External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Background: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines.

Sequential screening for lung cancer in a high-risk group: randomised controlled trial: LungSEARCH: a randomised controlled trial of Surveillance using sputum and imaging for the EARly detection of lung Cancer in a High-risk group.

Background: Low-dose computed tomography (LDCT) screening detects early-stage lung cancer and reduces mortality. We proposed a sequential approach targeted to a high-risk group as a potentially efficient screening strategy.

Methods: LungSEARCH was a national multicentre randomised trial.

Lung cancer stage-shift following a symptom awareness campaign.

Background: Lung cancer outcomes in the UK are worse than in many other developed nations. Symptom awareness campaigns aim to diagnose patients at an earlier stage to improve cancer outcomes.

Methods: An early diagnosis campaign for lung cancer commenced in Leeds, UK in 2011 comprising public and primary-care facing components.