The RSNA International COVID-19 Open Radiology Database (RICORD).

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.

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

BACKGROUND. Low-dose CT (LDCT) lung cancer screening (LCS) has been shown to decrease mortality in persons with a significant smoking history. However, adherence in real-world LCS programs is significantly lower than in randomized controlled trials. OBJECTIVE. The purpose of this article is to assess real-world LDCT LCS performance and factors predictive of adherence to LCS recommendations. METHODS. We retrospectively identified all persons who underwent at least two LCS examinations from 2014 to 2019. Patient demographics, smoking history and behavior changes, Lung-RADS category, PPV, NPV, and adherence to screening recommendations were recorded. Predictors of adherence were assessed via univariate comparisons and multivariate logistic regression. RESULTS. A total of 260 persons returned for follow-up LDCT (57.7% had two, 34.2% had three, 7.7% had four, and 0.4% had five LDCT examinations). A total of 43 of 260 (16.5%) had positive (Lung-RADS category 3 or above) scans, of which 27 of 260 persons (10.3%) were graded as Lung-RADS category 3, eight of 260 (3.1%) were category 4A, six of 260 (2.3%) were category 4B, and two of 260 (0.8%) were category 4X. Cancer was diagnosed in four of the 260 (three with lung cancer and one with metastatic melanoma). A total of 143 of 260 (55.0%) persons were current smokers at baseline and 121 of 260 (46.5%) were current smokers at the last round of LCS. LCS had sensitivity of 100.0%, specificity of 84.8%, PPV of 9.3%, and NPV of 100%. Overall adherence was 43.0% but increased progressively with higher Lung-RADS category (Lung-RADS 1: 33.2%; Lung-RADS 2: 46.3%; Lung-RADS 3: 53.8%; Lung-RADS 4A: 77.8%; Lung-RADS 4B: 83.3%; Lung-RADS 4X: 100%; p < .001). was also higher in former versus current smokers (50.0% vs 36.2%; p < .001). Being a former smoker and having a nodule that is Lung-RADS category 3 or greater were the only significant independent predictors of adherence. CONCLUSION. Our real-world LCS program showed very high sensitivity and NPV, but moderate specificity and very low PPV. Adherence to LCS recommendations increased with former versus current smokers and in those with positive (Lung-RADS categories 3, 4A, 4B, or 4X) LCS examinations. Adherence was less than 50.0% in current smokers and persons with negative (Lung-RADS categories 1 or 2) LCS examinations. CLINICAL IMPACT. Our results offer a road map for targeted performance improvement by focusing on LCS subjects less likely to remain in the program, such as persons with negative LCS examinations and persons who continue to smoke, potentially improving LCS cost effectiveness and maximizing its societal benefits.

Case 252: Acute Hyperammonemic Encephalopathy Resulting from Late-Onset Ornithine Transcarbamylase Deficiency.

History A 19-year-old woman with no pertinent medical history was brought to the emergency department after being found unconscious on her bathroom floor by her roommate. In the preceding weeks, she had reported intractable nausea and vomiting, for which she had been taking ondansetron. No other medications had been prescribed. The day prior to presentation, she had contacted her mother and described increasing confusion. Glasgow coma scale score on arrival in the emergency department was 4. Intravenous naloxone was administered, without immediate response. Initial blood glucose level was 232 mg/dL (12.8 mmol/L) (normal range, 79-140 mg/dL [4.4- 7.7 mmol/L]), and other routine laboratory test results were normal. Urine toxicology results were negative. Cerebrospinal fluid evaluation revealed levels were within normal limits. Neurologic examination revealed dilated pupils, which showed a sluggish response to light, and left lower extremity rigidity with intermittent tremors. Initial unenhanced cranial computed tomographic (CT) findings were negative. Magnetic resonance (MR) imaging of the brain was performed. The patient's condition deteriorated, with increasing cerebral edema over the next week, and she was declared brain dead. Her liver was transplanted into an adult recipient, who subsequently developed cerebral edema and elevated plasma ammonia levels, resulting in death in the immediate postoperative period.

The Short and Long of COVID-19: A Review of Acute and Chronic Radiologic Pulmonary Manifestations of SARS-2-CoV and Their Clinical Significance.

Coronavirus disease 2019 (COVID-19) pneumonia has had catastrophic effects worldwide. Radiology, in particular computed tomography (CT) imaging, has proven to be valuable in the diagnosis, prognostication, and longitudinal assessment of those diagnosed with COVID-19 pneumonia. This article will review acute and chronic pulmonary radiologic manifestations of COVID-19 pneumonia with an emphasis on CT and also highlighting histopathology, relevant clinical details, and some notable challenges when interpreting the literature.

Thoracic Infections in Solid Organ Transplants: Radiological Features and Approach to Diagnosis.

Solid organ transplantations (SOT) continue to increase in number, and infections remain one of, if not the most important factor affecting patient morbidity and mortality. The number of possible pulmonary infections in SOT is vast, which include community-acquired, nosocomial, and opportunistic pathogens. Incorporating additional information, such as characteristic imaging appearances, time from transplantation, and an approach to imaging features, the radiological differential diagnosis can be narrowed, allowing imaging to remain central in SOT patient management.

CT Features of Lymphatic Plastic Bronchitis in Adults: Correlation with Multimodality Lymphatic Imaging.

Purpose: To distinguish CT patterns of lymphatic and nonlymphatic causes of plastic bronchitis (PB) through comparison with lymphatic imaging. Materials and Methods: In this retrospective study, chest CT images acquired prior to lymphatic workup were assessed in 44 patients with PB from January 2014 to August 2020. The location and extent of ground-glass opacity (GGO) was compared with symptoms and lymphatic imaging. Statistical analysis was performed using descriptive statistics, logistic regression, Pearson correlation coefficient, and unweighted κ coefficient for interobserver agreement. Sensitivity and specificity of GGO for lymphatic PB were calculated. Results: Lymphatic imaging was performed in 44 patients (median age, 52 years ± 21 [IQR]; 23 women): 35 with lymphatic PB and nine with nonlymphatic PB. GGO was more frequently observed in patients with lymphatic PB than in those with nonlymphatic PB (91% [32 of 35] vs 33% [three of nine]; P < .001). Univariate logistic regression confirmed this result by showing that GGO was a significant predictor of lymphatic PB (odds ratio, 21 (95% CI: 3.8, 159.7). The model areas under the receiver operating characteristic curve (AUCs) of GGO unadjusted and adjusted for demographics were 0.79 and 0.86, respectively. The location of GGO correlated with lymphatic imaging and bronchoscopic findings. Overall sensitivity and specificity of GGO for lymphatic PB were 91% (32 of 35; 95% CI: 76, 98) and 67% (six of nine; 95% CI: 30, 93), respectively. Conclusion: Patients with lymphatic PB predominantly had multifocal GGO with or without a "crazy paving" pattern; identification of GGO should prompt lymphatic workup in this frequently misdiagnosed condition.Keywords: Lymphography, Lymphatic, CT, Tracheobronchial Tree, Thorax© RSNA, 2022See also commentary by Kligerman and White in this issue.

Radiomic Phenotypes for Improving Early Prediction of Survival in Stage III Non-Small Cell Lung Cancer Adenocarcinoma after Chemoradiation.

We evaluate radiomic phenotypes derived from CT scans as early predictors of overall survival (OS) after chemoradiation in stage III primary lung adenocarcinoma. We retrospectively analyzed 110 thoracic CT scans acquired between April 2012-October 2018. Patients received a median radiation dose of 66.6 Gy at 1.8 Gy/fraction delivered with proton (55.5%) and photon (44.5%) beam treatment, as well as concurrent chemotherapy (89%) with carboplatin-based (55.5%) and cisplatin-based (36.4%) doublets. A total of 56 death events were recorded. Using manual tumor segmentations, 107 radiomic features were extracted. Feature harmonization using ComBat was performed to mitigate image heterogeneity due to the presence or lack of intravenous contrast material and variability in CT scanner vendors. A binary radiomic phenotype to predict OS was derived through the unsupervised hierarchical clustering of the first principal components explaining 85% of the variance of the radiomic features. C-scores and likelihood ratio tests (LRT) were used to compare the performance of a baseline Cox model based on ECOG status and age, with a model integrating the radiomic phenotype with such clinical predictors. The model integrating the radiomic phenotype (C-score = 0.69, 95% CI = (0.62, 0.77)) significantly improved (p<0.005) upon the baseline model (C-score = 0.65, CI = (0.57, 0.73)). Our results suggest that harmonized radiomic phenotypes can significantly improve OS prediction in stage III NSCLC after chemoradiation.

Development of a robust radiomic biomarker of progression-free survival in advanced non-small cell lung cancer patients treated with first-line immunotherapy.

We aim to determine the feasibility of a novel radiomic biomarker that can integrate with other established clinical prognostic factors to predict progression-free survival (PFS) in patients with non-small cell lung cancer (NSCLC) undergoing first-line immunotherapy. Our study includes 107 patients with stage 4 NSCLC treated with pembrolizumab-based therapy (monotherapy: 30%, combination chemotherapy: 70%). The ITK-SNAP software was used for 3D tumor volume segmentation from pre-therapy CT scans. Radiomic features (n = 102) were extracted using the CaPTk software. Impact of heterogeneity introduced by image physical dimensions (voxel spacing parameters) and acquisition parameters (contrast enhancement and CT reconstruction kernel) was mitigated by resampling the images to the minimum voxel spacing parameters and harmonization by a nested ComBat technique. This technique was initialized with radiomic features, clinical factors of age, sex, race, PD-L1 expression, ECOG status, body mass index (BMI), smoking status, recurrence event and months of progression-free survival, and image acquisition parameters as batch variables. Two phenotypes were identified using unsupervised hierarchical clustering of harmonized features. Prognostic factors, including PDL1 expression, ECOG status, BMI and smoking status, were combined with radiomic phenotypes in Cox regression models of PFS and Kaplan Meier (KM) curve-fitting. Cox model based on clinical factors had a c-statistic of 0.57, which increased to 0.63 upon addition of phenotypes derived from harmonized features. There were statistically significant differences in survival outcomes stratified by clinical covariates, as measured by the log-rank test (p = 0.034), which improved upon addition of phenotypes (p = 0.00022). We found that mitigation of heterogeneity by image resampling and nested ComBat harmonization improves prognostic value of phenotypes, resulting in better prediction of PFS when added to other prognostic variables.

Statin-Induced Triad of Autoimmune Myocarditis, Myositis, and Transaminitis.

Despite well-established cardiovascular benefits, statins have been associated with myopathic side effects ranging from myalgias to rhabdomyolysis and autoimmune necrotizing myositis. Statins have not been previously shown to cause myocarditis. Our case highlights this rare entity.

Impact of Interobserver Variability in Manual Segmentation of Non-Small Cell Lung Cancer (NSCLC) Applying Low-Rank Radiomic Representation on Computed Tomography.

This study tackles interobserver variability with respect to specialty training in manual segmentation of non-small cell lung cancer (NSCLC). Four readers included for segmentation are: a data scientist (BY), a medical student (LS), a radiology trainee (MH), and a specialty-trained radiologist (SK) for a total of 293 patients from two publicly available databases. Sørensen-Dice (SD) coefficients and low rank Pearson correlation coefficients (CC) of 429 radiomics were calculated to assess interobserver variability. Cox proportional hazard (CPH) models and Kaplan-Meier (KM) curves of overall survival (OS) prediction for each dataset were also generated. SD and CC for segmentations demonstrated high similarities, yielding, SD: 0.79 and CC: 0.92 (BY-SK), SD: 0.81 and CC: 0.83 (LS-SK), and SD: 0.84 and CC: 0.91 (MH-SK) in average for both databases, respectively. OS through the maximal CPH model for the two datasets yielded c-statistics of 0.7 (95% CI) and 0.69 (95% CI), while adding radiomic and clinical variables (sex, stage/morphological status, and histology) together. KM curves also showed significant discrimination between high- and low-risk patients (p-value < 0.005). This supports that readers' level of training and clinical experience may not significantly influence the ability to extract accurate radiomic features for NSCLC on CT. This potentially allows flexibility in the training required to produce robust prognostic imaging biomarkers for potential clinical translation.

Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs.

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant (p < 0.001) overlap difference for spleen (DiceIML/DiceManual = 0.91/0.87), breast tumors (DiceIML/DiceManual = 0.84/0.82), and lung nodules (DiceIML/DiceManual = 0.78/0.83). For intra-rater consistency, a significant (p = 0.003) difference was found for spleen (DiceIML/DiceManual = 0.91/0.89). For inter-rater consistency, significant (p < 0.045) differences were found for spleen (DiceIML/DiceManual = 0.91/0.87), breast (DiceIML/DiceManual = 0.86/0.81), lung (DiceIML/DiceManual = 0.85/0.89), the non-enhancing (DiceIML/DiceManual = 0.79/0.67) and the enhancing (DiceIML/DiceManual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin.