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

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

BackgroundEstimating 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 est...

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Bibliographic Details
Main Authors: Ri-qiang Liao, An-wei Li, Hong-hong Yan, Jun-tao Lin, Si-yang Liu, Jing-wen Wang, Jian-sheng Fang, Hong-bo Liu, Yong-he Hou, Chao Song, Hui-fang Yang, Bin Li, Ben-yuan Jiang, Song Dong, Qiang Nie, Wen-zhao Zhong, Yi-long Wu, Xue-ning Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Oncology
Subjects:
sub solid pulmonary nodules
growth
mass
deep learning
radiomics
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.1002953/full

Internet

https://www.frontiersin.org/articles/10.3389/fonc.2022.1002953/full

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