A deep learning algorithm, developed for malignancy risk estimation of pulmonary nodules detected at screening CT, demonstrated good discriminative performance in a large independent validation set. 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...

Background The prognosis of hospitalized patients with severe coronavirus disease 2019 (COVID-19) is difficult to predict, while the capacity of intensive care units (ICUs) is a limiting factor dur...

Background All lung cancer CT screening trials used fixed follow-up intervals, which may not be optimal. We developed new lung cancer risk models for personalising screening intervals to 1 year or 2 years, and compared these with existing models. Methods We included participants in the CT arm of the National Lung Screening Trial (2002–2010) who underwent a baseline scan and a first annual follow-up scan and were not diagnosed with lung cancer in the first year. True and false positives and the area under the curve of each model were calculated. Internal validation was performed using bootstrapping. Results Data from 24 542 participants were included in the analysis. The accuracy was 0.785, 0.693, 0.697, 0.666 and 0.727 for the polynomial, patient characteristics, diameter, Patz and PanCan models, respectively. Of the 24 542 participants included, 174 (0.71%) were diagnosed with lung cancer between the first and the second annual follow-ups. Using the polynomial model, 2558 (10.4%, 95% CI 10.0% to 10.8%), 7544 (30.7%, 30.2% to 31.3%), 10 947 (44.6%, 44.0% to 45.2%), 16 710 (68.1%, 67.5% to 68.7%) and 20 023 (81.6%, 81.1% to 92.1%) of the 24 368 participants who did not develop lung cancer in the year following the first follow-up screening round could have safely skipped it, at the expense of delayed diagnosis of 0 (0.0%, 0.0% to 2.7%), 8 (4.6%, 2.2% to 9.2%), 17 (9.8%, 6.0% to 15.4%), 44 (25.3%, 19.2% to 32.5%) and 70 (40.2%, 33.0% to 47.9%) of the 174 lung cancers, respectively. Conclusions 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.

The interreader agreement for classifying solid opacities of 5 to 10 mm in diameter as perifissural nodules was moderate at best. Misclassifications of malignancies as perifissural nodules occurred mostly for nodules located in the upper lobe. PurposeTo study interreader variability for classifying pulmonary opacities at CT as perifissural nodules (PFNs) and determine how reliably radiologists differentiate PFNs from malignancies.Materials and MethodsCT 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.ResultsThe 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 m...

Nonuniform literature descriptions of what defines an intrapulmonary lymph node currently contribute to observer variability and risk of misclassification; the high prevalence of intrapulmonary lym... Several studies investigated the appearance of intrapulmonary lymph nodes (IPLNs) at CT with pathologic correlation. IPLNs are benign lesions and do not require follow-up after initial detection. T...

An observer study with seven radiologists showed that using a dedicated CT lung screening viewer (with computer-aided detection, a segmentation algorithm, and automatic Lung Imaging Reporting and D... Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung canc...