TY - JOUR
T1 - Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation
AU - Qi, Lin Lin
AU - Wu, Bo Tong
AU - Tang, Wei
AU - Zhou, Li Na
AU - Huang, Yao
AU - Zhao, Shi Jun
AU - Liu, Li
AU - Li, Meng
AU - Zhang, Li
AU - Feng, Shi Chao
AU - Hou, Dong Hui
AU - Zhou, Zhen
AU - Li, Xiu Li
AU - Wang, Yi Zhou
AU - Wu, Ning
AU - Wang, Jian Wei
N1 - Publisher Copyright:
© 2019, European Society of Radiology.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. Results: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339–8640) days, and their median MDT was 1332 (range, 290–38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. Conclusions: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key Points: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339–8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290–38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116–2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.
AB - 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. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. Results: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339–8640) days, and their median MDT was 1332 (range, 290–38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. Conclusions: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. Key Points: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339–8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290–38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116–2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.
KW - Biological phenomena
KW - Lung neoplasms
KW - Machine learning
KW - Neural networks (computer)
KW - Solitary pulmonary nodule
UR - http://www.scopus.com/inward/record.url?scp=85071745902&partnerID=8YFLogxK
U2 - 10.1007/s00330-019-06344-z
DO - 10.1007/s00330-019-06344-z
M3 - Article
C2 - 31485837
AN - SCOPUS:85071745902
SN - 0938-7994
VL - 30
SP - 744
EP - 755
JO - European Radiology
JF - European Radiology
IS - 2
ER -