Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images.

Background: Estimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth and (2) seek to establish a deep learning-based model to predict the growth of SSNs.

Methods: A total of 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules.