To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning–assisted nodule segmentation.
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.
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.
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.
• 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.
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Atypical adenomatous hyperplasia
Adenocarcinoma in situ
Computer-aided detection and diagnosis system
Convolution neural network
Mass doubling time
Minimally invasive adenocarcinoma
Pure ground-glass nodule
Volume doubling time
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We would like to thank Mr. Chang-Fa Xia (National Office for Cancer Prevention and Control, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) for his statistical assistance.
This study has received funding from the National Key R&D Program of China (2017YFC1308700), Chinese Academy of Medical Sciences Initiative for Innovative Medicine (2018-12M-AI-013), the National Natural Science Foundation of China (81171344), and Innovation Foundation for Doctoral Candidates of Peking Union Medical College (2018-1002-02-21).
The scientific guarantor of this publication is Jian-Wei Wang.
Conflict of interest
The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.
Statistics and biometry
Mr. Chang-Fa Xia (National Office for Cancer Prevention and Control, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China) kindly provided statistical advice for this manuscript.
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Qi, L., Wu, B., Tang, W. et al. Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning–assisted nodule segmentation. Eur Radiol 30, 744–755 (2020). https://doi.org/10.1007/s00330-019-06344-z
- Solitary pulmonary nodule
- Machine learning
- Neural networks (computer)
- Lung neoplasms
- Biological phenomena