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

• Published in: European radiology Vol. 30; no. 2; pp. 744 - 755
• Main Authors: Qi, Lin-Lin, Wu, Bo-Tong, Tang, Wei, Zhou, Li-Na, Huang, Yao, Zhao, Shi-Jun, Liu, Li, Li, Meng, Zhang, Li, Feng, Shi-Chao, Hou, Dong-Hui, Zhou, Zhen, Li, Xiu-Li, Wang, Yi-Zhou, Wu, Ning, Wang, Jian-Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-02-2020; Springer Nature B.V
• Subjects: Artificial neural networks; Chest; Computed tomography; Convolution; Deep learning; Diagnostic Radiology; Glass; Growth patterns; Hazards; Imaging; Internal Medicine; Interventional Radiology; Lung cancer; Lung nodules; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neural networks; Neuroradiology; Nodules; Radiology; Rank tests; Regression analysis; Risk analysis; Risk factors; Segmentation; Subgroups; Ultrasound; Neural networks (computer); Solitary pulmonary nodule; Machine learning; Biological phenomena; Lung neoplasms
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-019-06344-z

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.