Key Results 1. A model using baseline patient characteristics, laboratory markers, and chest radiography can predict short-term critical illness in hospitalized patients with COVID-19, with an internally validated AUC = 0.77. 2. At an example model risk threshold of 0.70, 71 of 356 patients would be predicted to develop critical illness of which 59 (83%) would be true-positives. 3. A risk calculator has been made available for download: Dutch COVID-19 risk model (https://docs.google.com/spreadsheets/d/1eFrdHxnOA-M_P-ijxnC2u30qk7IhMVV6YvHvJhrZ8Ws/edit#gid=0) (see Appendix E2).

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.

Purpose To study interreader variability for classifying pulmonary opacities at CT as perifissural nodules (PFNs) and determine how reliably radiologists differentiate PFNs from malignancies. Materials and Methods CT studies were obtained retrospectively from the National Lung Screening Trial (2002-2009). Nodules were eligible for the study if they were noncalcified, solid, within the size range of 5 to 10 mm, and scanned with a section thickness of 2 mm or less. Six radiologists classified 359 nodules in a cancer-enriched data set as PFN, non-PFN, or not applicable. Nodules classified as not applicable by at least three radiologists were excluded, leaving 316 nodules for post-hoc statistical analysis. Results The study group contained 22.2% cancers (70 of 316). The median proportion of nodules classified as PFNs was 45.6% (144 of 316). All six radiologists uniformly classified 17.7% (56 of 316) of the nodules as PFNs. The Fleiss κ was 0.50. Compared with non-PFNs, nodules classified as PFNs were smaller and more often located in the lower lobes and attached to a fissure (P < .001). Thirteen (18.6%) of 70 cancers were misclassified 21 times as PFNs. Individual readers' misclassification rates ranged from 0% (0 of 125) to 4.9% (eight of 163). Of 13 misclassified malignancies, 11 were in the upper lobes and two were attached to a fissure. Conclusion There was moderate interreader agreement when classifying nodules as perifissural nodules. Less than 2.5% of perifissural nodule classifications were misclassified lung cancers (21 of 865) in this cancer-enriched study. Allowing nodules classified as perifissural nodules to be omitted from additional follow-up in a screening setting could substantially reduce the number of unnecessary scans; excluding perifissural nodules in the upper lobes would greatly decrease the misclassification rate.

The polynomial model, using both patient characteristics and baseline scan morphology, was significantly superior in assigning participants to 1-year or 2-year screening intervals. Implementing personalised follow-up intervals would enable hundreds of participants to skip a screening round per lung cancer diagnosis delayed.

Lung cancer computed tomography (CT) screening trials using low-dose CT have repeatedly demonstrated a reduction in the number of lung cancer deaths in the screening group compared to a control group. With various countries currently considering the implementation of lung cancer screening, recurring discussion points are, among others, the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) has the potential to increase the efficiency of lung cancer screening. We discuss the performance levels of AI algorithms for various tasks related to the interpretation of lung screening CT scans, how they compare to human experts, and how AI and humans may complement each other. We discuss how AI may be used in the lung cancer CT screening workflow according to the current evidence and describe the additional research that will be required before AI can take a more prominent role in the analysis of lung screening CT scans.