Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Impact of COVID-19 in paediatric early-phase cancer clinical trials in Europe: A report from the Innovative Therapies for Children with Cancer (ITCC) consortium.

INTRODUCTION: Data regarding real-world impact on cancer clinical research during COVID-19 are scarce. We analysed the impact of the COVID-19 pandemic on the conduct of paediatric cancer phase I-II trials in Europe through the experience of the Innovative Therapies for Children with Cancer (ITCC). METHODS: A survey was sent to all ITCC-accredited early-phase clinical trial hospitals including questions about impact on staff activities, recruitment, patient care, supply of investigational products and legal aspects, between 1st March and 30th April 2020. RESULTS: Thirty-one of 53 hospitals from 12 countries participated. Challenges reported included staff constraints (30% drop), reduction in planned monitoring activity (67% drop of site initiation visits and 64% of monitoring visits) and patient recruitment (61% drop compared with that in 2019). The percentage of phase I, phase II trials and molecular platforms closing to recruitment in at least one site was 48.5%, 61.3% and 64.3%, respectively. In addition, 26% of sites had restrictions on performing trial assessments because of local contingency plans. Almost half of the units suffered impact upon pending contracts. Most hospitals (65%) are planning on improving organisational and structural changes. CONCLUSION: The study reveals a profound disruption of paediatric cancer early-phase clinical research due to the COVID-19 pandemic across Europe. Reported difficulties affected both patient care and monitoring activity. Efforts should be made to reallocate resources to avoid lost opportunities for patients and to allow the continued advancement of oncology research. Identified adaptations to clinical trial procedures may be integrated to increase preparedness of clinical research to futures crises.