental bronchus or proximal to the segmental bronchus) or peripherally (distal to the segmental bronchus). The maximum standardized uptake value (SUVmax) of each tumor was measured at PET. The 7th edition of the TNM staging system was adopted for staging. RESULTS: The study group was composed of 34 men and seven women with a mean age of 62.0 ± 10.2 (SD) years. Sixteen of 41 (39%) patients had pure SCLC, and the remaining patients had combined SCLC. The most common combined SCLC histologic subgroup was combined SCLC and large cell neuroendocrine carcinoma in 17 (41%) patients. The mean SUVmax of pure SCLCs was 5.6 ± 2.2 and was significantly lower than that of combined SCLCs (p < 0.01). Thirty-one patients (76%) had a peripheral tumor, and 10 (24%) had a central tumor. The overall survival (OS) of the 10 patients with a central tumor was 44.6 months, significantly shorter than the OS of the 31 patients with a peripheral tumor (179.2 months) (p = 0.017). The OS of 21 patients with stage I disease was significantly longer than the OS of patients with higher-stage cancer (p = 0.004). CONCLUSION: In our study group of patients with surgically resected SCLC, patients with a peripheral tumor (including a purely endobronchial tumor) or stage I disease showed a better prognosis than those with a central tumor or higher-stage disease.

Risk factors and clinical characteristics of lung cancer in idiopathic pulmonary fibrosis: a retrospective cohort study.

BACKGROUND: Lung cancer is a common comorbidity of idiopathic pulmonary fibrosis (IPF) and has poor outcomes. The incidence and clinical factors related to development of lung cancer in idiopathic pulmonary fibrosis (IPF) are unclear. The aim of this study was to elucidate the cumulative incidence, risk factors, and clinical characteristics of lung cancer in IPF. METHODS: In this retrospective study, we analyzed clinical data for 938 patients who were diagnosed with IPF without lung cancer between 1998 and 2013. Demographic, physiologic, radiographic, and histologic characteristics were reviewed. Cumulative incidence of lung cancer and survival were estimated by the Kaplan-Meier method. Risk factors of lung cancer development were determined by Cox proportional hazard analysis. RESULTS: Among 938 IPF patients without lung cancer at initial diagnosis, lung cancer developed in 135 (14.5%) during the follow-up period. The cumulative incidences of lung cancer were 1.1% at 1 year, 8.7% at 3, 15.9% at 5, and 31.1% at 10 years. Risk factors of lung cancer were male gender, current smoking at IPF diagnosis, and rapid annual decline of 10% or more in forced vital capacity (FVC). Patients who developed lung cancer were mostly elderly men with smoking history. Squamous cell carcinoma followed by adenocarcinoma was the most common histologic type. Lung cancer was frequently located in areas abutting or within fibrosis. Survival was significantly worse in patients with lung cancer compared to patients with IPF alone. CONCLUSION: Lung cancer frequently developed in patients with IPF and was common in current-smoking men with rapid decline of FVC.

Intermittent Antibiotic Therapy for Recurrent Nodular Bronchiectatic Mycobacterium avium Complex Lung Disease.

Intermittent, three-times-weekly oral antibiotic therapy is recommended for the initial treatment of noncavitary nodular bronchiectatic (NB) Mycobacterium avium complex (MAC) lung disease. However, intermittent therapy is not recommended for patients who have been previously treated. We evaluated 53 patients with recurrent noncavitary NB MAC lung disease who underwent antibiotic treatment for ≥12 months with daily therapy (n = 26) or intermittent therapy (n = 27) between January 2008 and December 2015. Baseline characteristics were comparable between daily therapy and intermittent therapy groups. Sputum culture conversion rates did not differ between daily therapy (21/26, 81%) and intermittent therapy (22/27, 82%) groups. Compared to the etiologic organism at the time of previous treatment, recurrent MAC lung disease was caused by the same MAC species in 38 patients (72%) and by a different MAC species in 15 patients (28%). Genotype analysis in patients with sequenced paired isolates revealed that 86% (12/14) of cases with same species recurrence were due to reinfection with a new MAC genotype. In conclusion, most recurrent noncavitary NB MAC lung disease cases were caused by reinfection rather than relapse. Intermittent antibiotic therapy is a reasonable treatment strategy for recurrent noncavitary NB MAC lung disease.

Lung Adenocarcinoma: CT Features Associated with Spread through Air Spaces.

Purpose To identify the features at CT that are predictive of spread through air spaces (STAS) in surgically resected lung adenocarcinomas. Materials and Methods For this retrospective study, presence of STAS was evaluated in 948 consecutive patients who underwent surgical resection for lung adenocarcinoma from April 2015 to December 2016. Patients who were positive for STAS and negative for STAS were matched at a ratio of 1:2 by using patient variables (age, sex, and smoking status). CT features (ie, percentage of solid component, maximum diameter of solid component, lesion density, location, margin, shape, pseudocavity, calcification, central low attenuation, ill-defined peripheral opacity, air bronchogram, satellite lesions, and pleural retraction) were analyzed by using multivariable logistic regression and receiver operating characteristic curves. Results The final study population consisted of 276 patients (mean age, 59 years; age range, 32-78 years) including 129 men (mean age, 60 years; age range, 36-78 years) and 147 women (mean age, 59 years; age range, 32-78 years). Ninety-two patients were positive for STAS and 184 patients were negative for STAS. STAS was more common in solid tumors (71 of 92; 77%) than in part-solid (21 of 92; 23%) or ground-glass lesions (0 of 92; 0%) (P < .001). STAS was also associated with central low attenuation, ill-defined opacity, air bronchogram, and percentage of solid component (all P < .001). Percentage of solid component was an independent predictor of STAS (odds ratio, 1.06; 95% confidence interval: 1.03, 1.08) and a cut-off value of 90% showed a discriminatory power with a sensitivity of 89.2% and a specificity of 60.3%. Conclusion Percentage of solid component was independently associated with spread through air spaces in lung adenocarcinomas. © RSNA, 2018 Online supplemental material is available for this article.

Pulmonary mucormycosis: serial morphologic changes on computed tomography correlate with clinical and pathologic findings.

PURPOSE: To evaluate serial computed tomography (CT) findings of pulmonary mucormycosis correlated with peripheral blood absolute neutrophil count (ANC). MATERIALS AND METHODS: Between February 1997 and June 2016, 20 immunocompromised patients (10 males, 10 females; mean age, 48.9 years) were histopathologically diagnosed as pulmonary mucormycosis. On initial (n=20) and follow-up (n=15) CT scans, the patterns of lung abnormalities and their changing features on follow-up scans were evaluated, and the pattern changes were correlated with ANC changes. RESULTS: All patients were immunocompromised. On initial CT scans, nodule (≤3cm)/mass (>3cm) or consolidation with surrounding ground-glass opacity halo (18/20, 90%)) was the most common pattern. On follow-up CT, morphologic changes (13/15, 87%) could be seen and they included reversed halo (RH) sign, central necrosis, and air-crescent sign. Although all cases did not demonstrate the regular morphologic changes at the same timeline, various combinations of pattern change could be seen in all patients. Sequential morphologic changes were related with recovering of ANC in 13 of 15 patients. CONCLUSION: Pulmonary mucormycosis most frequently presents as consolidation or nodule/mass with halo sign at CT. Morphologic changes into RH sign, central necrotic cavity or air-crescent sign occur with treatment and recovery of ANC. KEY POINTS: • Pulmonary mucormycosis showed various CT-morphology including CT halo sign • Pulmonary mucormycosis had trends of serial morphologic changes on follow-ups • Recovery of absolute neutrophil count changed CT-morphology of mucormycosis in immune-compromised patients.

Outcomes of Mycobacterium avium complex lung disease based on clinical phenotype.

The effect of the clinical phenotype of Mycobacterium avium complex (MAC) lung disease on treatment outcome and redevelopment of nontuberculous mycobacterial (NTM) lung disease after treatment completion has not been studied systematically.We evaluated 481 treatment-naïve patients with MAC lung disease who underwent antibiotic treatment for ≥12 months between January 2002 and December 2013.Out of 481 patients, 278 (58%) had noncavitary nodular bronchiectatic (NB) disease, 80 (17%) had cavitary NB disease and 123 (25%) had fibrocavitary disease. Favourable outcome was higher in patients with noncavitary disease (88%) than in patients with cavitary disease (76% for fibrocavitary and 78% for cavitary NB disease; p<0.05). Cavitary disease was independently associated with unfavourable outcomes (p<0.05). Out of 402 patients with favourable outcomes, 118 (29%) experienced redevelopment of NTM lung disease, with the same MAC species recurring in 65 (55%) patients. The NB form was an independent risk factor for redevelopment of NTM lung disease (p<0.05). In patients with recurrent MAC lung disease due to the same species, bacterial genotyping revealed that 74% of cases were attributable to reinfection and 26% to relapse.Treatment outcomes and redevelopment of NTM lung disease after treatment completion differed by clinical phenotype of MAC lung disease.