Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

Objective: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation.

Methods: Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr.

Lung cancer imaging methods in China from 2005 to 2014: A national, multicenter study.

Background: The study was conducted to examine changes in diagnostic and staging imaging methods for lung cancer in China over a 10-year period and to determine the relationships between such changes and socioeconomic development.

Methods: This was a hospital-based, nationwide, multicenter retrospective study of primary lung cancer cases. The data were extracted from the 10-year primary lung cancer databases at eight tertiary hospitals from various geographic areas in China.