Lung Cancer Diagnosis

Background: Lung cancer risk classification is an increasingly important area of research as low-dose thoracic CT screening programs have become standard of care for patients at high risk for lung cancer. There is limited availability of large, annotated public databases for the training and testing of algorithms for lung nodule classification. Methods: Screening chest CT scans done between January 1, 2015 and June 30, 2021 at Duke University Health System were considered for this study. Efficient nodule annotation was performed semi-automatically by using a publicly available deep learning nodule detection algorithm trained on the LUNA16 dataset to identify initial candidates, which were then accepted based on nodule location in the radiology text report or manually annotated by a medical student and a fellowship-trained cardiothoracic radiologist. Results: The dataset contains 1613 CT volumes with 2487 annotated nodules. Radiologist spot-checking confirmed the semi-automated annotation had an accuracy rate of >90%. Conclusions: The Duke Lung Nodule Dataset is the first large dataset for CT screening for lung cancer reflecting the use of current CT technology. This represents a useful resource of lung cancer risk classification research, and the efficient annotation methods described for its creation may be used to generate similar databases for research in the future.

The National Lung Screening Trial (NLST) was a randomized controlled trial conducted by the Lung Screening Study group (LSS) and the American College of Radiology Imaging Network (ACRIN) to determine whether screening for lung cancer with low-dose helical computed tomography (CT) reduces mortality from lung cancer in high-risk individuals relative to screening with chest radiography. Approximately 54,000 participants were enrolled between August 2002 and April 2004. Data collection has ended, and information is complete through December 31, 2009. NLST has the ClinicalTrials.gov registration number NCT00047385.