Mapping the cancer imaging research landscape: which cancers are more and which cancers are less frequently investigated?

OBJECTIVE: To investigate the proportion of published imaging studies relative to incidence and mortality rate per cancer type. METHODS: From a random sample of 2500 articles published in 2019 by the top 25 imaging-related journals, we included cancer imaging studies. The publication-to-incidence and publication-to-mortality ratios (defined as the publication rate divided by the proportional incidence and mortality rate, respectively) were calculated per cancer type. Ratios >1 indicate a higher publication rate compared to the relative incidence or mortality rate of a specific cancer. Ratios <1 indicate a lower publication rate compared to the relative incidence or mortality rate of a specific cancer. RESULTS: 620 original cancer imaging studies were included. Female breast cancer (20.2%), prostate cancer (13.0%), liver cancer (12.9%), lung cancer (8.8%), and cancers in the central nervous system (8.1%) comprised the top 5 of cancers investigated. Cancers in the central nervous system and liver had publication-to-incidence ratios >2, whereas nonmelanoma of the skin, leukemia, stomach cancer, and laryngeal cancer had publication-to-incidence ratios <0.2. Cancers in the prostate, central nervous system, female breast, and kidney had publication-to-mortality ratios >2, whereas esophageal cancer, stomach cancer, laryngeal cancer, and leukemia had publication-to-mortality ratios <0.2. CONCLUSION: This overview of published cancer imaging research may be informative and useful to all stakeholders in the field of cancer imaging. The potential causes of disproportionality between the publication rate vs. incidence and mortality rates of some cancer types are multifactorial and need to be further elucidated.

A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.

Pulmonary embolism in patients with COVID-19 and value of D-dimer assessment: a meta-analysis.

PURPOSE: To investigate, in a meta-analysis, the frequency of pulmonary embolism (PE) in patients with COVID-19 and whether D-dimer assessment may be useful to select patients for computed tomography pulmonary angiography (CTPA). METHODS: A systematic literature search was performed for original studies which reported the frequency of PE on CTPA in patients with COVID-19. The frequency of PE, the location of PE, and the standardized mean difference (SMD) of D-dimer levels between patients with and without PE were pooled by random effects models. RESULTS: Seventy-one studies were included. Pooled frequencies of PE in patients with COVID-19 at the emergency department (ED), general wards, and intensive care unit (ICU) were 17.9% (95% CI: 12.0-23.8%), 23.9% (95% CI: 15.2-32.7%), and 48.6% (95% CI: 41.0-56.1%), respectively. PE was more commonly located in peripheral than in main pulmonary arteries (pooled frequency of 65.3% [95% CI: 60.0-70.1%] vs. 32.9% [95% CI: 26.7-39.0%]; OR = 3.540 [95% CI: 2.308-5.431%]). Patients with PE had significantly higher D-dimer levels (pooled SMD of 1.096 [95% CI, 0.844-1.349]). D-dimer cutoff levels which have been used to identify patients with PE varied between 1000 and 4800 µg/L. CONCLUSION: The frequency of PE in patients with COVID-19 is highest in the ICU, followed by general wards and the ED. PE in COVID-19 is more commonly located in peripheral than in central pulmonary arteries, which suggests local thrombosis to play a major role. D-dimer assessment may help to select patients with COVID-19 for CTPA, using D-dimer cutoff levels of at least 1000 µg/L. KEY POINTS: • The frequency of PE in patients with COVID-19 is highest in the ICU, followed by general wards and the ED. • PE in COVID-19 is more commonly located in peripheral than in central pulmonary arteries. • D-dimer levels are significantly higher in patients with COVID-19 who have PE.

Radiological Society of North America Chest CT Classification System for Reporting COVID-19 Pneumonia: Interobserver Variability and Correlation with Reverse-Transcription Polymerase Chain Reaction.

PURPOSE: To evaluate the Radiological Society of North America (RSNA) chest CT classification system for reporting coronavirus disease 2019 (COVID-19) pneumonia. MATERIALS AND METHODS: Chest CT scans of consecutive patients suspected of having COVID-19 were retrospectively and independently evaluated by two chest radiologists and a 5th-year radiology resident using the RSNA chest CT classification system for reporting COVID-19 pneumonia. Interobserver agreement was evaluated by calculating weighted κ coefficients. The proportion of patients with real-time reverse-transcription polymerase chain reaction (RT-PCR)-confirmed COVID-19 in each of the four chest CT categories (typical, indeterminate, atypical, and negative features for COVID-19) was calculated. RESULTS: In total, 96 patients (61 men; median age, 70 years [range, 29-94]) were included, of whom 45 had RT-PCR-confirmed COVID-19. The number of patients assigned to chest CT categories typical, indeterminate, atypical, and negative by the three readers ranged from 18 to 29, 26 to 43, 19 to 31, and 5 to 8, respectively. The κ coefficient among the chest radiologists was 0.663 (95% confidence interval [CI]: 0.565, 0.761). κ coefficients among the chest radiologists and the 5th-year radiology resident were 0.570 (95% CI: 0.443, 0.696) and 0.564 (95% CI: 0.451, 0.678), respectively. The proportion of patients with RT-PCR-confirmed COVID-19 in the chest CT categories typical, indeterminate, atypical, and negative for the three readers ranged from 76.9% to 96.6%, 51.2% to 64.1%, 2.8% to 5.3%, and 20% to 25%, respectively. CONCLUSION: The RSNA chest CT classification system for reporting COVID-19 pneumonia has moderate-to-substantial interobserver agreement. However, the proportion of RT-PCR-confirmed COVID-19 cases in the categories atypical appearance and negative for pneumonia is nonnegligible.Supplemental material is available for this article.© RSNA, 2020.

Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis.

PURPOSE: To determine the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the Radiological Society of North America (RSNA) categorizations in patients with clinically suspected coronavirus disease 2019 (COVID-19) infection. MATERIALS AND METHODS: In this meta-analysis, studies from 2020, up to August 24, 2020 were assessed for inclusion criteria of studies that used CO-RADS or the RSNA categories for scoring chest CT in patients with suspected COVID-19. A total of 186 studies were identified. After review of abstracts and text, a total of nine studies were included in this study. Patient information (n¸ age, sex), CO-RADS and RSNA scoring categories, and other study characteristics were extracted. Study quality was assessed with the QUADAS-2 tool. Meta-analysis was performed with a random effects model. RESULTS: Nine studies (3283 patients) were included. Overall study quality was good, except for risk of non-performance of repeated reverse transcriptase polymerase chain reaction (RT-PCR) after negative initial RT-PCR and persistent clinical suspicion in four studies. Pooled COVID-19 frequencies in CO-RADS categories were: 1, 8.8%; 2, 11.1%; 3, 24.6%; 4, 61.9%; and 5, 89.6%. Pooled COVID-19 frequencies in RSNA classification categories were: negative 14.4%; atypical, 5.7%; indeterminate, 44.9%; and typical, 92.5%. Pooled pairs of sensitivity and specificity using CO-RADS thresholds were the following: at least 3, 92.5% (95% CI: 87.1, 95.7) and 69.2% (95%: CI: 60.8, 76.4); at least 4, 85.8% (95% CI: 78.7, 90.9) and 84.6% (95% CI: 79.5, 88.5); and 5, 70.4% (95% CI: 60.2, 78.9) and 93.1% (95% CI: 87.7, 96.2). Pooled pairs of sensitivity and specificity using RSNA classification thresholds for indeterminate were 90.2% (95% CI: 87.5, 92.3) and 75.1% (95% CI: 68.9, 80.4) and for typical were 65.2% (95% CI: 37.0, 85.7) and 94.9% (95% CI: 86.4, 98.2). CONCLUSION: COVID-19 infection frequency was higher in patients categorized with higher CORADS and RSNA classification categories.

Chest CT in COVID-19: What the Radiologist Needs to Know.

Chest CT has a potential role in the diagnosis, detection of complications, and prognostication of coronavirus disease 2019 (COVID-19). Implementation of appropriate precautionary safety measures, chest CT protocol optimization, and a standardized reporting system based on the pulmonary findings in this disease will enhance the clinical utility of chest CT. However, chest CT examinations may lead to both false-negative and false-positive results. Furthermore, the added value of chest CT in diagnostic decision making is dependent on several dynamic variables, most notably available resources (real-time reverse transcription-polymerase chain reaction [RT-PCR] tests, personal protective equipment, CT scanners, hospital and radiology personnel availability, and isolation room capacity) and the prevalence of both COVID-19 and other diseases with overlapping manifestations at chest CT. Chest CT is valuable to detect both alternative diagnoses and complications of COVID-19 (acute respiratory distress syndrome, pulmonary embolism, and heart failure), while its role for prognostication requires further investigation. The authors describe imaging and managing care of patients with COVID-19, with topics including (a) chest CT protocol, (b) chest CT findings of COVID-19 and its complications, (c) the diagnostic accuracy of chest CT and its role in diagnostic decision making and prognostication, and (d) reporting and communicating chest CT findings. The authors also review other specific topics, including the pathophysiology and clinical manifestations of COVID-19, the World Health Organization case definition, the value of performing RT-PCR tests, and the radiology department and personnel impact related to performing chest CT in COVID-19. ©RSNA, 2020.

Chest CT Imaging Signature of Coronavirus Disease 2019 Infection: In Pursuit of the Scientific Evidence.

BACKGROUND: Chest CT may be used for the diagnosis of coronavirus disease 2019 (COVID-19), but clear scientific evidence is lacking. Therefore, we systematically reviewed and meta-analyzed the chest CT imaging signature of COVID-19. RESEARCH QUESTION: What is the chest CT imaging signature of COVID-19 infection? STUDY DESIGN AND METHODS: A systematic literature search was performed for original studies on chest CT imaging findings in patients with COVID-19. Methodologic quality of studies was evaluated. Pooled prevalence of chest CT imaging findings were calculated with the use of a random effects model in case of between-study heterogeneity (predefined as I2 ≥50); otherwise, a fixed effects model was used. RESULTS: Twenty-eight studies were included. The median number of patients with COVID-19 per study was 124 (range, 50-476), comprising a total of 3,466 patients. Median prevalence of symptomatic patients was 99% (range, >76.3%-100%). Twenty-seven of the studies (96%) had a retrospective design. Methodologic quality concerns were present with either risk of or actual referral bias (13 studies), patient spectrum bias (eight studies), disease progression bias (26 studies), observer variability bias (27 studies), and test review bias (14 studies). Pooled prevalence was 10.6% for normal chest CT imaging findings. Pooled prevalences were 90.0% for posterior predilection, 81.0% for ground-glass opacity, 75.8% for bilateral abnormalities, 73.1% for left lower lobe involvement, 72.9% for vascular thickening, and 72.2% for right lower lobe involvement. Pooled prevalences were 5.2% for pleural effusion, 5.1% for lymphadenopathy, 4.1% for airway secretions/tree-in-bud sign, 3.6% for central lesion distribution, 2.7% for pericardial effusion, and 0.7% for cavitation/cystic changes. Pooled prevalences of other CT imaging findings ranged between 10.5% and 63.2%. INTERPRETATION: Studies on chest CT imaging findings in COVID-19 suffer from methodologic quality concerns. More high-quality research is necessary to establish diagnostic CT criteria for COVID-19. Based on the available evidence that requires cautious interpretation, several chest CT imaging findings appear to be suggestive of COVID-19, but normal chest CT imaging findings do not exclude COVID-19, not even in symptomatic patients.

Diagnostic performance of chest CT in screening patients with suspected COVID-19 infection in a Western population.

OBJECTIVE: To investigate the diagnostic performance of chest CT in screening patients suspected of Coronavirus disease 2019 (COVID-19) in a Western population. METHODS: Consecutive patients who underwent chest CT because of clinical suspicion of COVID-19 were included. CT scans were prospectively evaluated by frontline general radiologists who were on duty at the time when the CT scan was performed and retrospectively assessed by a chest radiologist in an independent and blinded manner. Real-time reverse transcriptase-polymerase chain reaction was used as reference standard. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Sensitivity and specificity of the frontline general radiologists were compared to those of the chest radiologist using the McNemar test. RESULTS: 56 patients were included. Sensitivity, specificity, PPV, and NPV for the frontline general radiologists were 89.3% [95% confidence interval (CI): 71.8%, 97.7%], 32.1% (95% CI: 15.9%, 52.4%), 56.8% (95% CI: 41.0%, 71.7%), and 75.0% (95% CI: 42.8%, 94.5%), respectively. Sensitivity, specificity, PPV, and NPV for the chest radiologist were 89.3% (95% CI: 71.8%, 97.7%), 75.0% (95% CI: 55.1%, 89.3%), 78.1% (95% CI: 60.0%, 90.7%), and 87.5% (95% CI: 67.6%, 97.3%), respectively. Sensitivity was not significantly different (p = 1.000), but specificity was significantly higher for the chest radiologist (p = 0.001). CONCLUSION: Chest CT interpreted by frontline general radiologists achieves insufficient screening performance. Although specificity of a chest radiologist appears to be significantly higher, sensitivity did not improve. A negative chest CT result does not exclude COVID-19. ADVANCES IN KNOWLEDGE: Our study shows that chest CT interpreted by frontline general radiologists achieves insufficient diagnostic performance to use it as an independent screening tool for COVID-19. Although specificity of a chest radiologist appears to be significantly higher, sensitivity is still insufficiently high.

Systematic Review and Meta-Analysis on the Value of Chest CT in the Diagnosis of Coronavirus Disease (COVID-19): Sol Scientiae, Illustra Nos.

OBJECTIVE. The purpose of this article is to systematically review and meta-analyze the diagnostic accuracy of chest CT in detecting coronavirus disease (COVID-19). MATERIALS AND METHODS. MEDLINE was systematically searched for publications on the diagnostic performance of chest CT in detecting COVID-19. Methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Meta-analysis was performed using a bivariate random-effects model. RESULTS. Six studies were included, comprising 1431 patients. All six studies included patients at high risk of COVID-19, and five studies explicitly reported that they included only symptomatic patients. Mean prevalence of COVID-19 was 47.9% (range, 27.6-85.4%). High or potential risk of bias was present throughout all QUADAS-2 domains in all six studies. Sensitivity ranged from 92.9% to 97.0%, and specificity ranged from 25.0% to 71.9%, with pooled estimates of 94.6% (95% CI, 91.9-96.4%) and 46.0% (95% CI, 31.9-60.7%), respectively. The included studies were statistically homogeneous in their estimates of sensitivity (p = 0.578) and statistically heterogeneous in their estimates of specificity (p < 0.001). CONCLUSION. Diagnostic accuracy studies on chest CT in COVID-19 suffer from methodologic quality issues. Chest CT appears to have a relatively high sensitivity in symptomatic patients at high risk of COVID-19, but it cannot exclude COVID-19. Specificity is poor. These data, along with other local factors such as COVID-19 prevalence, available real-time reverse transcriptase-polymerase chain reaction tests, staff, hospital, and CT scanning capacity, can be useful to healthcare professionals and policy makers to decide on the utility of chest CT for COVID-19 detection in the hospital setting.