Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation
Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation
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DOI：
10.1007/s00330-019-06344-z
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发表时间：
2020-02-01
影响因子：
5.9
通讯作者：
Wang, Jian-Wei
中科院分区：
文献类型：
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作者：
Qi, Lin-Lin;Wu, Bo-Tong;Wang, Jian-Wei
目的 通过深度学习辅助结节分割研究持续性肺部纯磨玻璃结节（pGGN）的自然史。方法 回顾性研究纳入2007年1月至2018年10月期间110例患者的110例pGGN，并进行573次随访CT扫描。使用基于卷积神经网络的 Dr. Wise 系统对初始和所有后续 CT 扫描进行 pGGN 自动分割。随后，自动计算pGGN直径、密度、体积、质量、体积倍增时间（VDT）和质量倍增时间（MDT）。登记的 pGGN 根据数量增长分为增长组 52 组（47.3%）和非增长组 58 组（52.7%）。采用对数秩检验和 Cox 比例风险回归分析进行 Kaplan-Meier 分析，以分析 pGGN 生长的累积百分比并确定生长的风险因素。结果 入组 pGGN 的平均随访时间为 48.7 +/- 23.8 个月。已生长的 52 个 pGGN 的中位 VDT 为 1448 天（范围为 339-8640）天，中位 MDT 为 1332 天（范围为 290-38,912）天。 pGGN 生长的 12 个月、24.7 个月和 60.8 个月累积百分比分别为 10%、25.5% 和 51.1%，并且它们在初始直径、体积和质量亚组之间存在显着差异（所有 p < 0.001）。 pGGNs的生长模式可能符合指数模型。分叶征 (p = 0.044)、初始平均直径 (p < 0.001)、体积 (p = 0.003) 和质量 (p = 0.023) 预测 pGGN 生长。结论 持续性 pGGN 表现出惰性病程。深度学习可以帮助准确阐明 pGGN 的自然历史。具有分叶征和较大初始直径、体积和质量的 pGGN 更有可能生长。
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.