Case 291: Amyloid-associated Cystic Lung Disease and Coexistent Mucosa-associated Lymphoid Tissue Lymphoma.

History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease. There was no family history of pulmonary or systemic disease. Her routine blood test results revealed a white blood cell count of 4.6 × 109/L (normal range, [4.4-10.1] × 109/L), a hemoglobin level of 7.76 mmol/L (normal range, 7.26-9.18 mmol/L), a platelet count of 189 × 109/L (normal range, [170-380] × 109/L), a bilirubin level of 8 mmol/L (normal range, <19 mmol/L), and a creatinine level of 63 mmol/L (normal range, 45-82 mmol/L), all within normal limits. Lung function tests at presentation yielded normal results, with a diffusing capacity for carbon monoxide of 95% and a forced vital capacity of 2.29 (98% predicted value). However, this patient had an elevated serum globulin level of 47 g/L (normal range, 26-32 g/L) and an erythrocyte sedimentation rate of 36 mm/h (normal range, 0-20 mm/h), while C-reactive protein level was normal at less than 0.35 mg/dL. She was seropositive for antinuclear (titer >1/720), anti-Ro, anti-La, and anti-extractable nuclear antigen antibodies. Chest radiography and CT were performed at presentation and 14-year follow-up. PET/CT was performed at 7- and 13-year follow-up. Throughout this 14-year follow-up period, she remained completely free of respiratory symptoms and continued to go for a brisk walk every day. At 14-year follow-up, there was no substantial change in serum laboratory values, but a lung function test revealed her diffusing capacity for carbon monoxide had decreased to 52%, while her forced vital capacity remained good at 95%; these findings were suggestive of interval development of restrictive lung function.

Case 291.

History A 46-year-old woman with known mixed connective tissue disease with clinical features of scleroderma and polymyositis and who was not on specific medications was referred to our institution to assess for interstitial lung disease due to her predisposing condition. She was a nonsmoker, had no respiratory symptoms, and enjoyed good exercise tolerance. She did not have any cutaneous lesions or renal disease. There was no family history of pulmonary or systemic disease. Her routine blood test results revealed a white blood cell count of 4.6 × 109/L (normal range, [4.4-10.1] × 109/L), a hemoglobin level of 7.76 mmol/L (normal range, 7.26-9.18 mmol/L), a platelet count of 189 × 109/L (normal range, [170-380] × 109/L), a bilirubin level of 8 µmol/L (7-19 µmol/L), and a creatinine level of 63 µmol/L (45-82 µmol/L), all within normal limits. Lung function tests at presentation yielded normal results, with a diffusing capacity for carbon monoxide of 95% and a forced vital capacity of 2.29 (98% predicted value). However, this patient had an elevated serum globulin level of 47 g/L (normal range, 26-32 g/L) and an erythrocyte sedimentation rate of 36 mm/h (normal range, 0-20 mm/h), while C-reactive protein level was normal at less than 0.35 mg/dL. She was seropositive for antinuclear (titer >1/720), anti-Ro, anti-La, and anti-extractable nuclear antigen antibodies. Chest radiography and CT were performed at presentation (Figs 1, 2) and 14-year follow-up (Figs 3, 4). PET/CT was performed at 7- (Fig 5, A and B) and 13-year follow-up (Fig 5, C and D). Throughout this 14-year follow-up period, she remained completely free of respiratory symptoms and continued to go for a brisk walk every day. At 14-year follow-up, there was no substantial change in serum laboratory values, but a lung function test revealed her diffusing capacity for carbon monoxide had decreased to 52%, while her forced vital capacity remained good at 95%; these findings were suggestive of interval development of restrictive lung function.

Development and validation of risk prediction models for COVID-19 positivity in a hospital setting.

OBJECTIVES: To develop: (1) two validated risk prediction models for coronavirus disease-2019 (COVID-19) positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation. METHODS: Patients with and without COVID-19 were included from 4 Hong Kong hospitals. The database was randomly split into 2:1: for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4 and 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). RESULTS: A total of 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. The first prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). The second model developed has the same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on the H-L test (p = 0.781 and 0.155, respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV. CONCLUSION: Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR). MATERIALS AND METHODS: In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC). RESULTS: The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test). CONCLUSIONS: A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis.

