CORADS-AI is a freely accessible deep learning algorithm that automatically assigns CO-RADS and CT severity scores to non-contrast CT scans of patients suspected of COVID-19 with high diagnostic performance.

Dutch-Belgian Lung Cancer Screening trial showed that screening high-risk individuals with low-dose chest CT reduced lung cancer mortality by 20% and 26%, respectively (1,2). This is linked to a beneficial stage shift, with stage I and II lung cancer having a much better prognosis than stage III or IV lung cancer (3). Lung cancer typically manifests as pulmonary nodules at CT. However, most nodules are benign and do not require further clinical workup. Nodule management guidelines and data-driven models have been developed to reduce the rate of false-positive findings and avoid overtreatment (4-8), but it remains a challenge to accurately distinguish between benign and malignant nodules (9).Deep learning (DL) with convolutional neural networks (CNNs) has recently become a method of choice for analyzing medical images (10). Several studies (11-13) showcased the potential of CNNs in predicting the malignancy risk of a pulmonary nodule by using the publicly available Lung Image Database Consortium image collection data set (14). However, these studies used the subjective labels provided by radiologists and lacked a solid reference standard set by histopathologic examination for malignant nodules and at least 2 years of imaging follow-up for benign nodules. Ardila et al (15) developed a DL algorithm that processes a whole CT image to predict patient-level malignancy risk. However, without risk scores for individual nodules, these algorithms are difficult to integrate as a second opinion in conjunction with current clinical guidelines like the Lung CT Screening Reporting and Data System (Lung-RADS) by the American College of Radiology (4,16). Another study evaluated a DL algorithm on two clinical data sets with a proven reference standard, but Background: Accurate estimation of the malignancy risk of pulmonary nodules at chest CT is crucial for optimizing management in lung cancer screening.Purpose: To develop and validate a deep learning (DL) algorithm for malignancy risk estimation of pulmonary nodules detected at screening CT. Materials and Methods:In this retrospective study, the DL algorithm was developed with 16 077 nodules (1249 malignant) collected between 2002 and 2004 from the National Lung Screening Trial. External validation was performed in the following three cohorts collected between 2004 and 2010 from the Danish Lung Cancer Screening Trial: a full cohort containing all 883 nodules (65 malignant) and two cancer-enriched cohorts with size matching (175 nodules, 59 malignant) and without size matching (177 nodules, 59 malignant) of benign nodules selected at random. Algorithm performance was measured by using the area under the receiver operating characteristic curve (AUC) and compared with that of the Pan-Canadian Early Detection of Lung Cancer (PanCan) model in the full cohort and a group of 11 clinicians composed of four thoracic radiologists, five radiology residents, and two pulmonologists in the cancer-enriched cohorts.

Amidst the ongoing pandemic, the assessment of computed tomography (CT) images for COVID-19 presence can exceed the workload capacity of radiologists. Several studies addressed this issue by automating COVID-19 classification and grading from CT images with convolutional neural networks (CNNs). Many of these studies reported initial results of algorithms that were assembled from commonly used components. However, the choice of the components of these algorithms was often pragmatic rather than systematic and systems were not compared to each other across papers in a fair manner. We systematically investigated the effectiveness of using 3D CNNs instead of 2D CNNs for seven commonly used architectures, including DenseNet, Inception, and ResNet variants. For the architecture that performed best, we furthermore investigated the effect of initializing the network with pre-trained weights, providing automatically computed lesion maps as additional network input, and predicting a continuous instead of a categorical output. A 3D DenseNet-201 with these components achieved an area under the receiver operating characteristic curve (AUC) of 0.930 on our test set of 105 CT scans and an AUC of 0.919 on a publicly available set of 742 CT scans, a substantial improvement in comparison with a previously published 2D CNN. This paper provides insights into the performance benefits of various components for COVID-19 classification and grading systems. We have created a challenge on grand-challenge.org to allow for a fair comparison between the results of this and future research.Impact Statement-Applied artificial intelligence (AI) research focuses disproportionately on novel architecture modifications that do not necessarily generalize to other datasets, while neglecting systematic comparisons between commonly used algorithm components. This inhibits the deployment of AI for real-world applications. For automatic COVID-19 grading specifically, attention for compatibility of AI with clinical workflow is lacking. This paper presents a systematic investigation of COVID-19 grading algorithm components using a large publicly available dataset. The results are published in an online challenge. These contributions speed up the development of AI applications for COVID-19 grading by establishing insights into the components of such applications and by allowing applications produced by future research to be compared in a fair manner. The adherence

L ung cancer is the deadliest cancer worldwide. Early detection through screening is critical to reducing lung cancer mortality (1,2). The National Lung Screening Trial (NLST) and the Dutch-Belgian Lung Cancer Screening Trial (NELSON) showed that screening a high-risk population with low-dose chest CT examinations reduces lung cancer mortality by up to 26% (3,4). Early-stage lung cancer often manifests as small pulmonary nodules. CT examinations are highly effective at depicting these nodules. However, most pulmonary nodules are benign. This is demonstrated by the false-positive rate of 24% in the NLST (3). Thus, it is challenging for radiologists to identify and monitor potentially malignant nodules. Despite the presence of nodule management guidelines (5,6), accurate characterization remains tedious and is subject to interand intrareader variability (7).Artificial intelligence using deep learning (DL) has demonstrated promising results for accurately estimating the malignancy risk of pulmonary nodules, especially compared with histopathologic analysis-based reference standards. For example, previous studies (8-11) describe DL algorithms that outperform established nodule risk calculators while performing similarly to expert thoracic radiologists. However, these algorithms do not include imaging information from prior CT examinations when available. For nodules observed at the follow-up screening rounds, temporal changes, such as growth and appearance compared with prior CT examinations, provide valuable additional information for Background: Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk.Purpose: To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods:In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results:The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT