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

Lin-Lin QiBo-Tong WuWei TangLi-Na ZhouYao HuangShi-Jun ZhaoLi LiuMeng LiLi ZhangShi-Chao FengDong-Hui HouZhen ZhouXiu-Li LiYi-Zhou WangNing WuJian-Wei Wang
Published in: European radiology (2019)
• 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.
Keyphrases
  • deep learning
  • neural network
  • convolutional neural network
  • pulmonary hypertension
  • machine learning
  • artificial intelligence