PURPOSE: The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs. METHODS: We retrospectively included COVID-19 patients between 24/01/2020 and 31/03/2020 as case group and patients with pulmonary GGOs between 04/02/2012 and 31/03/2020 as a control group. Radiomics features were extracted from contoured GGOs by PyRadiomics. The least absolute shrinkage and selection operator method was used to establish the radiomics model. We assessed the performance using the area under the curve of the receiver operating characteristic curve (AUC). RESULTS: A total of 301 patients (age mean ±â€¯SD: 64 ±â€¯15 years; male: 52.8 %) from three hospitals were enrolled, including 33 COVID-19 patients in the case group and 268 patients with malignancies or pneumonia in the control group. Thirteen radiomics features out of 474 were selected to build the model. This model achieved an AUC of 0.905, accuracy of 89.5 %, sensitivity of 83.3 %, specificity of 90.0 % in the testing set. CONCLUSION: We developed a noninvasive radiomics model based on CT imaging for the diagnosis of COVID-19 based on GGO lesions, which could be a promising supplementary tool for improving specificity for COVID-19 in a population confounded by ground glass opacity changes from other etiologies.

Frequency and Distribution of Chest Radiographic Findings in Patients Positive for COVID-19.

Background Current coronavirus disease 2019 (COVID-19) radiologic literature is dominated by CT, and a detailed description of chest radiography appearances in relation to the disease time course is lacking. Purpose To describe the time course and severity of findings of COVID-19 at chest radiography and correlate these with real-time reverse transcription polymerase chain reaction (RT-PCR) testing for severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2, nucleic acid. Materials and Methods This is a retrospective study of patients with COVID-19 confirmed by using RT-PCR and chest radiographic examinations who were admitted across four hospitals and evaluated between January and March 2020. Baseline and serial chest radiographs (n = 255) were reviewed with RT-PCR. Correlation with concurrent CT examinations (n = 28) was performed when available. Two radiologists scored each chest radiograph in consensus for consolidation, ground-glass opacity, location, and pleural fluid. A severity index was determined for each lung. The lung scores were summed to produce the final severity score. Results The study was composed of 64 patients (26 men; mean age, 56 years ± 19 [standard deviation]). Of these, 58 patients had initial positive findings with RT-PCR (91%; 95% confidence interval: 81%, 96%), 44 patients had abnormal findings at baseline chest radiography (69%; 95% confidence interval: 56%, 80%), and 38 patients had initial positive findings with RT-PCR testing and abnormal findings at baseline chest radiography (59%; 95% confidence interval: 46%, 71%). Six patients (9%) showed abnormalities at chest radiography before eventually testing positive for COVID-19 with RT-PCR. Sensitivity of initial RT-PCR (91%; 95% confidence interval: 83%, 97%) was higher than that of baseline chest radiography (69%; 95% confidence interval: 56%, 80%) (P = .009). Radiographic recovery (mean, 6 days ± 5) and virologic recovery (mean, 8 days ± 6) were not significantly different (P = .33). Consolidation was the most common finding (30 of 64; 47%) followed by ground-glass opacities (21 of 64; 33%). Abnormalities at chest radiography had a peripheral distribution (26 of 64; 41%) and lower zone distribution (32 of 64; 50%) with bilateral involvement (32 of 64; 50%). Pleural effusion was uncommon (two of 64; 3%). The severity of findings at chest radiography peaked at 10-12 days from the date of symptom onset. Conclusion Findings at chest radiography in patients with coronavirus disease 2019 frequently showed bilateral lower zone consolidation, which peaked at 10-12 days from symptom onset. © RSNA, 2020.

Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review.

PURPOSE: To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases. MATERIALS AND METHODS: This was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted. RESULTS: The predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations. CONCLUSION: Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue.